Planner2.ipynb

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     "text": [
      "Enter a skill you want to study (Java Development, Data Analyst, or Project Management): web development tools\n",
      "Enter your skill level (Beginner, Amateur, or Expert): Amateur\n",
      "Study plan for Amateur web development tools:\n",
      "1. Deepen understanding of CSS layout techniques (Flexbox, Grid)\n",
      "2. Learn about responsive web design principles\n",
      "3. Explore JavaScript frameworks/libraries like React or Vue.js\n",
      "4. Work on more complex projects (e.g., interactive web applications)\n",
      "5. Contribute to open-source web development projects on GitHub\n"
     ]
    }
   ],
   "source": [
    "# Importing necessary libraries\n",
    "import random\n",
    "\n",
    "# Function to generate a study plan based on the user's skill and level\n",
    "def generate_study_plan(skill, level):\n",
    "    # Dictionary of study plans for different skills and levels\n",
    "    study_plans = {\n",
    "        \"java development\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basic Java syntax and concepts\",\n",
    "                \"2. Practice basic programming exercises in Java\",\n",
    "                \"3. Take an introductory Java course on Coursera or Udemy\",\n",
    "                \"4. Explore Codecademy's Java track\",\n",
    "                \"5. Solve problems on HackerRank's Java section\",\n",
    "                \"6. Build simple Java projects like a calculator or todo list\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of Java object-oriented programming concepts\",\n",
    "                \"2. Take an intermediate Java course on Pluralsight\",\n",
    "                \"3. Work on more complex coding challenges on LeetCode\",\n",
    "                \"4. Read 'Effective Java' by Joshua Bloch\",\n",
    "                \"5. Contribute to open-source Java projects on GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced Java topics such as multithreading and concurrency\",\n",
    "                \"2. Study design patterns and best practices in Java development\",\n",
    "                \"3. Dive into Java frameworks like Spring or Hibernate\",\n",
    "                \"4. Explore advanced Java courses on platforms like Udacity\",\n",
    "                \"5. Mentor others in Java development through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"data analyst\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of data analysis and statistics\",\n",
    "                \"2. Take an introductory data analysis course on edX or Coursera\",\n",
    "                \"3. Practice basic data manipulation with Python and Pandas\",\n",
    "                \"4. Explore data visualization libraries like Matplotlib and Seaborn\",\n",
    "                \"5. Solve beginner-level data analysis problems on Kaggle\",\n",
    "                \"6. Work on simple data analysis projects using Jupyter Notebooks\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of statistical analysis and hypothesis testing\",\n",
    "                \"2. Take an intermediate data science course on DataCamp\",\n",
    "                \"3. Learn about machine learning algorithms and techniques\",\n",
    "                \"4. Participate in data analysis competitions on Kaggle\",\n",
    "                \"5. Read 'Python for Data Analysis' by Wes McKinney\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced data manipulation and cleaning techniques\",\n",
    "                \"2. Dive deep into machine learning and predictive modeling\",\n",
    "                \"3. Explore big data technologies like Apache Spark\",\n",
    "                \"4. Contribute to data science projects on GitHub\",\n",
    "                \"5. Stay updated with the latest research and trends in data analysis\"\n",
    "            ]\n",
    "        },\n",
    "        \"data visualization\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basic data visualization principles and techniques\",\n",
    "                \"2. Take an introductory data visualization course on Coursera or edX\",\n",
    "                \"3. Practice creating simple plots using Matplotlib and Seaborn in Python\",\n",
    "                \"4. Explore basic chart types and when to use them\",\n",
    "                \"5. Complete beginner-level data visualization projects on Kaggle\",\n",
    "                \"6. Read 'Fundamentals of Data Visualization' by Claus O. Wilke\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of data visualization best practices\",\n",
    "                \"2. Take an intermediate data visualization course on DataCamp\",\n",
    "                \"3. Learn advanced plotting techniques and customizations\",\n",
    "                \"4. Explore interactive visualization libraries like Plotly and Bokeh\",\n",
    "                \"5. Work on more complex data visualization projects incorporating storytelling\",\n",
    "                \"6. Contribute to data visualization communities on platforms like Tableau Public\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced data visualization concepts such as perception and cognition\",\n",
    "                \"2. Dive deep into specialized visualization techniques for specific data types\",\n",
    "                \"3. Explore advanced data visualization courses on platforms like Udacity\",\n",
    "                \"4. Conduct research on cutting-edge data visualization methods\",\n",
    "                \"5. Mentor others in data visualization through workshops or online forums\"\n",
    "            ]\n",
    "        },\n",
    "        \"sql\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basic SQL syntax (SELECT, INSERT, UPDATE, DELETE)\",\n",
    "                \"2. Practice simple SQL queries on online platforms like Codecademy or W3Schools\",\n",
    "                \"3. Take an introductory SQL course on Coursera, Udemy, or Khan Academy\",\n",
    "                \"4. Solve beginner-level SQL problems on LeetCode or HackerRank\",\n",
    "                \"5. Work on simple SQL projects like creating a database for a small business\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of SQL joins, subqueries, and aggregate functions\",\n",
    "                \"2. Take an intermediate SQL course on Pluralsight or edX\",\n",
    "                \"3. Practice complex SQL queries on platforms like LeetCode or HackerRank\",\n",
    "                \"4. Read 'SQL Performance Explained' by Markus Winand\",\n",
    "                \"5. Work on SQL projects that involve multiple tables and data manipulation\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced SQL topics such as stored procedures, triggers, and transactions\",\n",
    "                \"2. Study database optimization techniques and query tuning strategies\",\n",
    "                \"3. Explore advanced SQL courses on platforms like Udacity or DataCamp\",\n",
    "                \"4. Contribute to open-source SQL projects or database management systems\",\n",
    "                \"5. Mentor others in SQL development through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"javascript development\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basic JavaScript syntax and concepts\",\n",
    "                \"2. Practice basic programming exercises in JavaScript\",\n",
    "                \"3. Take an introductory JavaScript course on Coursera or Udemy\",\n",
    "                \"4. Explore freeCodeCamp's JavaScript track\",\n",
    "                \"5. Solve problems on LeetCode's JavaScript section\",\n",
    "                \"6. Build simple JavaScript projects like a to-do list or a calculator\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of JavaScript functions and closures\",\n",
    "                \"2. Take an intermediate JavaScript course on Pluralsight or Codecademy\",\n",
    "                \"3. Work on more complex coding challenges on HackerRank\",\n",
    "                \"4. Read 'Eloquent JavaScript' by Marijn Haverbeke\",\n",
    "                \"5. Contribute to open-source JavaScript projects on GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced JavaScript topics such as asynchronous programming and closures\",\n",
    "                \"2. Study modern JavaScript frameworks like React, Angular, or Vue.js\",\n",
    "                \"3. Dive into Node.js for server-side JavaScript development\",\n",
    "                \"4. Explore advanced JavaScript courses on platforms like Udacity or LinkedIn Learning\",\n",
    "                \"5. Mentor others in JavaScript development through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"project management\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basic concepts of project management like scope, schedule, and budget\",\n",
    "                \"2. Take an introductory project management course on Coursera or edX\",\n",
    "                \"3. Practice creating simple project plans using tools like Trello or Asana\",\n",
    "                \"4. Explore project management methodologies like Agile or Waterfall\",\n",
    "                \"5. Solve beginner-level project management problems on websites like ProjectManager.com\",\n",
    "                \"6. Participate in online forums or communities related to project management\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of project management frameworks and methodologies\",\n",
    "                \"2. Take an intermediate project management course focusing on specific methodologies\",\n",
    "                \"3. Work on more complex project management case studies and simulations\",\n",
    "                \"4. Read 'A Guide to the Project Management Body of Knowledge (PMBOK Guide)'\",\n",
    "                \"5. Contribute to open-source project management tools or projects on GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced project management topics such as risk management and stakeholder engagement\",\n",
    "                \"2. Study advanced project management certifications like PMP (Project Management Professional)\",\n",
    "                \"3. Dive into project management software tools like Microsoft Project or Primavera P6\",\n",
    "                \"4. Explore advanced project management courses on platforms like Udemy or LinkedIn Learning\",\n",
    "                \"5. Mentor others in project management through workshops or online platforms\"\n",
    "            ]\n",
    "        },\n",
    "        \"communication skills\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Start with basic communication concepts like verbal and non-verbal communication\",\n",
    "                \"2. Practice active listening skills by engaging in conversations with friends or family\",\n",
    "                \"3. Take an introductory communication skills course on platforms like Coursera or edX\",\n",
