• Python Overview
• Python 2.7 vs Python 3
• Writing your First Python Program Lines and Indentation
• Python Identifiers
•  Various Operators and Operators Precedence
• Getting input from User, Comments, Multi line Comments.

• Working with Numbers, Booleans and Strings
• String types and formatting 
• String operations Simple if Statement, if-else Statement if-elif Statement.
• Introduction to while Loops.
•  Introduction to for Loops
• Using continue and break.
• Class hands-on: programs/coding exercise on string, loop and conditions in the classroom.

• List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries 
• Accessing Values, Basic Operations Indexing, Slicing, and Matrixes Built-in Functions & Methods.
• Exercises on List, Tuples And Dictionary
• Class hands-on: Program to convert tuple to dictionary Remove Duplicate from Lists
• Python program to reverse a tuple Program to add all elements in list.

• Introduction To Functions
• Why Defining Functions
• Calling Functions
• Functions With Multiple Arguments.
• Anonymous Functions
• Lambda Using Built-In Modules
• User-Defined Modules
• Module Namespaces
• Iterators And Generators.

• Opening and Closing Files
• open Function, file Object Attributes close() Method ,Read, write,seek. 
• Exception Handling, try-finally Clause Raising an Exceptions, User-Defined Exceptions.
• Regular Expression- Search and Replace Regular Expression Modifiers Regular Expression Patterns.

• Introduction to Numpy.
• Array Creation,Printing Arrays
• Basic Operation -Indexing, Slicing and Iterating
• Shape Manipulation - Changing shape,stacking and splitting of arrays.
• Vector stacking, Broadcasting with Numpy, Numpy for Statistical Operation. 
• Pandas : Introduction to Pandas Importing data into Python
• Pandas Data Frames, Indexing Data Frames
• ,Basic Operations With Data frame,Renaming Columns,Subletting and filtering a data frame.

• Introduction,plot(),Controlling Line
• Properties,Subplot with Functional Method, MUltiple Plot, Working with Multiple Figures,Histograms Seaborn :
• Intro to Seaborn And Visualizing statistical relationships
• Import and Prepare data
• Plotting with categorical data and Visualizing linear relationships
• Seaborn Exercise

• Basic understanding of linear algebra, Matrics, vectors.
• Addition and Multiplication of matrices.
• Fundamentals of Probability
• Probability distributed function and cumulative distribution function.
• Class Hand-on Problem solving using R for vector manipulation.
• Problem solving for probability assignments.

• The mean,median,mode, curtosis and skewness Computing Standard deviation and Variance.
• Types of distribution.
• Class Handson: 5 Point summary BoxPlot Histogram and Bar Chart Exploratory analytics R Methods.
• Inferential Statistics
• What is inferential statistics Different types of Sampling techniques Central Limit Theorem.
• Point estimate and Interval estimate.
• Creating confidence interval for population parameter Characteristics of Z-distribution and T- Distribution.
• Basics of Hypothesis Testing Type of test and rejection region Type of errors in Hypothesis resting.
• Type-l error and Type-ii errors P-Value and Z-Score Method T-Test, Analysis of variance(ANOVA) and Analysis of Covariance(ANCOVA) Regression analysis in ANOVA.
• Class Hands-on: Problem solving for C.L.T Problem solving Hypothesis Testing Problem solving for T-test, Z-score test Case study and model run for ANOVA.

• 
  Basics of Hypothesis Testing Type of test and Rejection Region
• Type o errors-Type 1 Errors,Type 2 Errors
• P value method,Z score Method. 
• The Chi-Square Test of Independence Regression
• Factorial Analysis of Variance
• Pearson Correlation Coefficients in Depth Statistical Significance, Effect Size, and Confidence Intervals

• Introduction to Data Cleaning
• Data Preprocessing What is Data Wrangling?
• How to Restructure the data? What is Data Integration?
• Data Transformation
• EDA : Finding and Dealing with Missing Values.
• What are Outliers? Using Z- scores to Find Outliers. 
• Introduction to Bivariate Analysis,Scatter Plots and Heatmaps.
•  Introduction to Multivariate Analysis

• Introduction To Machine Learning
• What is Machine Learning? 
• Introduction to Supervised and Unsupervised Learning
• Introduction to SKLEARN (Classification, Regression, Clustering, Dimensionality reduction, Model selection, Preprocessing)
• What is Reinforcement Learning?
• Machine Learning applications
• Difference between Machine Learning and Deep Learning

• Support Vector Machines Linear regression
• Logistic Regression Naive Bayes
• Linear discriminant analysis Decision tree
• k-nearest neighbor algorithm Neural Networks (Multilayer perceptron)
• Similarity learning

• Introduction to Linear Regression
• Linear Regression with Multiple Variables
• Disadvantage of Linear Models
• Interpretation of Model Outputs
• Understanding Covariance and Collinearity
• Understanding Heteroscedasticity
• Case Study – Application of Linear Regression for Housing Price Prediction

• 
  Introduction to Logistic Regression.
• Why Logistic Regression .
• Introduce the notion of classification
• Cost function for logistic regression
• Application of logistic regression to multi-class classification.
• Confusion Matrix, Odds Ratio And ROC Curve
• Advantages And Disadvantages of Logistic Regression.
• Case Study:To classify an email as spam or not spam using logisticRegression.

•  
• 
  Decision Tree – data set How to build a decision tree?
• Understanding Kart Model
• Classification Rules- Overfitting Problem
• Stopping Criteria And Pruning 
• How to Find the final size of Trees?
• Model A Decision Tree.
• Naive Bayes
• Random Forests and Support Vector Machines
• Interpretation of Model Outputs

• Hierarchical Clustering
• k-Means algorithm for clustering – groupings of unlabeled data points.
• Principal Component Analysis(PCA)- Data
• Independent components analysis(ICA) Anomaly Detection
• Recommender System-collaborative filtering algorithm
• Case Study– Recommendation Engine for e-commerce/retail chain

Basics of Time Series Analysis and Forecasting ,Method Selection in Forecasting

Moving Average (MA) Forecast Example,Different Components of Time Series Data ,Log Based Differencing, Linear Regression For Detrending