Course curriculum
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1
Class Recordings
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Class 1 - Applied Statistics P1
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Class 2 - Applied Statistics P2
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Class 3 - Data Science Coding P1
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Class 4 - Data Science Coding P2
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Class 5 - Product SQL
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Class 6 - Product SQL P2
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Class 7 - Machine Learning
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Class 8 - Machine Learning P2
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Class 9 - Behavioral & Leadership
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Class 10 - Additional Practice Cases
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Class 11 - AB Testing P1
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Class 12 - AB Testing P2
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Class 13 - Advanced AB testing P1
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Class 14 - Advanced AB testing P2
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2
Applied Statistics
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Univariate Statistics
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Simpson's Paradox
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Common Probability Distributions
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The Bayes Theorem
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Uniform Distribution
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Binomial Distribution
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Geometric Distribution
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Normal Distribution
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The Central Limit Theorem
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Hypothesis Testing
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Z-Test and T-Test
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Chi-Squared Test
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ANOVA Tests
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Confidence Interval
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Linear Regression
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3
Data Science Coding
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Intro to Algorithms & Data Structures
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Python Data Structures
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Big-O Notation
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Sorting Algorithms
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Pandas Operations
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4
Class Recordings (Archive)
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Class 1 - Applied Statistics P1
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Class 2 - Applied Statistics P2
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Class 3 - DS Coding P1 | Algos & Data Structures + Data Manipulation
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Class 3 - DS Coding P1B | Algos & Data Structures + Data Manipulation
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Class 4 - DS Coding P2 | Statistical and ML Coding
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Class 5 - SQL P1
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Class 6 - SQL P2
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Class 7 - ML P1 | ML Breath + Depth | ML Case Study (Colab)
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Class 8 - ML P2 | ML Deployment + ML Depth | ML Cases
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Class 9 - Interview Ready P1 | Behavioral + Leadership
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Class 10 - Interview Ready P2 | Getting Hired + Resume Tips + Practice Questions
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Class 11 - Product Sense - Defining Metrics + Investigation
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Class 12 - Opportunity Sizing + Feature Improvement
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Class 13 - AB Testing P1
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Class 14 - AB Testing P2
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Class 15 - Advanced AB Testing
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Class 16 - Causal Inference
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