    "                \"4. Explore Toastmasters International for public speaking practice\",\n",
    "                \"5. Read books like 'How to Win Friends and Influence People' by Dale Carnegie\",\n",
    "                \"6. Watch TED Talks and analyze the speakers' communication techniques\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of advanced communication theories and models\",\n",
    "                \"2. Take an intermediate communication skills course focusing on professional communication\",\n",
    "                \"3. Join local speaking clubs or debate teams to enhance speaking and debating skills\",\n",
    "                \"4. Practice giving presentations and receiving constructive feedback\",\n",
    "                \"5. Read 'Crucial Conversations' by Kerry Patterson for mastering difficult conversations\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced communication strategies for leadership and negotiation\",\n",
    "                \"2. Study advanced rhetoric and persuasion techniques\",\n",
    "                \"3. Mentor others in communication skills through workshops or coaching sessions\",\n",
    "                \"4. Attend communication conferences and seminars to stay updated with the latest trends\",\n",
    "                \"5. Conduct research and publish articles in the field of communication\"\n",
    "            ]\n",
    "        },\n",
    "        \"software engineering\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of programming concepts (variables, loops, conditionals)\",\n",
    "                \"2. Take an introductory course in Python or C++ on Coursera or Udemy\",\n",
    "                \"3. Practice coding exercises on websites like LeetCode or Codecademy\",\n",
    "                \"4. Build simple projects like a calculator or a small game\",\n",
    "                \"5. Read 'Clean Code' by Robert C. Martin for best practices\",\n",
    "                \"6. Participate in online coding communities for support and learning\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of data structures and algorithms\",\n",
    "                \"2. Take intermediate courses on algorithms and data structures\",\n",
    "                \"3. Solve medium to hard level coding challenges on platforms like HackerRank\",\n",
    "                \"4. Learn version control systems like Git and work on collaborative projects\",\n",
    "                \"5. Read 'Design Patterns: Elements of Reusable Object-Oriented Software' by Gang of Four\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced algorithms and data structures\",\n",
    "                \"2. Study software architecture principles and patterns\",\n",
    "                \"3. Learn about software development methodologies like Agile and Scrum\",\n",
    "                \"4. Contribute to open-source projects on GitHub\",\n",
    "                \"5. Stay updated with the latest trends and advancements in software engineering\"\n",
    "            ]\n",
    "        },\n",
    "        \"data engineering\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn fundamentals of databases and data modeling\",\n",
    "                \"2. Take an introductory course on SQL on Codecademy\",\n",
    "                \"3. Study basic data manipulation with Python and Pandas\",\n",
    "                \"4. Get familiar with ETL (Extract, Transform, Load) concepts\",\n",
    "                \"5. Practice SQL queries on LeetCode or HackerRank\",\n",
    "                \"6. Build simple ETL pipelines with Python\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of distributed computing and Hadoop ecosystem\",\n",
    "                \"2. Take an intermediate course on Apache Spark on Udemy\",\n",
    "                \"3. Learn about data warehousing and dimensional modeling\",\n",
    "                \"4. Work on more complex ETL projects integrating multiple data sources\",\n",
    "                \"5. Explore advanced SQL topics like window functions and CTEs\",\n",
    "                \"6. Contribute to open-source data engineering projects on GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced concepts in stream processing and real-time data pipelines\",\n",
    "                \"2. Dive deep into distributed systems architecture\",\n",
    "                \"3. Explore cloud-based data engineering solutions like AWS Glue or Google Dataflow\",\n",
    "                \"4. Study advanced topics in Apache Spark optimization and tuning\",\n",
    "                \"5. Mentor others in data engineering through online forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"statistical analysis\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of statistics: mean, median, mode, and standard deviation\",\n",
    "                \"2. Take an introductory statistics course on Khan Academy or Coursera\",\n",
    "                \"3. Practice basic statistical analysis using Python and libraries like NumPy and Pandas\",\n",
    "                \"4. Explore data visualization with Matplotlib and Seaborn\",\n",
    "                \"5. Solve beginner-level statistics problems on LeetCode or HackerRank\",\n",
    "                \"6. Work on simple statistical analysis projects using Jupyter Notebooks\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of probability theory and hypothesis testing\",\n",
    "                \"2. Take an intermediate statistics course on edX or DataCamp\",\n",
    "                \"3. Learn about regression analysis and its applications\",\n",
    "                \"4. Participate in statistical analysis competitions on Kaggle\",\n",
    "                \"5. Read 'Introduction to Statistical Learning' by Gareth James et al.\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced statistical modeling techniques such as time series analysis and Bayesian inference\",\n",
    "                \"2. Dive deep into machine learning algorithms for statistical analysis\",\n",
    "                \"3. Explore advanced topics in experimental design and multivariate analysis\",\n",
    "                \"4. Contribute to statistical analysis projects on GitHub\",\n",
    "                \"5. Stay updated with the latest research and trends in statistical analysis\"\n",
    "            ]\n",
    "        },\n",
    "        \"database management\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basic concepts of databases (e.g., relational vs. non-relational)\",\n",
    "                \"2. Understand fundamental SQL queries (e.g., SELECT, INSERT, UPDATE, DELETE)\",\n",
    "                \"3. Take an introductory course on databases on Coursera or Udemy\",\n",
    "                \"4. Practice SQL queries on platforms like LeetCode or HackerRank\",\n",
    "                \"5. Create simple databases and tables using MySQL or SQLite\",\n",
    "                \"6. Learn about database normalization and basic ER modeling\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of advanced SQL concepts (e.g., JOINs, subqueries)\",\n",
    "                \"2. Study database design principles and normalization techniques\",\n",
    "                \"3. Work on more complex SQL problems and scenarios\",\n",
    "                \"4. Take an intermediate course on database management systems\",\n",
    "                \"5. Implement database transactions and concurrency control\",\n",
    "                \"6. Practice database optimization techniques\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced database concepts like indexing and query optimization\",\n",
    "                \"2. Explore NoSQL databases and their use cases\",\n",
    "                \"3. Dive into database administration and performance tuning\",\n",
    "                \"4. Study advanced topics like data warehousing and OLAP\",\n",
    "                \"5. Contribute to open-source database projects on GitHub\",\n",
    "                \"6. Stay updated with emerging trends in database technologies\"\n",
    "            ]\n",
    "        },\n",
    "        \"ui/ux design\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn the fundamentals of UI/UX design principles\",\n",
    "                \"2. Take an introductory course on UI/UX design on Coursera or Udemy\",\n",
    "                \"3. Practice creating wireframes and mockups using tools like Adobe XD or Figma\",\n",
    "                \"4. Explore beginner-level UI/UX design projects on Behance or Dribbble\",\n",
    "                \"5. Read 'Don't Make Me Think' by Steve Krug\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of user research and usability testing methods\",\n",
    "                \"2. Take an intermediate UI/UX design course on LinkedIn Learning\",\n",
    "                \"3. Work on more complex design challenges on platforms like DesignCrowd\",\n",
    "                \"4. Study UI/UX design case studies from renowned designers and agencies\",\n",
    "                \"5. Learn prototyping and interaction design with tools like InVision or Proto.io\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced UI/UX design techniques such as microinteractions and motion design\",\n",
    "                \"2. Study user psychology and behavior to optimize user experiences\",\n",
    "                \"3. Dive into advanced prototyping and animation techniques\",\n",
    "                \"4. Explore UI/UX design trends and emerging technologies\",\n",
    "                \"5. Mentor others in UI/UX design through workshops or online communities\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"cloud computing\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the basics of cloud computing (what it is, benefits, etc.)\",\n",
    "                \"2. Learn about popular cloud service providers like AWS, Azure, and Google Cloud Platform\",\n",
    "                \"3. Take an introductory course on cloud computing fundamentals\",\n",
    "                \"4. Practice basic cloud computing concepts with hands-on labs on platforms like Coursera or Udemy\",\n",
    "                \"5. Explore beginner-level cloud computing problems on LeetCode\",\n",
    "                \"6. Build simple cloud-based projects like a static website on AWS S3 or a virtual machine on Azure\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of cloud architecture and services\",\n",
    "                \"2. Take an intermediate course on a specific cloud platform (AWS, Azure, etc.)\",\n",
    "                \"3. Work on more complex cloud computing challenges on HackerRank\",\n",
    "                \"4. Read 'Architecting the Cloud' by Michael J. Kavis\",\n",
    "                \"5. Contribute to open-source cloud projects on GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced cloud computing topics such as serverless computing and containerization\",\n",
