• What is Data Science?
• What is Machine Learning ? 
• What is Artificial Intelligence? 
• Role of Python in Data Science
• What is Deep Learning?
• SAS and Data Science
• Data Analytics and its types

• Basics Statistics:
• Descriptive statistics and inferential statistics
• Measure of central tendency -Mean, Median and Mode
• Measure of Dispersion-Range, Variance, standard deviation and coefficient of variation
• Frequency distribution
• Introduction to Probability
• Practice Session & Assignments

• What is Python?
• Role of Python in Data Science
• Installing Python
• Python IDEs
• Jupyter Notebook Overview
• Impementation of Advance Python techniques in Data Science

• What is Python & History?
• Installing Python & Python Environment
• Basic commands in Python
• Data Types & Operators
• Data Structures in python- List, tuples, dictionary and sets
• Python packages – math, Numpy, Pandas, Matplotlib, seaborn, scikit learn Loops- for loop do while
• User Defined Functions

• Data importing
• Working with datasets
• Manipulating the data sets
• Subset the data
• Sort the data
• Creating new variables
• Bins creation
• Identifying & removing duplicates
• Exporting the datasets into external files
• Data Merging
• Pivot table analysis
• Data visualization through matplotlib, seaborn
• Histogram
• Bar Plot
• Pie Chart
• Scatter Matrix Pandas
• Scatter matrix Violin
• Plots
• Line Graphs

• Taking a random sample from data
• Descriptive statistics
• Central Tendency
• Variance
• Quartiles
• Percentiles
• Box Plots
• Graphs
• Visualization case study with poke man data

•  Discreate Distribution
  • Bi-nominal distribution
  • Poisson distribution
  • Multinomial distribution
• Continuous distribution:
  • Normal distribution
  • T-student distribution
  • Exponential distribution
  • Chi- square distribution
  • F- distribution
  • Anova

1.      Random sampling:

• Sample with replacement
• Sample without replacement
• Training, testing and hold out dataset

2.       Stratified sampling

3. Sequential or systematical sampling

4. Clustering sampling techniques

• • What is Hypothesis testing
  • Need of hypothesis testing
  • Null hypothesis testing
  • Alternative hypothesis testing
  • Use case to solve the hypothesis testing

• Data sanity checks
• Anomalies detection
• Missing Value detections & treatments

• Project on Data handling
• Data exploration
• Data validation
• Missing values identification
• Outliers Identification
• Data Cleaning
• Basic Descriptive statistics
• EDA analysis
• Generating the insights
•  

• Correlation
  • Pearson correlation
  • Rank Correlation
• VIF/Multi collinearity
• PCA
• Chi-Square Technique
• Information value
• Cluster based method
• Tree based method
• Lasso regression method
• Stepwise regression method

• Introduction to Machine Learning
• Supervised Learning 
• Un-supervised Learning

• Supervised learning -Regression

  • Linear Regression
  • Multiple linear Regression
  • Rigid Regression
  • Lasso Regression
  • Elastic Net Regression
  • Polynomial Regression
  • Time series Analysis :
  
  
  • Need of time series
  • Moving average method
  • Holt-winter method
  • ARIMA method
  • Model Evolution metrics
  • Use case with Regression models-Project and Assignments

  Supervised Learning -Classification

  • Logistic Regression
  • Decision Tree
  • Decision Tree Regressors
  • Decision Tree Classifier
  • Naive Bayes
  • KNN
  • KNN-Regressors
  • KNN-Classifiers- Binary labels and multi labels
  • Support Vector Machines
  • Support vectors-Regressors
  • Support vectors-Classifiers
  • Ensemble learning
  • Bagging
  • Boosting
  • Random Forest
  • Random Forest -Regressor
  • Random Forest-Classifier
  • Extra Tree Network
  • Model Elevation metrics

• Clustering Analysis
• Hierarchical Clustering
• Agglomerative Clustering
• Non-Hierarchical Clustering K-Means

• • How to validate a model?
  • What is a best model?
  • Types of data
  • Types of errors
  • The problem of over fitting
  • The problem of under fitting
  • Bias Variance Tradeoff
  • Cross Validation
  • Boot Strapping

• • Neural Networks Introduction
  • Neural Network Intuition
  • Neural Network and vocabulary
  • Neural Network algorithm
  • Math behind Neural Network algorithm
  • Building the Neural Networks
  • Validating the Neural network model
  • Neural Network applications
  • Image recognition using Neural Networks

• • What is Text mining
  • Corpus
  • Tokenizer
  • POS
  • Named Entry recognizers
  • Lemmatization
  • NLTK
  • Text cleaning
  • Words Cleaning
  • Stop words
  • Cleaning Twitter Data
  • Sentimental Analysis
  • Text blob
  • Word2Vec
  • Spelling correction
  • TFIDF
  • Use Case with Text mining Analysis

1.      Overview of Deep Learning by using keras and Tensor flow

2. Tensor flow
   • Introduction to Tensor flow
   • Constant
   • Place holders
   • Variables

3.      Multi layers Neural Networks

• Neurons
• Weights
• Activations
• Networks of Neurons
• Training Networks
• Back propagation
• Gradient Descent

4. CNN

• Feature learning

1. 1. Convolution
   2. Pooling

• Classification learning
• Flatten
• Fully Connected
• SoftMax

    5. DNN

   6. Digit Recognizer Classification

• Introduction to SAS
• Base SAS environment:
• Interactive Vs Batch Mode
• Elements of SAS Software Interface
• SAS Program Editor
• Output Window
• Log Window
• Other – Explorer and Result Window
• Components on Base SAS
• Data Management Facility
• Programming Language
• Data Analysis & Reporting Facility
• SAS Data Libraries

• • Managing SAS Data Libraries
  • SAS Variable Values and Names
  • SAS Date Values

Missing Date Values

• Reading, Writing and Sub-setting Data
• SAS Functions
• Mathematical functions
• String Functions
• Date Functions
• Format conversion functions
• Random number generators
  • Data Import & Export
  • Data Validation
  • Data Visualization
  • SQL Query
  • Sorting
  • Data Step merge
  • SQL merge
  • Conditional Programming
  • Transpose
  • Do loop and Array
  • SAS Macros
  • SAS Modeling Procedures

• • Project on Data handling
  • Data exploration
  • Data validation
  • Missing values identification
  • Outliers identification
  • Data Cleaning
  • Basic Descriptive statistics
  • EDA analysis
  • Generating the insights
  • Presentation the insights

• Business understanding-Credit cards and Telecom
• Data requirement
• Data cleaning
• EDA and insight generation
• Variable creation
• Variable reduction
• Model Building
• Validation Building
• Recommendation to clients