Bootcamp curriculum

    1. 1. What is Data Science?

    2. 2. Data Science Career Path

    3. 3. Skills required in Data Science

    4. 4. Where is Data Science Used?

    5. 5. Data Science Content & Interview Preparation

    1. Fundamentals to Statistics Course Preview

    2. Fundamentals to Machine Learning Course Preview

    3. A/B Testing Course Preview

    4. Introduction to NLP Course Preview

    5. Python for Data Science Course Preview

    6. Applied Data Science Course Preview

    7. Interview Preparation Preview

    8. Free Resources Preview

    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 (Part 1)

    6. Lecture 4.2: Linear Regression & Ordinary Least Squares (OLS) (Part 2)

    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 Test Results Analysis (Part 2) Optional

    8. Lecture 7: A/B Test Results Analysis Coding in Python (Part 3)

    9. Lecture 8: A/B Testing Pitfalls

    10. A/B Testing Quiz

About this course

  • $149.97 / month with 7 day free trial
  • 147 lessons
  • 50 quizzes
  • 15 bonus E-books

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

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