    "                \"2. Study advanced cloud services like AWS Lambda, Azure Functions, and Google Kubernetes Engine\",\n",
    "                \"3. Dive into cloud security and compliance best practices\",\n",
    "                \"4. Explore advanced cloud courses on platforms like Udacity or Linux Academy\",\n",
    "                \"5. Mentor others in cloud computing through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"data mining\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of data mining concepts and techniques\",\n",
    "                \"2. Take an introductory data mining course on Coursera or edX\",\n",
    "                \"3. Practice basic data mining algorithms like decision trees and clustering\",\n",
    "                \"4. Explore data mining tools like Weka or RapidMiner\",\n",
    "                \"5. Solve beginner-level data mining problems on Kaggle or HackerRank\",\n",
    "                \"6. Work on simple data mining projects using Jupyter Notebooks\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of advanced data mining algorithms such as association rule mining\",\n",
    "                \"2. Take an intermediate data mining course on Udemy or DataCamp\",\n",
    "                \"3. Learn about text mining and natural language processing techniques\",\n",
    "                \"4. Participate in data mining competitions on Kaggle or DrivenData\",\n",
    "                \"5. Read 'Introduction to Data Mining' by Pang-Ning Tan\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics in data mining like deep learning for mining unstructured data\",\n",
    "                \"2. Explore research papers and journals on cutting-edge data mining techniques\",\n",
    "                \"3. Dive deep into big data mining frameworks like Apache Mahout or Spark MLlib\",\n",
    "                \"4. Contribute to open-source data mining projects on GitHub\",\n",
    "                \"5. Stay updated with the latest research and trends in data mining\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"mobile app development\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn the basics of programming languages used in mobile app development like Java or Kotlin for Android or Swift for iOS.\",\n",
    "                \"2. Understand the fundamentals of mobile app development including user interface design and app architecture.\",\n",
    "                \"3. Take an introductory course on mobile app development platforms like Udemy or Coursera.\",\n",
    "                \"4. Practice building simple mobile apps with tutorials from websites like Codecademy or SoloLearn.\",\n",
    "                \"5. Solve beginner-level coding challenges related to mobile app development on websites like LeetCode or HackerRank.\",\n",
    "                \"6. Join online communities or forums dedicated to mobile app development beginners for support and guidance.\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of mobile app development frameworks such as React Native or Flutter.\",\n",
    "                \"2. Take intermediate courses on specific mobile app development topics like advanced UI design or app monetization strategies.\",\n",
    "                \"3. Work on more complex mobile app projects to gain hands-on experience and improve problem-solving skills.\",\n",
    "                \"4. Read books or online resources on mobile app design patterns and best practices.\",\n",
    "                \"5. Collaborate with other amateur developers on GitHub by contributing to open-source mobile app projects.\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics in mobile app development like performance optimization and security.\",\n",
    "                \"2. Explore cutting-edge technologies and trends in mobile app development such as augmented reality or machine learning integration.\",\n",
    "                \"3. Dive deep into mobile app testing methodologies and debugging techniques.\",\n",
    "                \"4. Mentor other developers and share knowledge through writing blogs, giving talks, or hosting workshops.\",\n",
    "                \"5. Contribute to the development of mobile app development tools and libraries to improve the ecosystem.\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"network security\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of computer networks and protocols\",\n",
    "                \"2. Understand common security threats and vulnerabilities\",\n",
    "                \"3. Take an introductory course on network security on Coursera or Udemy\",\n",
    "                \"4. Practice setting up a basic firewall and configuring network security policies\",\n",
    "                \"5. Explore websites like Hack The Box or TryHackMe for beginner-level security challenges\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of cryptography and encryption techniques\",\n",
    "                \"2. Take an intermediate course on ethical hacking and penetration testing\",\n",
    "                \"3. Work on capturing and analyzing network traffic using tools like Wireshark\",\n",
    "                \"4. Solve intermediate-level security challenges on platforms like OverTheWire\",\n",
    "                \"5. Read 'Network Security Essentials' by William Stallings\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics such as network intrusion detection and prevention systems\",\n",
    "                \"2. Study advanced cryptography algorithms and protocols\",\n",
    "                \"3. Dive into reverse engineering and malware analysis\",\n",
    "                \"4. Participate in Capture The Flag (CTF) competitions to test skills\",\n",
    "                \"5. Stay updated with the latest security research and trends through conferences and publications\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"cybersecurity\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of cybersecurity concepts and terminology\",\n",
    "                \"2. Take an introductory cybersecurity course on Coursera or Udemy\",\n",
    "                \"3. Practice basic security concepts like password management and network security\",\n",
    "                \"4. Explore cybersecurity challenges on platforms like Hack The Box or TryHackMe\",\n",
    "                \"5. Solve beginner-level cybersecurity problems on websites like OverTheWire\",\n",
    "                \"6. Participate in cybersecurity capture the flag (CTF) competitions for hands-on experience\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of cybersecurity protocols and encryption techniques\",\n",
    "                \"2. Take an intermediate cybersecurity course focusing on specific domains (e.g., network security, cryptography)\",\n",
    "                \"3. Work on more complex cybersecurity challenges on platforms like RootMe or VulnHub\",\n",
    "                \"4. Read 'Hacking: The Art of Exploitation' by Jon Erickson\",\n",
    "                \"5. Contribute to cybersecurity projects on GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced cybersecurity topics such as penetration testing and ethical hacking\",\n",
    "                \"2. Study advanced cybersecurity courses on platforms like Offensive Security (e.g., OSCP)\",\n",
    "                \"3. Dive into cybersecurity research and vulnerability discovery\",\n",
    "                \"4. Explore advanced cybersecurity certifications (e.g., CISSP, CEH)\",\n",
    "                \"5. Mentor others in cybersecurity through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"artificial intelligence\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand basic concepts of Artificial Intelligence (AI) such as machine learning and neural networks\",\n",
    "                \"2. Take an introductory AI course on Coursera or edX\",\n",
    "                \"3. Practice Python programming for AI with libraries like NumPy and Pandas\",\n",
    "                \"4. Explore basic machine learning algorithms like linear regression and decision trees\",\n",
    "                \"5. Solve beginner-level AI problems on Kaggle\",\n",
    "                \"6. Complete exercises on websites like Codecademy's Python track\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of machine learning techniques and algorithms\",\n",
    "                \"2. Take an intermediate AI course focusing on deep learning\",\n",
    "                \"3. Implement neural networks using frameworks like TensorFlow or PyTorch\",\n",
    "                \"4. Participate in AI competitions on platforms like Kaggle\",\n",
    "                \"5. Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics in AI such as natural language processing and computer vision\",\n",
    "                \"2. Explore cutting-edge research papers and publications in AI\",\n",
    "                \"3. Contribute to open-source AI projects on GitHub\",\n",
    "                \"4. Attend AI conferences and workshops to stay updated with the latest advancements\",\n",
    "                \"5. Mentor others in AI through online forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"big data analytics\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of data analysis and statistics\",\n",
    "                \"2. Take an introductory data analysis course on edX or Coursera\",\n",
    "                \"3. Practice basic data manipulation with Python and Pandas\",\n",
    "                \"4. Explore data visualization libraries like Matplotlib and Seaborn\",\n",
    "                \"5. Solve beginner-level data analysis problems on Kaggle\",\n",
    "                \"6. Work on simple data analysis projects using Jupyter Notebooks\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of statistical analysis and hypothesis testing\",\n",
    "                \"2. Take an intermediate data science course on DataCamp\",\n",
    "                \"3. Learn about machine learning algorithms and techniques\",\n",
    "                \"4. Participate in data analysis competitions on Kaggle\",\n",
    "                \"5. Read 'Python for Data Analysis' by Wes McKinney\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced data manipulation and cleaning techniques\",\n",
    "                \"2. Dive deep into machine learning and predictive modeling\",\n",
    "                \"3. Explore big data technologies like Apache Spark\",\n",
    "                \"4. Contribute to data science projects on GitHub\",\n",
    "                \"5. Stay updated with the latest research and trends in data analysis\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"digital marketing\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the basics of digital marketing: SEO, SEM, SMM, Email Marketing, etc.\",\n",
    "                \"2. Take an introductory digital marketing course on platforms like Coursera, Udemy, or LinkedIn Learning\",\n",
