INTRODUCTION TO STATISTICS
Basic business statistics	How businesses use statistics |The basic vocabulary of statistics |The types of data used in business
Organizing and Visualizing Data	The sources of data used in business | To construct tables and charts for numerical data | To construct tables and charts for categorical data | The principles of properly presenting graphs
Numerical Descriptive Measures	To describe the properties of central tendency, variation, and shape in numerical data | To construct and interpret a boxplot | To compute descriptive summary measures for a population | To compute the covariance and the coefficient of correlation

 

Basic Probability and Terms	Basic probability concepts | Conditional probability | To use Bayes’ Theorem to revise probabilities | Various counting rules
Probability Distributions	Types of Distributions like Discrete and Continuous | Functions of Random Variables | Probability Distribution Graphs
Sampling and Sampling Distributions	To distinguish between different sampling methods | The concept of the sampling distribution | To compute probabilities related to the sample mean and the sample proportion | The importance of the Central Limit Theorem
Confidence Interval Estimation	To construct and interpret confidence interval estimates for the mean and the proportion | How to determine the sample size necessary to develop a confidence interval estimate for the mean or proportion | How to use confidence interval estimates in auditing
Fundamentals of Hypothesis Testing	Null and Alternate hypothesis | One sample Tests | Test statistic and critical values | Possible errors in testing | p-value approach | t and z-tests | Testing on proportions

 

DATA TRANSFORMATION AND VISUALIZATION

 

Data Transformations	Merge, Rollup, Transpose and Append | Smoothing | Aggregation | Normalization | Attribute construction
Feature Engineering	Missing value Imputation | Outlier Analysis and Treatment | Binning | Creating dummy variables | feature scaling | Extracting Date | Log Transformation | Feature split | Label Encoding | One-Hot Encoding
Exploratory Data Analysis	Summarizing and Visualizing the Important Characteristics of Data | Visualization techniques – Box plot | Histogram | Multi-variate chart | Pareto chart | Scatter plot | Stem-and-leaf plot.

 

PYTHON PROGRAMMING

 

 

 

 

Introduction to Python	Installation | Python Basics | Spyder IDE | Jupyter Notebook | Floats and Strings Simple Input & Output | Variables | Operators | Single and Multiline Comments | Taking input from user
Data Structures	List | Strings | Tuple | Dictionary | Sets and their examples
Conditional Statements	If | if-else | if-elif-else | nested if else
Loops	For | while | nested loops| loop control statements
Functions	Creating user defined Functions | Function arguments like-Required, Keyword, Default and variable-length | Scope of variables in creating functions | Anonymous Functions – Lambda
Exception and File handling	Exception Handling | Raising Exceptions | Assertions | Files I/O

 

 

 

 

	INTRODUCTION TO MACHINE LEARNING
Introduction	What is Machine Learning? | End-to-end Process of Investigating Data Through a Machine Learning Lens | Evolution and Trends | Application of Machine Learning | Best Practices of Machine Learning
Machine Learning Methods	Supervised | Unsupervised
Machine Learning Algorithms	Classification |  Regression | Time Series | Collaborative Filtering | Clustering | Principal Component Analysis

 

 

 

	MACHINE LEARNING WITH PYTHON
Numpy module	Basics of numpy | creating arrays | Numpy functions
Pandas	Introduction to Pandas | IO Tools | Pandas – Series and Dataframe and their functions
Matplotlib and Seaborn	Graphical representation of data using various plots
Scikit learn	Introduction to SciKit Learn | Load Data into Scikit Learn | Run Machine Learning Algorithms Both for Unsupervised and Supervised Data | Supervised Methods: Classification & Regression | Unsupervised Methods: Clustering, Gaussian Mixture Models | Decide What’s the Best Model for Every Scenario
	MACHINE LEARNING APPLICATIONS
Linear Regression	Implementing Simple & Multiple Linear Regression | Making Sense of Result Parameters | Model Validation | Handling Other Issues/Assumptions in Linear Regression: Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity | Prediction and Confidence Intervals | Use Cases
Logistic Regression	Implementing Logistic Regression| Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test | Goodness of Fit Measures | Model Validation: Cross Validation, ROC Curve, Confusion Matrix | Use Cases
Decision Tree	Implementing Decision Trees | Homogeneity | Entropy Information Gain | Gini Index | Standard Deviation Reduction | Vizualizing & Prunning a Tree | Implementing Random Forests using Python | Random Forest Algorithm | Important hyper-parameters of Random Forest for tuning the model | Variable Importance | Out of Bag Errors

