● Syntax and Semantics of Python programming
● Python History
● Versions of Python
● Features of Python
● Input and Output Functions
● Variable and Data Types
● What is a Variable
● Typecasting
● Data Types in Python
o Numeric
o String
o Boolean
o Compound
o List
o Tuple
o Set
o Frozen Set
o Dictionary
● Operators in Python
● Conditional Statements in Python
● Loops in Python
● User defined functions in python
● Strings in Python

● Creating arrays
● Array indexing
● Array slicing
● Numpy Data Types
● Copy vs View
● Array Shape
● Array reshape
● Iterating
● Join
● Search
● Filter
● Split
● Sort

● Pandas Series
● Pandas DataFrame
● Read CSV
● Read JSON
● Cleaning Data
● Missing Value Handling
● Optimizing Data Format
● Redundancy Minimization
● The corr() function
● Plotting Graphs in pandas

● Introduction to Data Analytics
● Brief history of Data Analytics
● What is Data Analytics?
● Why is Data Analytics important?
● What benefits does Data Analytics have?

● Text to Columns
● Concatenate
● Right with Concatenate
● Absolute Cell Reference
● Data Validation
● Time and Date Calculations
● Conditional Formatting
● Exploring Styles and clear formatting
● Using Conditional Formatting to hide details using the IF function
● pivot table
● pivot chart
● slicers
● creating charts

● MYSQL Server Basics
● Database models
● ER Model Overview
● Data types
● Understanding Test Database
● Basics Queries
● Removing Duplicates
● Data Filters Using Operators
● Data Sorting
● Grouping
● Joins
● Arithmetic and String functions
● Advanced Functions
● SET Operators
● Creating Complex Queries
● DML operations – Insert, Update & Delete
● TSQL

● Introduction to Power BI
● Report Visualization and Properties
● Chart and Map Report Properties
● Hierarchies and Drilldown Reports
● Power Query & M Language
● DAX EXPRESSIONS – Level 1
● DAX EXPRESSIONS – Level 2
● Power BI Cloud Operations
● Improving Power BI Reports
● Insights and Subscriptions
● Power BI Integration Elements

● High-Level overview of Data Science / Machine Learning project management methodology
● Random Variable and its definition
● Probability and Probability Distribution
● Balanced vs Imbalanced datasets
● Sampling Funnel, its application and its components
● Measure of central tendency
● Measure of Dispersion
● Expected value of probability distribution
● Measure of Skewness
● Measure of Kurtosis
● Various graphical techniques to understand data
● Installation of Python IDE
● Anaconda and Spyder
● Working with Python with some basic commands
● Normal Distribution
● Standard Normal Distribution / Z distribution
● Z scores and Z table
● QQ Plot / Quantile-Quantile plot
● Sampling Variation
● Central Limit Theorem
● Sample size calculator
● T-distribution / Student’s-t distribution
● Confidence interval

● Parametric vs Non-parametric tests
● Formulating a Hypothesis
● Choosing Null and Alternative hypothesis
● Type I and Type II errors
● Comparative study of sample proportions
● ANOVA
● 2 Proportion test
● Chi-Square test
● Non-Parametric test
● Non-Parametric test continued
● Hypothesis testing using Python

● Scatter Diagram
● Correlation Analysis
● Principles of Regression
● Introduction to Simple Linear Regression
● R shiny and Python Flask
● Multiple Linear Regression
● Scatter diagram
● Ordinary least squares
● Principles of regression
● Splitting the data into training, validation and testing datasets
● Understanding Overfitting vs Underfitting
● Generalization error and Regularization techniques
● Introduction to Simple Linear Regression
● Heteroscedasticity / Equal Variance
● LINE assumption
● Multiple Linear Regression
● Model Quality metrics
● Deletion diagnostics

● Principles of Logistic Regression
● Types of Logistic Regression
● Assumption and Steps in Logistic Regression
● Analysis of Simple Logistic Regression result
● Multiple Logistic Regression
● Confusion matrix
● Receiver operating characteristics curve (ROC curve)
● Lift charts and Gain charts
● Lasso and Ridge Regressions
● Logit and Log Likelihood
● Category Baselining
● Modeling Nominal categorical data

● Supervised vs Unsupervised learning
● Data Mining Process
● Measure of distance
● Types of Linkages
● Hierarchical Clustering / Agglomerative Clustering
● Non-clustering
● Why dimension reduction
● Advantages of PCA
● Calculation of PCA weights
● 2D Visualization using Principal components
● Basics of Matrix algebra
● SVD – Decomposition of matrix data
● Definition of a network (the LinkedIn analogy)
● Measure of Node strength in a Network
● Introduction to Google Page Ranking

● What is Market Basket / Affinity Analysis
● Measure of association
● Apriori Algorithm
● Sequential Pattern Mining
● User-based collaborative filtering
● Measure of distance / similarity between users
● Driver for recommendation
● Computation reduction techniques
● Search based methods / Item to item collaborative filtering
● SVD in recommendation
● Vulnerability of recommender systems

● Sources of data
● Bag of words
● Pre-processing, corpus DTM and TDM
● Word Clouds
● Corpus level word clouds
● Semantic network
● Clustering
● Extract Tweets from Twitter
● Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor
● Install Libraries from Shell
● Extraction and text analytics in Python

● Elements of Classification Tree – Root node, Child Node, Leaf Node, etc.
● Greedy algorithm
● Measure of Entropy
● Attribute selection using Information Gain
● Ensemble techniques
● Decision Tree C5.0 and understanding various arguments
● Random Forest and understanding various arguments
● Boosting / Bootstrap Aggregating
● AdaBoost / Adaptive Boosting
● Stacking
● Gradient Boosting
● Extreme Gradient Boosting (XGB)
● Artificial Neural Network
● Biological Neuron vs Artificial Neuron
● ANN structure
● Activation function
● Network Topology
● Support Vector Machines
● Classification Hyperplanes
● Best fit “boundary”
● Kernel Trick

● Introduction to time series data
● Steps of forecasting
● Components of time series data
● Scatter plot and Time Plot
● Lag Plot
● ACF – Auto-Correlation Function / Correlogram
● Visualization principles
● Naive forecast methods
● Errors in forecast and its metrics
● Model Based approaches
● Model-Based approaches
● AR (Auto-Regressive) model for errors
● Random walk
● ARMA (Auto-Regressive Moving Average), Order p and q
● ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
● Data-driven approach to forecasting
● Smoothing techniques
● De-seasoning and de-trending
● Econometric Models
● Forecasting Best Practices
● Forecasting using Python

1. Iris Flowers Classification
2. Loan Default Prediction
3. Real Estate Price Prediction
4. Stock Price Prediction
5. Titanic Survival Classification
6. Breast Cancer Classification
7. Music Genre Classification
8. Fake News Detection
9. Credit Card Fraud Detection
10. Movie Recommender System