    "                \"3. Start a blog or website to practice content creation and SEO techniques\",\n",
    "                \"4. Join online communities or forums related to digital marketing to stay updated with trends\",\n",
    "                \"5. Practice creating social media posts and advertisements on platforms like Facebook and Instagram\",\n",
    "                \"6. Explore basic analytics tools like Google Analytics to understand website traffic\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of digital marketing strategies and tools\",\n",
    "                \"2. Take an intermediate digital marketing course focusing on specific areas like SEO or Social Media Marketing\",\n",
    "                \"3. Start implementing more advanced SEO techniques like backlink building and on-page optimization\",\n",
    "                \"4. Experiment with running paid advertising campaigns on platforms like Google Ads or Facebook Ads\",\n",
    "                \"5. Analyze data from digital marketing campaigns to optimize performance\",\n",
    "                \"6. Start building an email list and experimenting with email marketing campaigns\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced digital marketing concepts such as conversion rate optimization and marketing automation\",\n",
    "                \"2. Dive into advanced analytics tools and techniques for in-depth data analysis\",\n",
    "                \"3. Develop a comprehensive digital marketing strategy for a business or project\",\n",
    "                \"4. Stay updated with the latest trends and innovations in digital marketing through conferences, webinars, and industry publications\",\n",
    "                \"5. Mentor others in digital marketing through online platforms or local meetups\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"business analysis\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the basics of business analysis and its importance\",\n",
    "                \"2. Learn about different types of requirements and how to gather them\",\n",
    "                \"3. Take an introductory course on business analysis on platforms like Coursera or edX\",\n",
    "                \"4. Practice basic techniques for requirement elicitation and documentation\",\n",
    "                \"5. Solve beginner-level business analysis problems on platforms like Experfy or BA Times\",\n",
    "                \"6. Join online communities or forums to discuss business analysis topics\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of requirement analysis techniques and tools\",\n",
    "                \"2. Take an intermediate-level course on business analysis methodologies\",\n",
    "                \"3. Work on case studies or real-world projects to apply business analysis concepts\",\n",
    "                \"4. Read books like 'Business Analysis Techniques' by James Cadle and Donald Yeates\",\n",
    "                \"5. Participate in workshops or webinars focused on advanced business analysis topics\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced business analysis methodologies such as Agile or Lean\",\n",
    "                \"2. Study advanced techniques for stakeholder management and communication\",\n",
    "                \"3. Obtain certifications like CBAP (Certified Business Analysis Professional)\",\n",
    "                \"4. Mentor others in business analysis through mentorship programs or online platforms\",\n",
    "                \"5. Stay updated with the latest trends and developments in business analysis\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"content writing\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn the basics of grammar and punctuation\",\n",
    "                \"2. Understand the structure of different types of content (articles, blogs, etc.)\",\n",
    "                \"3. Take an introductory writing course on Coursera or Udemy\",\n",
    "                \"4. Practice writing short articles or blog posts\",\n",
    "                \"5. Explore content writing tips and techniques online\",\n",
    "                \"6. Use platforms like Grammarly to improve writing skills\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of SEO principles and keyword research\",\n",
    "                \"2. Take an intermediate content writing course on Skillshare\",\n",
    "                \"3. Write longer-form content such as guides or tutorials\",\n",
    "                \"4. Guest post on websites to gain experience and exposure\",\n",
    "                \"5. Join writing communities or forums for feedback\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced content strategies for different platforms (SEO, social media, etc.)\",\n",
    "                \"2. Study advanced writing techniques such as storytelling and persuasion\",\n",
    "                \"3. Explore niche topics and become an authority in a specific area\",\n",
    "                \"4. Offer writing services professionally or start a blog\",\n",
    "                \"5. Mentor others in content writing through workshops or online platforms\"\n",
    "            ]\n",
    "        },\n",
    "        \"machine vision\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand basic concepts of image processing\",\n",
    "                \"2. Learn about different image formats and their properties\",\n",
    "                \"3. Familiarize yourself with basic machine learning algorithms\",\n",
    "                \"4. Take an introductory course on machine vision on Coursera or edX\",\n",
    "                \"5. Practice basic image processing techniques using OpenCV\",\n",
    "                \"6. Solve beginner-level image processing problems on LeetCode\",\n",
    "                \"7. Explore tutorials on PyImageSearch for practical projects\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of advanced image processing algorithms\",\n",
    "                \"2. Explore deep learning models for computer vision tasks\",\n",
    "                \"3. Take an intermediate course on deep learning for computer vision\",\n",
    "                \"4. Work on more complex image processing challenges on Kaggle\",\n",
    "                \"5. Read 'Computer Vision: Algorithms and Applications' by Richard Szeliski\",\n",
    "                \"6. Implement computer vision projects from research papers\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics in computer vision such as object detection and segmentation\",\n",
    "                \"2. Dive into cutting-edge research papers in computer vision\",\n",
    "                \"3. Contribute to open-source computer vision projects on GitHub\",\n",
    "                \"4. Attend computer vision conferences and workshops\",\n",
    "                \"5. Mentor others in computer vision through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"natural language processing\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of Python programming language\",\n",
    "                \"2. Understand fundamental concepts of NLP such as tokenization and stemming\",\n",
    "                \"3. Take an introductory NLP course on Coursera or Udemy\",\n",
    "                \"4. Explore NLTK (Natural Language Toolkit) for basic NLP tasks\",\n",
    "                \"5. Solve beginner-level NLP problems on LeetCode or HackerRank\",\n",
    "                \"6. Work on simple NLP projects like sentiment analysis or text classification\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of NLP techniques such as named entity recognition and part-of-speech tagging\",\n",
    "                \"2. Take an intermediate NLP course on platforms like DataCamp or edX\",\n",
    "                \"3. Implement more advanced NLP algorithms like word embeddings\",\n",
    "                \"4. Read 'Speech and Language Processing' by Jurafsky & Martin\",\n",
    "                \"5. Participate in NLP competitions on platforms like Kaggle\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced NLP topics such as sequence-to-sequence models and attention mechanisms\",\n",
    "                \"2. Study recent research papers in NLP from conferences like ACL and EMNLP\",\n",
    "                \"3. Dive deep into deep learning frameworks for NLP such as TensorFlow or PyTorch\",\n",
    "                \"4. Contribute to open-source NLP projects on GitHub\",\n",
    "                \"5. Mentor others in NLP through online forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"computer vision\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of image processing and computer vision concepts\",\n",
    "                \"2. Take an introductory computer vision course on Coursera or Udemy\",\n",
    "                \"3. Practice basic image processing techniques with OpenCV library in Python\",\n",
    "                \"4. Explore beginner computer vision projects on GitHub\",\n",
    "                \"5. Solve beginner-level computer vision problems on LeetCode or HackerRank\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of image filtering and feature extraction techniques\",\n",
    "                \"2. Take an intermediate computer vision course on edX or Udacity\",\n",
    "                \"3. Work on more complex computer vision projects involving object detection and tracking\",\n",
    "                \"4. Participate in computer vision competitions on Kaggle\",\n",
    "                \"5. Read 'Learning OpenCV' by Gary Bradski and Adrian Kaehler\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics in computer vision such as deep learning-based approaches\",\n",
    "                \"2. Study advanced computer vision algorithms and architectures\",\n",
    "                \"3. Dive into computer vision research papers and journals\",\n",
    "                \"4. Contribute to open-source computer vision projects on GitHub\",\n",
    "                \"5. Mentor others in computer vision through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"iot development\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand basics of IoT architecture and concepts\",\n",
    "                \"2. Learn about sensors and actuators commonly used in IoT\",\n",
    "                \"3. Study communication protocols such as MQTT and HTTP\",\n",
    "                \"4. Explore IoT development platforms like Arduino and Raspberry Pi\",\n",
    "                \"5. Take an introductory IoT course on Coursera or Udemy\",\n",
    "                \"6. Build simple IoT projects like a temperature monitoring system\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of IoT protocols and networking\",\n",
    "                \"2. Learn about cloud platforms for IoT like AWS IoT and Azure IoT\",\n",
    "                \"3. Study security and privacy considerations in IoT\",\n",
