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Curriculum

If you would love to have the doc of the curriculum or a breakdown of each topic kindly send a mail to [email protected] using the Subject "100daysofdscode"

  • Week 1 Python for Data Science

    • Day 1. Introduction on Python for Data Science and Why Data Scientist should use Python.
    • Day 2. Market Potential for Python,5 industries that make effective use of their data, and OOP (object-oriented programming).
    • Day 3. The Basic of Python
    • Day 4-5. Python Libraries for Data Science
    • Project

  • Week 2 Statistics and probability(Maths for Data Science)

    • Day 6-7. Introduction to Statisics.
    • Day 8. Introduction to Probability.
    • Day 9-10. Introduction to Linear Algebra.
    • Project

  • Week 3 Intro to computing for Data Analysis

    • Day 11-12. Data Science Mehodology.
    • Day 13. Data Science Glossary and Open Source tools for Data Science.
    • Day 14 - 15. Exploratory Data Analysis.
    • Project

  • Week 4 Data Visualization

    • Day 16-17. Introduction to Data Visualization Tools.
    • Day 18-19. Advanced Visualizations and Geospatial Data
    • Day 20. Kepler.gl
    • Project

  • Week 5 Machine Learning

    • Day 21. Introduction to Machine Learning and Python Libraries and Algorithms for Machine Learning.
    • Day 22. Regression, Classififcation, and Clustering.
    • Day 23. Machine Learning workshop 1(Regression)
    • Day 24. Machine Learning workshop 2(Classfication)
    • Day 25. Machine Learning Workshop 3(Clustering)
    • Project

  • Week 6 Solving Zindi and Kaggle Challenge( Santander Customer Transaction Prediction, Kaggle Career Con)

    • Santander Customer Transaction Prediction
    • Predicting Future Sales
    • Data Science Loan Prediction Challenge

  • Week 7 Data Analytics tools

    • Day 31. Data Analytics Techniques and Big Data
    • Day 32-33. Introduction to excel.
    • Day 34-35. Introduction to Tableau.
    • Project

  • Week 8 Analytics storytelling

    • Day 36. Data Analytics Storytelling
    • Day 37-38. Ethic and laws of Data and Analytics.
    • Day 39-40. Working in an Data oriented company.
    • Project

  • Week 9 Deep Learning

    • Day 41. Introduction to deep learning
    • Day 42-43. Introduction to neural networks
    • Day 44-45. Intro to optimization
    • Project

  • Week 10 Ways to use deep learning to solve a problem

    • Project

  • Week 11- 12 Solve a Kaggle/ Zindi chanllege

  • Week 13 Intro to Database

    • Day 66. Introduction to Database
    • Day 67-68. SQL, Mongodb and Nosql
    • Day 69-70. Big query & IBM DB Warehouse

  • Week 14 - 15 Hadoop(mapreduce)

    • Day 76. Introduction to Hadoop and Mapreduce.
    • Day 77. HDFS and Mapreduce
    • Day 78-79. Write your first mapreduce code
    • Day 80. Mapreduce design pattern
    • Day 71-75. Project

  • Week 16 Solve a Kaggle / Zindi challenge

    -Project

  • Week 17 Web scraping

    • Day 81-82. Intro to webscraping -The Andela way
    • Day 83-85. Webscraping tools
    • Day Project

  • Week 18 - 20 Solve Kaggle Challenge