June 12, 2020
At the end of this 100 Days challenge, I would like to build a rich portfolio of data science Skills and projects.
| Day | Topic | Learning Concepts |
|---|---|---|
| Day 1 | Data Science: R Basics | Introduction and Welcome Section 1: R Basics, Functions, and Data Types Section 2: Vectors, Sorting |
| Day 2 | Section 3: Indexing, Data Wrangling, Plots Section 4: Programming Basics |
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| Day 3 | Data Science: Visualization | Introduction and Welcome Section 1: Introduction to Data Visualization and Distributions Section 2: Introduction to ggplot2 Drafting an article about R Basics |
| Day 4 | Section 3: Summarizing with dplyr Section 4: Gapminder Section 5: Data Visualization Principles Comprehensive Assessment and End of Course Survey Publishing the article about R Basics |
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| Day 5 | Data Science: Probability | Introduction and Welcome Section 1: Discrete Probability Section 2: Continuous Probability Drafting an article about Visualization |
| Day 6 | Section 3: Random Variables, Sampling Models, and the Central Limit Theorem Section 4: The Big Short Publishing the article about Visualization |
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| Day 7 | Data Science: Inference & Modeling | Introduction and Welcome Section 1: Parameters and Estimates Section 2: The Central Limit Theorem in Practice Section 3: Confidence Intervals and p-Values Section 4: Statistical Models Drafting an article Probability |
| Day 8 | Section 5: Bayesian Statistics Section 6: Election Forecasting Section 7: Association Tests Course Wrap-up and Comprehensive Assessment: Brexit Publishing the article aboutProbability |
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| Day 9 | Data Science: Productivity Tools | Introduction and Welcome Section 1: Installing Softwares Section 2: Basic Unix Section 3: Reproducible Reports Drafting an article about Inference and Modeling |
| Day 10 | Section 4: Git and GitHub Section 5: Advanced Unix Publishing the article about Inference and Modeling |
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| Day 11 | Data Science: Wrangling | Introduction and Welcome Section 1: Data Import Section 2: Tidy Data Drafting an article about Productivity Tools |
| Day 12 | Section 3: String Processing Section 4: Dates, Times, and Text Mining Comprehensive Assessment and Course Wrap-up Publishing the article about Productivity Tools |
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| Day 13 | Data Science: Linear Regression | Introduction and Welcome Section 1: Introduction to Regression Drafting an article about Wrangling |
| Day 14 | Section 2: Linear Models Section 3: Confounding Publishing the article about Wrangling |
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| Day 15 | Data Science: Machine Learning | Introduction and Welcome Section 1: Introduction to Machine Learning Section 2: Machine Learning Basic Drafting an article about Linear Regression |
| Day 16 | Section 3: Linear Regression for Prediction, Smoothing, and Working with Matrices Section 4: Distance, Knn, Cross-validation, and Generative Models Publishing the article about Linear Regression |
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| Day 17 | Section 5: Classification with More than Two Classes and the Caret Package Section 6: Model Fitting and Recommendation Systems Section 7: Final Assessment and Course Wrap-Up |
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| Day 18 | Data Science: Capstone | Introduction and Welcome Capstone Project: All Learners Drafting an article about Machine Learning |
| Day 19 | Capstone Project: IDV Learners Concluding Materials Publishing the article about Linear Regression |
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| Day 20 | Drafting and publishing an article about the Capstone project |