    "                \"4. Explore IoT data analytics and visualization techniques\",\n",
    "                \"5. Work on intermediate-level IoT projects involving multiple sensors and actuators\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced IoT concepts such as edge computing and fog computing\",\n",
    "                \"2. Dive deep into IoT security mechanisms and threat modeling\",\n",
    "                \"3. Explore IoT interoperability standards and frameworks\",\n",
    "                \"4. Contribute to open-source IoT projects on GitHub\",\n",
    "                \"5. Mentor others in IoT development through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"blockchain development\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the basics of blockchain technology and its applications\",\n",
    "                \"2. Learn about cryptographic techniques used in blockchain\",\n",
    "                \"3. Take an introductory blockchain course on Coursera or Udemy\",\n",
    "                \"4. Explore basic concepts like hash functions and digital signatures\",\n",
    "                \"5. Solve beginner-level problems on websites like LeetCode and HackerRank\",\n",
    "                \"6. Build a simple blockchain prototype\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of blockchain consensus mechanisms (e.g., Proof of Work, Proof of Stake)\",\n",
    "                \"2. Take an intermediate blockchain course on Pluralsight or Udacity\",\n",
    "                \"3. Work on more complex blockchain problems on Codewars or CodeSignal\",\n",
    "                \"4. Read 'Mastering Blockchain' by Imran Bashir\",\n",
    "                \"5. Explore Ethereum smart contract development\",\n",
    "                \"6. Build a decentralized application (DApp)\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced blockchain concepts like sharding and sidechains\",\n",
    "                \"2. Study advanced cryptography for blockchain security\",\n",
    "                \"3. Dive deep into blockchain scalability solutions\",\n",
    "                \"4. Contribute to open-source blockchain projects on GitHub\",\n",
    "                \"5. Explore blockchain interoperability and cross-chain communication\",\n",
    "                \"6. Mentor others in blockchain development through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"devops\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the basics of DevOps: principles, practices, and culture\",\n",
    "                \"2. Learn about version control systems like Git\",\n",
    "                \"3. Familiarize yourself with containerization using Docker\",\n",
    "                \"4. Explore configuration management tools like Ansible\",\n",
    "                \"5. Take an introductory DevOps course on Coursera or Udemy\",\n",
    "                \"6. Practice basic DevOps exercises on platforms like Linux Academy\",\n",
    "                \"7. Solve problems on DevOps related topics on LeetCode or HackerRank\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of container orchestration with Kubernetes\",\n",
    "                \"2. Learn about continuous integration and continuous deployment (CI/CD)\",\n",
    "                \"3. Explore infrastructure as code (IaC) concepts using Terraform\",\n",
    "                \"4. Work on more complex DevOps projects integrating multiple tools\",\n",
    "                \"5. Read 'The Phoenix Project' by Gene Kim for insights into DevOps practices\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics like microservices architecture and serverless computing\",\n",
    "                \"2. Dive into monitoring and observability tools like Prometheus and Grafana\",\n",
    "                \"3. Explore advanced DevOps concepts such as chaos engineering\",\n",
    "                \"4. Contribute to open-source DevOps projects on GitHub\",\n",
    "                \"5. Mentor others in DevOps practices through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"cloud architecture\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of cloud computing concepts (e.g., virtualization, scalability)\",\n",
    "                \"2. Take an introductory course on cloud computing platforms like AWS, Azure, or Google Cloud\",\n",
    "                \"3. Practice using basic cloud services such as virtual machines, storage, and networking\",\n",
    "                \"4. Explore cloud computing fundamentals on platforms like Coursera or edX\",\n",
    "                \"5. Solve beginner-level cloud architecture problems on LeetCode or HackerRank\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of cloud infrastructure design principles\",\n",
    "                \"2. Take an intermediate-level course on a specific cloud platform (e.g., AWS Solutions Architect Associate)\",\n",
    "                \"3. Work on more complex cloud architecture scenarios and case studies\",\n",
    "                \"4. Read 'Cloud Native Architectures' by Tom Laszewski and Kamal Arora\",\n",
    "                \"5. Contribute to cloud-related open-source projects on GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced cloud services and solutions for scalability, security, and reliability\",\n",
    "                \"2. Study advanced topics such as cloud-native development, serverless computing, and containerization\",\n",
    "                \"3. Dive deep into cloud architecture patterns and best practices\",\n",
    "                \"4. Explore advanced cloud certifications (e.g., AWS Solutions Architect Professional)\",\n",
    "                \"5. Mentor others in cloud architecture through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"agile methodologies\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the Agile Manifesto and its principles\",\n",
    "                \"2. Learn about Scrum framework and its roles (Scrum Master, Product Owner, Development Team)\",\n",
    "                \"3. Take an introductory course on Agile methodologies on Coursera or Udemy\",\n",
    "                \"4. Explore Agile practices like Daily Stand-ups, Sprint Planning, and Retrospectives\",\n",
    "                \"5. Participate in a beginner-level Scrum simulation workshop\",\n",
    "                \"6. Practice creating user stories and estimating effort using Planning Poker\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of Agile methodologies by studying various Agile frameworks (Kanban, XP, Lean)\",\n",
    "                \"2. Take an intermediate Agile course on Pluralsight or LinkedIn Learning\",\n",
    "                \"3. Work on more complex Agile simulations and case studies\",\n",
    "                \"4. Read 'Scrum: The Art of Doing Twice the Work in Half the Time' by Jeff Sutherland\",\n",
    "                \"5. Attend Agile conferences or meetups to network with professionals\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced Agile concepts such as scaling Agile for large organizations (SAFe, LeSS)\",\n",
    "                \"2. Study Agile coaching techniques and facilitation skills\",\n",
    "                \"3. Dive into Agile transformation strategies and organizational change management\",\n",
    "                \"4. Explore advanced Agile workshops and certifications (Certified Scrum Trainer, SAFe Program Consultant)\",\n",
    "                \"5. Mentor others in Agile methodologies through coaching and training sessions\"\n",
    "            ]\n",
    "        },\n",
    "        \"scrum framework\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the Agile Manifesto and its principles\",\n",
    "                \"2. Learn about Scrum roles (Scrum Master, Product Owner, Development Team)\",\n",
    "                \"3. Read 'Scrum Guide' to understand Scrum framework\",\n",
    "                \"4. Take an introductory Scrum course on Coursera or Udemy\",\n",
    "                \"5. Participate in a Scrum Foundation workshop\",\n",
    "                \"6. Practice creating and refining Product Backlog items\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of Scrum artifacts (Product Backlog, Sprint Backlog, Increment)\",\n",
    "                \"2. Take an intermediate Scrum course on Pluralsight or LinkedIn Learning\",\n",
    "                \"3. Work on more complex Scrum simulations and case studies\",\n",
    "                \"4. Read 'Scrum Mastery: From Good to Great Servant Leadership' by Geoff Watts\",\n",
    "                \"5. Attend Scrum Alliance Certified Scrum Master (CSM) training\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced Scrum topics such as scaling Scrum (Scrum@Scale, Nexus)\",\n",
    "                \"2. Study Agile coaching techniques and facilitation skills\",\n",
    "                \"3. Dive into Scrum transformation strategies and organizational change management\",\n",
    "                \"4. Explore advanced Scrum workshops and certifications (Certified Scrum Trainer, Certified Scrum Professional)\",\n",
    "                \"5. Mentor others in Scrum framework through coaching and training sessions\"\n",
    "            ]\n",
    "        },\n",
    "        \"software testing\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand basic software testing concepts (types of testing, test cases, test plans)\",\n",
    "                \"2. Learn manual testing techniques and practices\",\n",
    "                \"3. Take an introductory software testing course on Udemy or Coursera\",\n",
    "                \"4. Explore basic testing tools like Selenium IDE or JUnit\",\n",
    "                \"5. Solve beginner-level testing problems on HackerRank or CodeSignal\",\n",
    "                \"6. Practice writing test cases for simple applications\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of software testing methodologies (Agile testing, TDD)\",\n",
    "                \"2. Take an intermediate software testing course on Pluralsight\",\n",
    "                \"3. Work on more complex testing scenarios and test automation\",\n",
    "                \"4. Read 'Agile Testing: A Practical Guide for Testers and Agile Teams' by Lisa Crispin and Janet Gregory\",\n",
    "                \"5. Contribute to open-source testing projects on GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced testing techniques such as test-driven development (TDD) and behavior-driven development (BDD)\",\n",
    "                \"2. Study advanced test automation frameworks like Selenium WebDriver or Appium\",\n",
    "                \"3. Dive into performance testing, security testing, and other specialized testing areas\",\n",
    "                \"4. Explore advanced testing courses and certifications on platforms like Udacity or LinkedIn Learning\",\n",
