Basic Concepts. Python Programing. Knowledge Transference
Data Curation, Preparation, Visualization, and Exploratory Data Analysis
Lec 1: Welcome, Introduction, & Housekeeping
Lecture slides: Overview & What is Data Science?
Google colab: Intro to Colab
Lec 2: Basic Programming in Python - Getting Started
Lec 3: Intro to Pandas, Reading & Writing Data
Lec 4: Subsetting & Selecting Rows & Columns. Data Visualization
Lec 5: Vectorized Operations. Summary Statistics
Lec 6: Textual Data
Lec 7: Data Visualization with Seaborn
Lec 8: Reshaping DataFrame: Pivot & Melt
Lec 9: Combining DataFrame: Concat & Merge
>>> First Project Submission Due
Data Ethics. Computational and Statistical Thinking. Mathmatical Foundations.
Lec 12: Defining Functions. Conditional Statement. Iteration
Lec 13: Simulation. Cumulative Distribution Function (CDF). Random Number Generation
Lec 14: Exploratory Data Analysis (EDA)
Lec 15: Statistical Inference. Probability and Uncertainty. Bernoulli Trials
Lecture slides
Lec 16: Statistical Inference - Discrete Variables. Binomial Distribution
>>> Second Project Team Presentation Upload Due
>>> Second Project Submission Due
Statistical Inference. Statistical Methods. Machine Learning
Lec 17: Statistical Inference - Continuous Variables. Normal Distribution. Parameter Estimation
>> # Bonus lecture: What is a Model?
Lec 18: Linear Regression by Least Squares
Lec 19: Bootstrap Replicates & Confidence Intervals
Lec 20: Pairs Bootstrap for Linear Regression
>>> Final Project Announcement
Lec 21: Permutation Hypothesis Testing
Lec 22: Bootstrap Hypothesis Testing
Lec 23: Randomized Experiments. A/B Testing for A Binary Outcome Variable
Lec 24: A/B Testing for A Continuous Outcome Variable
>> Upload Final Project Progress Report Presetation
Lec 25: Hypothesis Testing for Correlation
>> Bonus Lec: Advanced Topics #1: Statistical Analysis with SciPy
>> Bonus Lec: Advanced Topics#2. Machine Learning with Scikit-Learn
Lec 26: Review and Closure.