• What is Python?
• Why does Data Science require Python?
• Installation of Anaconda
• Understanding Jupyter Notebook
• Basic commands in Jupyter Notebook
• Understanding Python Syntax

Data Types and Data Structures

• Variables and Strings
• Lists, Sets, Tuples, and Dictionaries

Control Flow and Conditional Statements

• Conditional Operators, Arithmetic Operators, and Logical Operators
• If, Elif and Else Statements
• While Loops
• For Loops
• Nested Loops and List and Dictionary Comprehensions

Functions

• What is function and types of functions
• Code optimization and argument functions
• Scope
• Lambda Functions
• Map, Filter, and Reduce

File Handling

• Create, Read, Write files and Operations in File Handling
• Errors and Exception Handling

Class and Objects

• Create a class
• Create an object
• The __init__()
• Modifying Objects
• Object Methods
• Self
• Modify the Object Properties
• Delete Object
• Pass Statements

• Introduction to Array
• Creation and Printing of an array
• Basic Operations in Numpy
• Indexing
• Mathematical Functions of Numpy

2. Data Manipulation with Pandas

• Series and DataFrames
• Data Importing and Exporting through Excel, CSV Files
• Data Understanding Operations
• Indexing and slicing and More filtering with Conditional Slicing
• Group by, Pivot table, and Cross Tab
• Concatenating and Merging Joining
• Descriptive Statistics
• Removing Duplicates
• String Manipulation
• Missing Data Handling

DATA VISUALIZATION

Data Visualization using Matplotlib and Pandas

• Introduction to Matplotlib
• Basic Plotting
• Properties of plotting
• About Subplots
• Line plots
• Pie chart and Bar Graph
• Histograms
• Box and Violin Plots
• Scatterplot

Case Study on Exploratory Data Analysis (EDA) and Visualizations

• What is EDA?
• Uni – Variate Analysis
• Bi-Variate Analysis
• More on Seaborn based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots.

UNSTRUCTURED DATA PROCESSING

Regular Expressions

• Structured Data and Unstructured Data
• Literals and Meta Characters
• How to Regular Expressions using Pandas?
• Inbuilt Methods
• Pattern Matching

PROJECT ON WEB SCRAPING: DATA MINING and EXPLORATORY DATA ANALYSIS

• Data Mining (WEB – SCRAPING)
  This project starts completely from scratch which involves the collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models. This project covers the main four steps of the Data Science Life Cycle which involves.
  • Data Collection
  • Data Mining
  • Data Preprocessing
  • Data Visualization
    Ex: Text, CSV, TSV, Excel Files, Matrices, Images

Data Types and Data Structures

• Statistics in Data science:
• What is Statistics?
• How is Statistics used in Data Science?
• Population and Sample
• Parameter and Statistic
• Variable and its types

Data Gathering Techniques

• Data types
• Data Collection Techniques
• Sampling Techniques:
• Convenience Sampling, Simple Random Sampling
• Stratified Sampling, Systematic Sampling, and Cluster Sampling

Descriptive Statistics

• What is Univariate and Bi Variate Analysis?
• Measures of Central Tendencies
• Measures of Dispersion
• Skewness and Kurtosis
• Box Plots and Outliers detection
• Covariance and Correlation

Probability Distribution

• Probability and Limitations
• Discrete Probability Distributions
• Bernoulli, Binomial Distribution, Poisson Distribution
• Continuous Probability Distributions
• Normal Distribution, Standard Normal Distribution

Inferential Statistics

• Sampling variability and Central Limit Theorem
• Confidence Intervals
• Hypothesis Testing
• Z-test, T-test
• Chi-Square Test
• F-Test and ANOVA

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Introduction to Databases

• Basics of SQL
  • DML, DDL, DCL, and Data Types
  • Common SQL commands using SELECT, FROM, and WHERE
  • Logical Operators in SQL
• SQL Joins
  • INNER and OUTER joins to combine data from multiple tables
  • RIGHT, LEFT joins to combine data from multiple tables
• Filtering and Sorting
  • Advanced filtering using IN, OR, and NOT
  • Sorting with GROUP BY and ORDER BY
• SQL Aggregations
  • Common Aggregations including COUNT, SUM, MIN, and MAX
  • CASE and DATE functions as well as work with NULL values
• Subqueries and Temp Tables
  • Subqueries to run multiple queries together
  • Temp tables to access a table with more than one query
• SQL Data Cleaning
  • Perform Data Cleaning using SQL

INTRODUCTION TO MACHINE LEARNING

• What Is Machine Learning?

• Supervised Versus Unsupervised Learning

• Regression Versus Classification Problems

• Assessing Model Accuracy

• Why Unsupervised Learning?