    "                \"5. Mentor others in software testing through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"ux research\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the basics of UX research and its importance\",\n",
    "                \"2. Learn about different research methods (surveys, interviews, usability testing)\",\n",
    "                \"3. Take an introductory UX research course on Coursera or Udemy\",\n",
    "                \"4. Explore beginner-level UX research projects on platforms like Behance or Dribbble\",\n",
    "                \"5. Practice conducting simple usability tests with friends or family\",\n",
    "                \"6. Read 'Don't Make Me Think' by Steve Krug\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of UX research methodologies and analysis techniques\",\n",
    "                \"2. Take an intermediate UX research course on Interaction Design Foundation\",\n",
    "                \"3. Work on more complex research projects with real users\",\n",
    "                \"4. Learn about creating personas and journey maps\",\n",
    "                \"5. Attend UX research workshops or conferences to learn from professionals\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced UX research techniques such as eye-tracking and remote usability testing\",\n",
    "                \"2. Study qualitative and quantitative data analysis methods\",\n",
    "                \"3. Dive into UX research tools and software (e.g., Morae, Optimal Workshop)\",\n",
    "                \"4. Explore advanced UX research courses on platforms like Nielsen Norman Group\",\n",
    "                \"5. Mentor others in UX research through workshops or online forums\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"data warehousing\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the basics of data warehousing and its importance in business intelligence\",\n",
    "                \"2. Learn about data warehousing concepts such as ETL (Extract, Transform, Load) processes\",\n",
    "                \"3. Take an introductory course on data warehousing fundamentals on edX or Coursera\",\n",
    "                \"4. Explore data warehousing tools like Amazon Redshift, Google BigQuery, or Snowflake\",\n",
    "                \"5. Practice creating simple data warehouse schemas and dimensional models\",\n",
    "                \"6. Work on basic SQL queries for data extraction and transformation\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of dimensional modeling techniques (star schema, snowflake schema)\",\n",
    "                \"2. Take an intermediate course on data warehousing design and implementation\",\n",
    "                \"3. Explore advanced ETL tools like Informatica or Talend\",\n",
    "                \"4. Practice optimizing data warehouse performance and tuning SQL queries\",\n",
    "                \"5. Read 'The Data Warehouse Toolkit' by Ralph Kimball\",\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced data warehousing architectures (data lakes, data vaults)\",\n",
    "                \"2. Study advanced topics in data warehousing automation and orchestration\",\n",
    "                \"3. Dive into real-time data warehousing and streaming analytics\",\n",
    "                \"4. Explore data warehouse security and compliance\",\n",
    "                \"5. Stay updated with emerging trends in data warehousing and cloud-native solutions\"\n",
    "            ]\n",
    "        },\n",
    "        \"predictive analytics\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of statistics and probability theory\",\n",
    "                \"2. Take an introductory course on predictive analytics on Coursera or edX\",\n",
    "                \"3. Practice basic data manipulation with Python and Pandas\",\n",
    "                \"4. Explore data visualization libraries like Matplotlib and Seaborn\",\n",
    "                \"5. Solve beginner-level predictive modeling problems on Kaggle\",\n",
    "                \"6. Work on simple predictive analytics projects using Jupyter Notebooks\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of predictive modeling techniques such as regression and classification\",\n",
    "                \"2. Take an intermediate predictive analytics course on DataCamp or LinkedIn Learning\",\n",
    "                \"3. Learn about feature engineering and model evaluation metrics\",\n",
    "                \"4. Participate in predictive modeling competitions on Kaggle\",\n",
    "                \"5. Read 'Introduction to Statistical Learning' by Gareth James et al.\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced predictive modeling algorithms such as ensemble methods and deep learning\",\n",
    "                \"2. Dive deep into time series analysis and forecasting techniques\",\n",
    "                \"3. Explore big data technologies for predictive analytics like Apache Spark MLlib\",\n",
    "                \"4. Contribute to predictive analytics projects on GitHub\",\n",
    "                \"5. Stay updated with the latest research and trends in predictive analytics\"\n",
    "            ]\n",
    "        },\n",
    "        \"data governance\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn the basics of data governance and its importance\",\n",
    "                \"2. Understand key concepts such as data quality, data stewardship, and data ownership\",\n",
    "                \"3. Take an introductory course on data governance on edX or Coursera\",\n",
    "                \"4. Explore data governance frameworks like DAMA-DMBOK or IBM Data Governance Council\",\n",
    "                \"5. Participate in beginner-level data governance workshops or webinars\",\n",
    "                \"6. Join data governance forums or communities to learn from professionals\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of data governance principles and practices\",\n",
    "                \"2. Take an intermediate data governance course on Udemy or LinkedIn Learning\",\n",
    "                \"3. Work on case studies and real-world scenarios related to data governance\",\n",
    "                \"4. Read 'Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program' by John Ladley\",\n",
    "                \"5. Attend data governance conferences or seminars to network with experts\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics in data governance such as regulatory compliance and data privacy\",\n",
    "                \"2. Study advanced data governance methodologies and frameworks\",\n",
    "                \"3. Dive deep into data governance implementation strategies and best practices\",\n",
    "                \"4. Explore advanced data governance courses and certifications (CDMP, DGSP)\",\n",
    "                \"5. Mentor others in data governance through workshops or consulting\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"data integration\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of data integration and ETL (Extract, Transform, Load)\",\n",
    "                \"2. Take an introductory course on data integration on Coursera or Udemy\",\n",
    "                \"3. Practice basic data manipulation with Python or SQL\",\n",
    "                \"4. Explore ETL tools like Talend or Informatica\",\n",
    "                \"5. Solve beginner-level data integration problems on LeetCode\",\n",
    "                \"6. Work on simple data integration projects using Jupyter Notebooks or similar tools\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of data integration architectures and patterns\",\n",
    "                \"2. Take an intermediate course on advanced ETL techniques\",\n",
    "                \"3. Learn about data warehousing concepts and dimensional modeling\",\n",
    "                \"4. Participate in data integration challenges on Kaggle or HackerRank\",\n",
    "                \"5. Read 'The Data Warehouse Toolkit' by Ralph Kimball\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced ETL concepts such as real-time data integration and CDC (Change Data Capture)\",\n",
    "                \"2. Dive deep into data governance and metadata management\",\n",
    "                \"3. Explore cloud-based data integration solutions like AWS Glue or Azure Data Factory\",\n",
    "                \"4. Contribute to data integration projects on GitHub or similar platforms\",\n",
    "                \"5. Stay updated with the latest trends and technologies in data integration\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "         \"data quality management\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the importance of data quality and its impact on business decisions\",\n",
    "                \"2. Learn basic concepts of data cleansing and data profiling\",\n",
    "                \"3. Take an introductory course on Data Quality Management on Coursera or edX\",\n",
    "                \"4. Explore data quality assessment tools like Talend or Informatica\",\n",
    "                \"5. Practice identifying data quality issues in sample datasets\",\n",
    "                \"6. Learn about data governance and data quality frameworks\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of data quality dimensions (Accuracy, Completeness, Consistency, etc.)\",\n",
    "                \"2. Take an intermediate Data Quality Management course on Pluralsight or LinkedIn Learning\",\n",
    "                \"3. Work on more complex data quality projects or case studies\",\n",
    "                \"4. Study advanced data profiling techniques and data quality metrics\",\n",
    "                \"5. Read 'Data Quality: The Accuracy Dimension' by Jack E. Olson\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced data cleansing and standardization techniques\",\n",
    "                \"2. Dive deep into data quality automation and monitoring strategies\",\n",
    "                \"3. Explore data quality in the context of big data and real-time data streams\",\n",
    "                \"4. Contribute to data quality improvement initiatives in your organization\",\n",
    "                \"5. Stay updated with the latest research and trends in data quality management\"\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        \"data privacy management\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the basics of data privacy laws and regulations (e.g., GDPR, CCPA)\",\n",
    "                \"2. Take an introductory course on Data Privacy Management on Coursera or edX\",\n",
    "                \"3. Familiarize yourself with privacy principles and concepts (e.g., data minimization, consent)\",\n",
    "                \"4. Explore case studies on data breaches and privacy violations\",\n",
    "                \"5. Join online forums or communities focused on data privacy\",\n",
    "                \"6. Begin creating a personal data inventory for practice\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of specific privacy regulations and compliance requirements\",\n",
