Bootcamp curriculum

    1. Introduction

    2. 1. What is Data Science?

    3. 2. Data Science Career Path

    4. 3. Skills required in Data Science

    5. 4. Where is Data Science Used?

    6. 5. Data Science Content & Interview Preparation

    1. Introduction

    2. 1. Random Variables

    3. 2. Mean, Variance, Standard Deviation

    4. 3. Covariance, Correlation

    5. 4. Probability Distribution Functions

    6. 5. Conditional Probability & Bayes Theorem

    7. 6. Introduction to Causal Analysis & Linear Regression

    8. 7. Hypothesis Testing & Statistical Significance

    9. 8. P-Values, Type I & Type II Errors, Confidence Intervals

    10. 9. Statistical Tests (Part 1)

    11. 10. Statistical Tests (Part 2)

    12. 11. Inferential Statistics (CLT & LLN)

    13. Fundamentals to Statistics: Quiz

    1. Introduction

    2. Lecture 1: Machine Learning Basics

    3. Lecture 2: Bias-Variance Trade-off

    4. Lecture 3: Overfitting & Regularization

    5. Lecture 4.1: Causal Analysis & Linear Regression (from Fundamentals to Statistics)

    6. Lecture 4.2: Linear Regression & Ordinary Least Squares (OLS)

    7. Lecture 5: Logistic Regression & Maximum Likelihood Estimation (MLE)

    8. Lecture 6: Linear Discriminant Analysis (LDA)

    9. Lecture 7: K-Nearest Neighbors (KNN)

    10. Lecture 8: Decision Trees

    11. Lecture 9: Bagging

    12. Lecture 10: Random Forest

    13. Lecture 11: Boosting (Part 1) Introduction

    14. Lecture 12: Boosting (Part 2) - AdaBoost

    15. Lecture 13: Boosting (Part 3) - Gradient Boosting Model (GBM)

    16. Lecture 14: Boosting (Part 4) - XGBoost

    17. Lecture 15: Clustering (Part 1) - K-Means & Elbow Method

    18. Lecture 16: Clustering (Part 2) - Hierarchical Clustering

    19. Lecture 17: Clustering (Part 3) - DBSCAN

    20. Lecture 18: Dimensionality Reduction (Part 1) - Feature Selection

    21. Lecture 19: Dimensionality Reduction (Part 2) - Principal Component Analysis

    22. Lecture 20: Resampling Techniques (Part 1) - Cross Validation

    23. Lecture 21: Resampling Techniques (Part 2) - Bootstrapping

    24. Lecture 22: Optimization Techniques - Grid-Search, GD, SGD, SGD-Momentum, Adam

    25. Fundamentals to Machine Learning Quiz

    1. Introduction to A/B Testing

    2. Lecture 1: A/B Testing Basics

    3. Lecture 2: Setting Hypothesis & Primary Metric

    4. Lecture 3: Crafting A/B Design

    5. Lecture 4: Running A/B Test

    6. Lecture 5: A/B Test Results Analysis (Part 1)

    7. Lecture 6: A/B Testing Pitfalls

    8. Extra (Theory): A/B Test Results Analysis (Part 2) - from Python for Data Science

    9. Extra (Coding): A/B Test Results Analysis (Part 3) - from Python for Data Science

    10. A/B Testing Quiz

    1. Introduction

    2. Lecture 1: Text Preprocessing in NLP

    3. Lecture 2: Tokenization

    4. Lecture 3: Bag-of-Words Representation

    5. Lecture 4: Word embeddings

    6. Lecture 5: Semantic Analysis

    7. Lecture 6: Term Frequency-Inverse Document Frequency (Tf-Idf)

    8. Lecture 7: Machine Learning & NLP

    9. Lecture 8: Recent Developments in NLP and AI

    10. Extra (Coding): Text Cleaning & Preparation from Python for Data Science

    11. Introduction to NLP Quiz

About this course

  • 121 lessons
  • 230 interview questions
  • 11 bonus E-books
  • 3 real world Case Studies

Ignite Your Career Path. We'll Fuel Your Success.

Tatev Karen Aslanyan (Instructor & Co-Founder) Vahe Karen Aslanyan (Co-Founder)