• How it Differs from Supervised Learning

• The Challenges of Unsupervised Learning

SUPERVISED LEARNING

Regression Techniques

• Linear Regression

  • Simple Linear Regression:

    • Estimating the Coefficients

    • Assessing the Coefficient Estimates

    • R Squared and Adjusted R Squared

    • MSE and RMSE

  • Multiple Linear Regression:

    • Estimating the Regression Coefficients

    • OLS Assumptions

    • Multicollinearity

    • Feature Selection

    • Gradient Descent

    • Evaluating the Metrics of Regression Techniques

• Error Analysis & Model Validation

  • Homoscedasticity and Heteroscedasticity of error terms

  • Residual Analysis

  • Q-Q Plot

  • Cook’s Distance and Shapiro-Wilk Test

  • Identifying the line of best fit

• Other Considerations in the Regression Model

  • Qualitative Predictors

  • Interaction Terms

  • Non-linear Transformations of the Predictors

• Polynomial Regression

  • Why Polynomial Regression

  • Creating polynomial linear regression

  • Evaluating the metrics

• Regularization Techniques

  • Lasso Regularization

  • Ridge Regularization

  • ElasticNet Regularization

• Case Study: Linear, Multiple Linear Regression, Polynomial Regression using Python

Classification Techniques

• Logistic Regression

  • An Overview of Classification

  • Difference Between Regression and Classification Models

  • Why Not Linear Regression?

  • Logistic Regression:

    • The Logistic Model

    • Estimating the Regression Coefficients and Making Predictions

    • Logit and Sigmoid functions

    • Setting the threshold and understanding decision boundary

    • Logistic Regression for >2 Response Classes

  • Evaluation Metrics for Classification Models:

    • Confusion Matrix

    • Accuracy and Error Rate

    • TPR and FPR

    • Precision and Recall, F1 Score

    • AUC-ROC

    • Kappa Score

• Naive Bayes

  • Principle of Naive Bayes Classifier

  • Bayes Theorem

  • Terminology in Naive Bayes

  • Posterior Probability

  • Prior Probability of Class

  • Likelihood

  • Types of Naive Bayes Classifier:

    • Multinomial Naive Bayes

    • Bernoulli Naive Bayes

    • Gaussian Naive Bayes

• Tree-Based Models

  • Decision Trees (Rule-Based Learning):

    • Basic Terminology in Decision Tree

    • Root Node and Terminal Node

    • Regression Trees and Classification Trees

    • Trees Versus Linear Models

    • Advantages and Disadvantages of Trees

    • Gini Index

    • Overfitting and Pruning

    • Stopping Criteria

    • Accuracy Estimation using Decision Trees

  • Case Study: Decision Tree using Python

• Resampling Methods

  • Cross-Validation

  • The Validation Set Approach

  • Leave-One-Out Cross-Validation

  • K-Fold Cross-Validation

  • Bias-Variance Trade-Off for K-Fold Cross-Validation

• Ensemble Methods in Tree-Based Models

  • What is Ensemble Learning?

  • What is Bootstrap Aggregation (Bagging) and how does it work?

  • Random Forest

    • What is it and how does it work?

    • Variable selection using Random Forest

  • Boosting: AdaBoost, Gradient Boosting

    • What is it and how does it work?

    • Hyperparameters, Pros and Cons

  • Case Study: Ensemble Methods – Random Forest Techniques using Python

• Distance-Based Models

  • K-Nearest Neighbors

    • K-Nearest Neighbor Algorithm

    • Eager Vs Lazy learners

    • How does the KNN algorithm work?

    • How do you decide the number of neighbors in KNN?

    • Curse of Dimensionality

    • Pros and Cons of KNN

    • How to improve KNN performance

    • Case Study: KNN using Python

  • Support Vector Machines

    • The Maximal Margin Classifier

    • HyperPlane

    • Support Vector Classifiers and Support Vector Machines

    • Hard and Soft Margin Classification

    • Classification with Non-linear Decision Boundaries

    • Kernel Trick (Polynomial and Radial)

    • Tuning Hyperparameters for SVM:

      • Gamma, Cost, and Epsilon

    • SVMs with More than Two Classes

    • Case Study: SVM using Python

UNSUPERVISED LEARNING

Principal Components Analysis (PCA)

• Introduction to Dimensionality Reduction and its necessity

• What Are Principal Components?

• Demonstration of 2D PCA and 3D PCA

• Eigen Values, EigenVectors, and Orthogonality

• Transforming Eigenvalues into a new dataset

• Proportion of variance explained in PCA

• Case Study: PCA using Python

Clustering

• K-Means Clustering

  • Centroids and Medoids

  • Deciding the optimal value of ‘K’ using Elbow Method

  • Linkage Methods

• Hierarchical Clustering

  • Divisive and Agglomerative Clustering

  • Dendrograms and their interpretation

• Applications of Clustering

• Practical Issues in Clustering

• Case Study: Clustering using Python

RECOMMENDATION SYSTEMS

• What are recommendation engines?

• How does a recommendation engine work?

• Data Collection

• Data Storage

• Filtering the Data

• Content-based Filtering

• Collaborative Filtering

• Cold Start Problem

• Matrix Factorization

• Building a recommendation engine using matrix factorization

• Case Study: Recommendation Systems

CAPSTONE PROJECTS

• Regression Capstone Project: Use case covering Data Understanding, EDA, Data Processing, and Regression Techniques

• Classification Capstone Project: Use case covering Data Understanding, EDA, Data Processing, and Classification Techniques

• Unsupervised Learning Capstone Project: Use case covering Data Understanding, EDA, Data Processing, and Clustering/Dimensionality Reduction techniques