    "                \"2. Take an intermediate course on Privacy Impact Assessments (PIAs)\",\n",
    "                \"3. Work on simulated privacy impact assessment projects\",\n",
    "                \"4. Study privacy-enhancing technologies (PETs) and anonymization techniques\",\n",
    "                \"5. Participate in privacy-related workshops and webinars\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics in data anonymization and pseudonymization\",\n",
    "                \"2. Study privacy by design principles and methodologies\",\n",
    "                \"3. Dive into privacy engineering and secure data handling practices\",\n",
    "                \"4. Explore advanced courses on Privacy-Enhancing Technologies (PETs)\",\n",
    "                \"5. Publish research papers or articles on data privacy topics\"\n",
    "            ]\n",
    "        },\n",
    "        \"data architecture\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basic concepts of data architecture and data modeling\",\n",
    "                \"2. Take an introductory course on data architecture on Coursera or edX\",\n",
    "                \"3. Practice basic SQL queries and database design principles\",\n",
    "                \"4. Explore data modeling tools like Lucidchart or Erwin\",\n",
    "                \"5. Solve beginner-level data modeling exercises\",\n",
    "                \"6. Work on simple data architecture projects using relational databases\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of data modeling techniques and normalization\",\n",
    "                \"2. Take an intermediate course on data architecture and database design\",\n",
    "                \"3. Learn about data warehouse design and dimensional modeling\",\n",
    "                \"4. Practice designing complex database schemas for real-world scenarios\",\n",
    "                \"5. Read 'Data Architecture: A Primer for the Data Scientist' by W.H. Inmon\",\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced data architecture concepts such as data governance and metadata management\",\n",
    "                \"2. Study big data architecture frameworks like Hadoop and Spark\",\n",
    "                \"3. Dive into cloud-based data architecture solutions (AWS, Azure)\",\n",
    "                \"4. Explore advanced courses on data architecture optimization and scalability\",\n",
    "                \"5. Mentor others in data architecture principles and best practices\"\n",
    "            ]\n",
    "        },\n",
    "        \"data modeling\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Understand the basics of relational databases and data modeling concepts (Entity-Relationship Diagrams, Normalization)\",\n",
    "                \"2. Take an introductory course on data modeling on Coursera or Udemy\",\n",
    "                \"3. Practice creating simple data models for real-world scenarios\",\n",
    "                \"4. Learn about different types of relationships (one-to-one, one-to-many, many-to-many)\",\n",
    "                \"5. Explore tools like Lucidchart or Draw.io for creating data models\",\n",
    "                \"6. Study basic SQL queries for querying and manipulating data in databases\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of advanced data modeling techniques (denormalization, star schema, snowflake schema)\",\n",
    "                \"2. Take an intermediate course on database design and optimization\",\n",
    "                \"3. Work on more complex data modeling projects involving multiple entities and relationships\",\n",
    "                \"4. Learn about NoSQL databases and their data modeling approaches\",\n",
    "                \"5. Read 'The Data Warehouse Toolkit' by Ralph Kimball\",\n",
    "                \"6. Practice optimizing data models for performance and scalability\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced data modeling concepts such as dimensional modeling and fact tables\",\n",
    "                \"2. Study industry-specific data modeling patterns and best practices\",\n",
    "                \"3. Dive into data architecture and design principles\",\n",
    "                \"4. Explore advanced database technologies like columnar databases or graph databases\",\n",
    "                \"5. Contribute to open-source data modeling projects or tools\",\n",
    "                \"6. Mentor others in data modeling through workshops or online forums\"\n",
    "            ]\n",
    "        },\n",
    "        \"data storage\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn the basics of data storage concepts (relational databases, NoSQL databases, etc.)\",\n",
    "                \"2. Take an introductory course on databases and SQL on Coursera or edX\",\n",
    "                \"3. Practice basic SQL queries on online platforms like HackerRank or LeetCode\",\n",
    "                \"4. Explore relational database management systems (RDBMS) like MySQL or PostgreSQL\",\n",
    "                \"5. Learn about data modeling and normalization techniques\",\n",
    "                \"6. Work on simple database projects, such as creating a basic inventory management system\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of advanced database concepts (transactions, indexing, etc.)\",\n",
    "                \"2. Take an intermediate course on NoSQL databases like MongoDB or Cassandra\",\n",
    "                \"3. Study data warehousing and data lakes architectures\",\n",
    "                \"4. Learn about cloud-based databases and storage solutions (AWS RDS, Azure Cosmos DB, etc.)\",\n",
    "                \"5. Practice designing and optimizing database schemas for performance\",\n",
    "                \"6. Work on more complex database projects involving multiple tables and relationships\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master database administration and optimization techniques\",\n",
    "                \"2. Study advanced topics like sharding, replication, and high availability\",\n",
    "                \"3. Dive into big data storage technologies like Hadoop Distributed File System (HDFS)\",\n",
    "                \"4. Explore distributed databases and NewSQL databases like Google Spanner\",\n",
    "                \"5. Contribute to open-source database projects on GitHub\",\n",
    "                \"6. Stay updated with emerging trends in data storage and database management\"\n",
    "            ]\n",
    "        },\n",
    "        \"data security\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of cybersecurity and data protection\",\n",
    "                \"2. Take an introductory course on cybersecurity on Coursera or edX\",\n",
    "                \"3. Understand common threats to data security such as malware, phishing, and social engineering\",\n",
    "                \"4. Learn about encryption techniques and basic cryptography principles\",\n",
    "                \"5. Practice basic security hygiene like using strong passwords and enabling two-factor authentication\",\n",
    "                \"6. Explore beginner-friendly cybersecurity blogs and forums for additional learning\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of network security concepts and protocols\",\n",
    "                \"2. Take an intermediate cybersecurity course on Cybrary or Udemy\",\n",
    "                \"3. Learn about access control mechanisms and identity management\",\n",
    "                \"4. Work on hands-on labs and capture the flag (CTF) challenges\",\n",
    "                \"5. Participate in cybersecurity competitions and hackathons\",\n",
    "                \"6. Read 'CISSP All-in-One Exam Guide' by Shon Harris for a comprehensive overview\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics in data security such as threat intelligence and security analytics\",\n",
    "                \"2. Study advanced cryptography and cryptanalysis techniques\",\n",
    "                \"3. Dive into penetration testing and ethical hacking methodologies\",\n",
    "                \"4. Obtain professional certifications like Certified Information Systems Security Professional (CISSP) or Certified Ethical Hacker (CEH)\",\n",
    "                \"5. Contribute to open-source cybersecurity projects and research\"\n",
    "            ]\n",
    "        },\n",
    "        \"data analysis tools\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of data analysis and statistics\",\n",
    "                \"2. Take an introductory data analysis course on edX or Coursera\",\n",
    "                \"3. Practice basic data manipulation with Python and Pandas\",\n",
    "                \"4. Explore data visualization libraries like Matplotlib and Seaborn\",\n",
    "                \"5. Solve beginner-level data analysis problems on Kaggle\",\n",
    "                \"6. Work on simple data analysis projects using Jupyter Notebooks\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of statistical analysis and hypothesis testing\",\n",
    "                \"2. Take an intermediate data science course on DataCamp\",\n",
    "                \"3. Learn about machine learning algorithms and techniques\",\n",
    "                \"4. Participate in data analysis competitions on Kaggle\",\n",
    "                \"5. Read 'Python for Data Analysis' by Wes McKinney\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced data manipulation and cleaning techniques\",\n",
    "                \"2. Dive deep into machine learning and predictive modeling\",\n",
    "                \"3. Explore big data technologies like Apache Spark\",\n",
    "                \"4. Contribute to data science projects on GitHub\",\n",
    "                \"5. Stay updated with the latest research and trends in data analysis\"\n",
    "            ]\n",
    "        },\n",
    "        \"data visualization tools\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of data visualization principles and techniques\",\n",
    "                \"2. Take an introductory data visualization course on Coursera or Udemy\",\n",
    "                \"3. Practice creating simple visualizations with Matplotlib and Seaborn\",\n",
    "                \"4. Explore tutorials on data visualization libraries like Plotly and Bokeh\",\n",
    "                \"5. Solve beginner-level data visualization problems on Kaggle\",\n",
    "                \"6. Work on simple data visualization projects using Jupyter Notebooks\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of advanced data visualization techniques\",\n",
    "                \"2. Take an intermediate data visualization course on DataCamp\",\n",
    "                \"3. Experiment with interactive visualizations using Plotly Dash\",\n",
