Course curriculum

  • 1
    Machine Learning Lifecycle
    • Course Introduction
    • Course Handouts
    • 6 Steps of Machine Learning Lifecycle
    • Introduction to Predictive Modeling
  • 2
    Problem statement and Hypothesis Generation
    • Defining the Problem statement
    • Introduction to Hypothesis Generation
    • Performing Hypothesis generation
    • Quiz: Performing Hypothesis generation
    • Reading: List of Hypothesis
    • Data Collection/Extraction
    • Quiz - Data Collection/Extraction
  • 3
    Importance of Stats and EDA
    • Introduction to Exploratory Data Analysis & Data Insights
    • Quiz - Introduction to Exploratory Data Analysis & Data Insights
    • Role of Statistics in EDA
    • Descriptive Statistics
    • Inferential Statistics
    • Quiz - Descriptive and Inferential Statistics
  • 4
    Understanding Data
    • Introduction to dataset
    • Quiz - Introduction to dataset
    • Reading data files into python
    • Quiz - Reading data files into python
    • Different Variable Datatypes
    • Variable Identification
    • Quiz - Variable Identification
  • 5
    Probability
    • Probability for Data Science
    • Quiz - Probability for Data Science
    • Basic Concepts of Probability
    • Quiz - Basic Concepts of Probability
    • Axioms of Probability
    • Quiz - Axioms of Probability
    • Conditional Probability
    • Quiz - Conditional Probability
  • 6
    Exploring Continuous Variable
    • Data range for continuous variables
    • Central Tendencies for continuous variables
    • Spread of the data
    • Central Tendencies and Spread of the data: Implementation
    • Quiz: Central Tendencies and Spread of data
    • KDE plots for continuous variable
    • KDE plots : Implementation
    • Overview of Distributions for Continuous Variables
    • Normal Distribution
    • Normality Check
    • Skewed Distribution
    • Skewness and Kurtosis
    • Distributions for continuous variable
    • Quiz: Distribution of Continuous variables
    • Approaching Univariate Analysis
    • Approaching Univariate Analysis: Numerical Variables
    • Quiz: Univariate analysis for Continuous variables
  • 7
    Exploring Categorical Variables
    • Central Tendencies for categorical variables
    • Understanding Discrete Distributions
    • Discrete Distributions Demonstration
    • Performing EDA on Catagorical Variables
    • Quiz: Univariate Analysis for Categorical Variables
  • 8
    Missing Values and Outliers
    • Dealing with Missing values
    • Understanding Outliers
    • Identifying Outliers in data
    • Identifying Outliers in data: Implementation
    • Quiz: Identifying Outliers in datasets
    • Quiz: Outlier treatment
  • 9
    Central Limit theorem
    • Important Terminologies
    • Central Limit Theorem
    • CLT: Implementation
    • Quiz: Central Limit Theorem
    • Confidence Interval and Margin of error
  • 10
    Bivariate analysis - Introduction
    • Introduction to Bivariate Analysis
  • 11
    Continuous - Continuous Variables
    • Covariance
    • Pearson Correlation
    • Spearman's Correlation & Kendall's Tau
    • Correlation versus Causation
    • Tabular and Graphical Methods
    • Performing Bivariate Analysis on Continuous - Continuous variables
    • Quiz: Continuous-Continuous Variables
  • 12
    Continuous Categorical
    • Tabular and Graphical Methods
    • Introduction to hypothesis Testing
    • P-Value
    • One Sample z-test
    • Two Sampled z-test
    • T-Test
    • T-Test vs Z-Test
    • Quiz: T tests
    • Performing Bivariate Analysis on Catagorical - Continuous variables
  • 13
    Categorical Categorical
    • Chi-Squared Test
    • Quiz: Chi squared tests
    • Bivariate Analysis for Categorical Categorical Variables
  • 14
    Multivariate Analysis
    • Multivariate Analysis
    • Multivariate Analysis Implementation
    • Project: EDA
    • Course Conclusion