A Roadmap

Below is a very good AI answer to a question I ask to the Claude Heroku model to hand out a data analyst roadmap. But for the Excel part of the answer I did some digging and found those playlists exactly what a beginner data analyst would need to master Excel (an essential tool in Data Analysis):

• The 1st playlist is: Excel 365 Basics Free Course: Learn to Efficiently Use Excel at Work and to Get a Job.

• The 2nd playlist is: Excel 365 & Power BI Intermediate to Advanced Free Course.

That’s it. Now you may go see the AI’s answer.

Here’s a detailed roadmap to become a data analyst, with free study materials and resources:

Phase 1: Fundamentals (1-2 months)

1. Statistics and Data Analysis
   • Khan Academy’s Statistics and Probability course: https://www.khanacademy.org/math/statistics-probability
   • edX’s Data Analysis and Interpretation course (audit for free): https://www.edx.org/course/data-analysis-and-interpretation
2. Microsoft Excel
   • Microsoft Excel Tutorials (free): https://support.microsoft.com/en-us/office/excel-tutorials-9bc0539f-048c-4a1b-8c95-54c2c9c96a5e
   • Excel Is Fun (YouTube channel): https://www.youtube.com/user/excelisfun
3. SQL and Data Management
   • SQLCourse (free online course): https://www.sqlcourse.com/
   • W3Schools SQL Tutorial (free): https://www.w3schools.com/sql/

Phase 2: Data Visualization and Tools (1-2 months)

1. Data Visualization
   • DataCamp’s Data Visualization with Python course (audit for free): https://www.datacamp.com/courses/data-visualization-with-python
   • Tableau’s Data Visualization Tutorials (free): https://www.tableau.com/learn
2. Python and Data Analysis Libraries
   • Python.org (official Python documentation): https://docs.python.org/3/
   • Pandas Documentation (free): https://pandas.pydata.org/docs/
   • NumPy Documentation (free): https://numpy.org/doc/
3. Power BI and Reporting
   • Microsoft Power BI Tutorials (free): https://powerbi.microsoft.com/en-us/tutorials/
   • Power BI YouTube channel: https://www.youtube.com/c/PowerBI

Phase 3: Machine Learning and Modeling (1-2 months)

1. Machine Learning Basics
   • Andrew Ng’s Machine Learning course (Coursera, audit for free): https://www.coursera.org/learn/machine-learning
   • scikit-learn Documentation (free): https://scikit-learn.org/stable/
2. Supervised and Unsupervised Learning
   • DataCamp’s Supervised Learning with Python course (audit for free): https://www.datacamp.com/courses/supervised-learning-with-python
   • Unsupervised Learning with Python (DataCamp, audit for free): https://www.datacamp.com/courses/unsupervised-learning-with-python

Phase 4: Practice and Projects (2-3 months)

1. Kaggle
   • Kaggle’s Data Science Tutorials (free): https://www.kaggle.com/learn/overview
   • Participate in Kaggle competitions and practice with public datasets.
2. Personal Projects
   • Choose a dataset and build a project using the skills you’ve learned.
   • Share your project on platforms like GitHub or Kaggle.

Additional Resources

• Books
  • “Data Analysis with Python” by Wes McKinney (free PDF): https://www.pythonfordatascience.org/
  • “Data Visualization: A Handbook for Data Driven Design” by Andy Kirk (free PDF): https://www.amazon.com/Data-Visualization-Handbook-Driven-Design/dp/1473912147
• Communities
  • Reddit’s r/dataanalysis and r/statistics communities: https://www.reddit.com/r/dataanalysis/ and https://www.reddit.com/r/statistics/
  • Data Science subreddit: https://www.reddit.com/r datascience/

Job Preparation

• Resume and LinkedIn Profile
  • Tailor your resume to highlight data analysis skills.
  • Update your LinkedIn profile to showcase your skills and experience.
• Whiteboarding Exercises
  • Practice whiteboarding exercises to improve your communication skills.
  • Use platforms like Pramp or Glassdoor to practice.