    "                \"4. Explore advanced visualization concepts like geospatial mapping\",\n",
    "                \"5. Participate in data visualization challenges on Kaggle or DataHack\",\n",
    "                \"6. Read 'The Visual Display of Quantitative Information' by Edward Tufte\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced data visualization libraries like D3.js\",\n",
    "                \"2. Study design principles for effective data storytelling\",\n",
    "                \"3. Dive deep into customizing visualizations for specific datasets\",\n",
    "                \"4. Explore cutting-edge visualization research and techniques\",\n",
    "                \"5. Contribute to data visualization projects on GitHub\",\n",
    "                \"6. Mentor others in data visualization through forums or communities\"\n",
    "            ]\n",
    "        },\n",
    "        \"statistical software\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basics of statistics and its application in data analysis\",\n",
    "                \"2. Take an introductory course on statistical software like SPSS or R\",\n",
    "                \"3. Practice basic statistical analysis techniques such as descriptive statistics\",\n",
    "                \"4. Explore basic data visualization techniques in the chosen statistical software\",\n",
    "                \"5. Solve beginner-level statistical problems on platforms like LeetCode or HackerRank\",\n",
    "                \"6. Work on simple statistical analysis projects using Jupyter Notebooks\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of advanced statistical concepts like hypothesis testing\",\n",
    "                \"2. Take an intermediate course on the chosen statistical software focusing on data analysis\",\n",
    "                \"3. Learn about advanced statistical techniques like regression analysis\",\n",
    "                \"4. Participate in data analysis competitions or challenges related to statistics\",\n",
    "                \"5. Read 'Introduction to Statistical Learning' by Gareth James et al.\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced statistical modeling techniques like time series analysis\",\n",
    "                \"2. Dive deep into multivariate statistical analysis and experimental design\",\n",
    "                \"3. Explore advanced features and programming capabilities of the chosen statistical software\",\n",
    "                \"4. Contribute to statistical software projects or packages on GitHub\",\n",
    "                \"5. Stay updated with the latest research and trends in statistical analysis\"\n",
    "            ]\n",
    "        },\n",
    "        \"databases\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn basic concepts of databases (relational vs. non-relational)\",\n",
    "                \"2. Understand SQL fundamentals (SELECT, INSERT, UPDATE, DELETE)\",\n",
    "                \"3. Take an introductory database course on Coursera or Udemy\",\n",
    "                \"4. Practice SQL queries on websites like LeetCode and HackerRank\",\n",
    "                \"5. Create simple database projects using SQLite or MySQL\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of database design principles (normalization, indexing)\",\n",
    "                \"2. Learn about advanced SQL concepts (joins, subqueries, transactions)\",\n",
    "                \"3. Work on complex database modeling exercises\",\n",
    "                \"4. Participate in database design challenges on platforms like CodeSignal\",\n",
    "                \"5. Read 'Database Systems: The Complete Book' by Hector Garcia-Molina\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced database administration and optimization techniques\",\n",
    "                \"2. Study NoSQL databases and their use cases (MongoDB, Cassandra)\",\n",
    "                \"3. Explore data warehousing and OLAP concepts\",\n",
    "                \"4. Contribute to open-source database projects on GitHub\",\n",
    "                \"5. Stay updated with the latest trends and technologies in databases\"\n",
    "            ]\n",
    "        },\n",
    "        \"programming languages\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Choose a programming language to start with (e.g., Python, Java, C++)\",\n",
    "                \"2. Learn basic syntax and concepts of the selected language\",\n",
    "                \"3. Practice simple programs and exercises to reinforce learning\",\n",
    "                \"4. Take an introductory course on platforms like Coursera, Udemy, or Codecademy\",\n",
    "                \"5. Solve beginner-level problems on coding websites like LeetCode, HackerRank, or CodeSignal\",\n",
    "                \"6. Build small projects to apply what you've learned\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of data structures and algorithms in the chosen language\",\n",
    "                \"2. Take intermediate-level courses focusing on specific language features or application domains\",\n",
    "                \"3. Work on more challenging coding problems on platforms like LeetCode, Codeforces, or AtCoder\",\n",
    "                \"4. Read advanced programming books or research papers related to your language of choice\",\n",
    "                \"5. Collaborate on open-source projects to gain practical experience and contribute to the community\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced topics such as concurrency, optimization, or advanced language features\",\n",
    "                \"2. Dive into the internals of your language and understand how it works under the hood\",\n",
    "                \"3. Explore specialized areas like game development, system programming, or AI using your language\",\n",
    "                \"4. Mentor others in the community, participate in forums, or give talks at conferences\",\n",
    "                \"5. Continuously challenge yourself with new problems and stay updated with the latest developments\"\n",
    "            ]\n",
    "        },\n",
    "        \"software development tools\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Familiarize yourself with basic software development tools like text editors and version control systems\",\n",
    "                \"2. Learn the fundamentals of programming languages commonly used in software development\",\n",
    "                \"3. Take an introductory course on using Git and GitHub for version control\",\n",
    "                \"4. Practice basic coding exercises on platforms like Codecademy or LeetCode\",\n",
    "                \"5. Explore online tutorials on popular integrated development environments (IDEs) like Visual Studio Code or JetBrains IntelliJ IDEA\",\n",
    "                \"6. Build simple projects to apply your newly acquired skills\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen your understanding of version control concepts and advanced Git workflows\",\n",
    "                \"2. Explore more advanced features of your preferred IDE, such as debugging and profiling tools\",\n",
    "                \"3. Learn about unit testing and continuous integration practices\",\n",
    "                \"4. Work on larger coding projects to gain experience with project organization and collaboration\",\n",
    "                \"5. Contribute to open-source projects on platforms like GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced software development tools and techniques specific to your chosen programming languages and frameworks\",\n",
    "                \"2. Dive deep into DevOps practices and tools for automating software development processes\",\n",
    "                \"3. Explore advanced topics such as containerization with Docker and orchestration with Kubernetes\",\n",
    "                \"4. Mentor others in software development tools and best practices through online forums or communities\",\n",
    "                \"5. Stay updated with the latest trends and advancements in software development\"\n",
    "            ]\n",
    "        },\n",
    "        \"web development tools\": {\n",
    "            \"beginner\": [\n",
    "                \"1. Learn HTML basics (tags, attributes, structure)\",\n",
    "                \"2. Understand CSS fundamentals (selectors, properties, styling)\",\n",
    "                \"3. Practice basic JavaScript concepts (variables, loops, functions)\",\n",
    "                \"4. Take an introductory web development course on Codecademy\",\n",
    "                \"5. Complete exercises on W3Schools to reinforce learning\",\n",
    "                \"6. Build simple static websites using HTML, CSS, and JavaScript\"\n",
    "            ],\n",
    "            \"amateur\": [\n",
    "                \"1. Deepen understanding of CSS layout techniques (Flexbox, Grid)\",\n",
    "                \"2. Learn about responsive web design principles\",\n",
    "                \"3. Explore JavaScript frameworks/libraries like React or Vue.js\",\n",
    "                \"4. Work on more complex projects (e.g., interactive web applications)\",\n",
    "                \"5. Contribute to open-source web development projects on GitHub\"\n",
    "            ],\n",
    "            \"expert\": [\n",
    "                \"1. Master advanced CSS concepts (animations, preprocessors)\",\n",
    "                \"2. Dive into backend development with Node.js or Django\",\n",
    "                \"3. Learn about databases and server-side technologies\",\n",
    "                \"4. Explore advanced topics like web security and performance optimization\",\n",
    "                \"5. Mentor others in web development through forums or communities\"\n",
    "            ]\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    # Check if the provided skill and level have a predefined study plan\n",
    "    if skill.lower() in study_plans and level.lower() in study_plans[skill.lower()]:\n",
    "        print(f\"Study plan for {level} {skill}:\")\n",
    "        for step in study_plans[skill.lower()][level.lower()]:\n",
    "            print(step)\n",
    "    else:\n",
    "        print(\"Sorry, we don't have a predefined study plan for that skill or level. Please try again.\")\n",
    "\n",
    "# Main code\n",
    "def main():\n",
    "    # Get skill input from the user\n",
    "    skill = input(\"Enter a skill you want to study (Java Development, Data Analyst, or Project Management): \")\n",
    "    \n",
    "    # Get level input from the user\n",
    "    level = input(\"Enter your skill level (Beginner, Amateur, or Expert): \")\n",
    "\n",
    "    # Generate study plan for the provided skill and level\n",
    "    generate_study_plan(skill, level)\n",
    "\n",
    "# Run the main code\n",
    "main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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