Data Science Bootcamp: Your First Step as a Data Scientist

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the Course!

Lecture 2: Course Materials

Chapter 2: Setting up Environment – R and R Studio

Lecture 1: Installing R

Lecture 2: Installing R Studio

Chapter 3: Installing Libraries

Lecture 1: Installing Libraries

Lecture 2: Loading Libraries

Lecture 3: Let's start!

Chapter 4: Manipulating Data with Dplyr

Lecture 1: [Slides] – R Dplyr Library

Lecture 2: Intro to Dplyr and Tibble Data Structure

Lecture 3: Filter and Pipe Format

Lecture 4: Glimpse and Lists as Columns

Lecture 5: Function Encapsulation and Multiple Arguments

Lecture 6: Arrange and Mutate

Lecture 7: Select and Distinct

Lecture 8: Sample_N and Sample_Frac

Lecture 9: Summarize and Group By

Lecture 10: Joining Dataframes

Lecture 11: Small Typo

Lecture 12: [TUTORIAL] – Completing and Debugging Coding Exercises on Udemy Platform

Lecture 13: Practical Exercises – Time to test your skills on Dplyr in your Environment!

Chapter 5: Linear Regression

Lecture 1: [Slides] – Linear Regression – Intuition

Lecture 2: [Slides] – Linear Regression – Error Calculation and R Implementation

Lecture 3: Linear Regression – Introduction

Lecture 4: Loading the Data into R

Lecture 5: Plotting Feature (Age) and Target (Income) Variables

Lecture 6: Fitting a Random Line

Lecture 7: Adjusting the Weight of our Linear Model

Lecture 8: Training our First Linear Model

Lecture 9: Linear Regression Evaluation

Lecture 10: Linear Regression Closed Form Solution

Lecture 11: Gradient Descent Intuition – Part 1

Lecture 12: Gradient Descent Intuition – Part 2

Lecture 13: Visualizing Gradient Descent

Lecture 14: Multivariate Linear Regression

Lecture 15: Practical Exercises – Time to test your skills on Linear Regression!

Chapter 6: Classification Problems and Logistic Regression

Lecture 1: [Slides] – Classification Problems and Logistic Regression – Part 1

Lecture 2: [Slides] – Classification Problems and Logistic Regression – Part 2

Lecture 3: Classification Problems – Introduction

Lecture 4: Classification Problems Intuition – Why Linear Regression is unfit

Lecture 5: Calculating Sigmoid Function and Fitting a Logistic Regression

Lecture 6: Summary of Logistic Regression and Accuracy

Lecture 7: Log-Loss Function Intuition

Lecture 8: Gradient Descent Intuition – Classification

Lecture 9: Visualizing Log-Loss in 3 Dimensions

Lecture 10: Practical Exercises – Time to test your skills on Classification Problems!

Chapter 7: Model Evaluation and Selection

Lecture 1: [Slides] – Model Evaluation and Selection

Lecture 2: Model Evaluation and Selection – Introduction

Lecture 3: Example of a High Bias Model

Lecture 4: Example of a High Variance Model

Lecture 5: Evaluating the Model on Unseen Data

Lecture 6: Randomized Train and Test Split

Lecture 7: Performance across Training and Test Data

Lecture 8: Regression Metrics – Plotting the Residuals

Lecture 9: Regression Metrics – MSE, MAE and RMSE

Lecture 10: Regression Metrics – R-Square Breakdown and MAPE

Lecture 11: Classification Metrics – Fitting Logistic Regression and Confusion Matrix Intro

Lecture 12: Classification Metrics – TP, FP, TN, FN

Lecture 13: Classification Metrics – Precision, Recall and F-Score

Lecture 14: Classification Metrics – Building ROC Curve

Lecture 15: Classification Metrics – ROCR Package and Area Under the Curve

Lecture 16: Practical Exercises – Time to test your skills on Model Evaluation and Selection

Chapter 8: Tree Based Models – Decision Trees

Lecture 1: [Slides] – Decision Trees Intuition – Part 1

Lecture 2: [Slides] – Decision Trees Intuition – Part 2

Lecture 3: Classification Trees – Problem Evaluation and Fitting a Logistic Regression

Lecture 4: Classification Trees – First Split and Gini Impurity Concept

Lecture 5: Classification Trees – Finding the Best Split with Minimum Gini Impurity

Lecture 6: Classification Trees – Fitting a Decision Tree using RPart

Lecture 7: Classification Trees – Adding more Thresholds and Visualizing Classification

Lecture 8: Classification Trees – Tweaking Hyperparameters and Checking Accuracy

Lecture 9: Regression Trees – Intuition

Lecture 10: Regression Trees – Calculating Residual Sum of Squares

Lecture 11: Regression Trees – Finding the Best Split with Residual Sum of Squares

Lecture 12: Regression Trees – Fitting the Algorithm

Lecture 13: Regression Trees – Comparing between Tree and Linear Model

Lecture 14: Practical Exercises – Time to test your Skills on Decision Trees

Chapter 9: Tree Based Models – Random Forests

Lecture 1: [Slides] – Random Forests Intuition

Lecture 2: Random Forest Intuition and Subsetting Data

Lecture 3: Fitting Different Decision Trees

Lecture 4: Building a Random Forest from Scratch with Three Estimators

Lecture 5: Measuring the Accuracy of Each Trees and of the Ensemble Average