 

Naïve Bayes	Bayes Theorem | Gaussian Naïve Bayes & its implementation | Multinomial Naïve Bayes, Count vectorizer, TF-IDF Vectorizer and its use cases on Text Classification
K-Nearest Neighbors	Concept of nearest neighbors | Euclidean Distance | Use Cases of KNN Classifier & Regressor
Support Vector Machine	Introduction to support vectors | Concept of hard & soft margins | slack variable | Lagrangian Primal & Dual | Kernel Trick | Use Cases
Time Series	Introduction | Components of Time Series | Stationarity | ACF | PACF | ARIMA model for forecasting | Use Cases
K-Means Clustering	Clustering concept | Finding optimal number of clusters | Use Cases
Hierarchical Clustering	Agglomerative & Divisive Clustering | Dendrograms | Linkage Matrix like Single, Complete & Average | Use Cases
Ensemble Techniques	Bagging | Boosting | Stacking | Regularization | Different cross-validation techniques used to treat Over fitting and Under fitting in machine learning models
Principal Component Analysis	Concept of Dimensionality reduction | Eigen values | Eigen Vectors | Use Cases

 

 

 

	PROJECTS
PROJECT 1	Title: Real Estate Price Prediction using Linear Regression Industry: Real Estate Description: The goal of this Use-case is to make property price predictions using Real Estate data. The dataset contains the of the price of apartments and various characteristics of the property. Based on this data, decide on the price of new properties.
PROJECT 2	Title: Loan Prediction using Logistic Regression & Decision Tree Industry: Finance Description: The goal is to build a classification model to predict if a loan is approved or not. Dataset contains demographic information like age, income etc. of various customers. Based on this, predict for a new customer whether loan will be approved or not
PROJECT 3	Title: Student’s Performance Prediction using KNN & Naïve Bayes Industry: Education Description: The goal is to build a classification model to predict student’s performance from various characteristics associated with student’s academics
PROJECT 4	Title: Handwritten digit recognition using Decision Tree &Random forest Description: Dataset contains various pixel values of handwritten digits between 0 to 9. The goal is to build a classification model to predict the digit from its pixel values.
PROJECT 5	Title: Amazon fine food review-Sentiment Analysis Industry: Amazon Description: The analysis is to study Amazon food review from customers and try to predict whether a review is positive or negative. The dataset contains more than 500k reviews with number of upvotes and total votes to those comments

 

 

DEEP LEARNING APPLICATIONS

 

 

 

 

Neural Networks	Understanding Neural Networks  |  The Biological Inspiration  |  Perceptron Learning & Binary Classification  | Backpropagation Learning  |  Learning Feature Vectors for Words  |  Object Recognition
Keras	Keras for Classification and Regression in Typical Data Science Problems  |  Setting up KERAS  |  Di erent Layers in KERAS  |  Creating a Neural Network Training Models and Monitoring  |  Artificial Neural Networks
Tensorflow	Introducing Tensorflow  |  Neural Networks using Tensorflow  |  Debugging and Monitoring  |  Convolutional Neural Networks
Recurrent Neural Networks	Introduction to RNN | RNN long & short term dependencies | Vanishing gradient problem | Basic LSTM | Step by step walk through LSTM | Use cases
PROJECT 1 ANN ON KERAS	Title: Credit Default using ANN on Keras Industry: Finance Description: This research aimed at the case of customers’  default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification – credible or not credible clients.
PROJECT 2 CNN ON KERAS & TENSORFLOW	Title: Handwriting/Facial recognition using CNN on TensorFlow & Keras Industry: Pattern Recognition Description: This project will help build a model using Convolutional Neural Network to recognize handwriting/faces
PROJECT 3 LSTM	Title: Google stock price prediction Industry: Finance Description: Dataset contains dates, volume, open, close, high and low prices of stocks. Based on this build a LSTM model to predict current and future stock price

 

 

 

Computer Vision with Python:

• Introduction to OpenCV
• Core Operations
• Image Processing in OpenCV
• Feature Detection and Description
• Video Analysis
• Machine Learning
• Object Detection
• OpenCV-Python Bindings

 

NLP with Python:

• Introduction to NLTK
• Tokenizing words and Sentences with NLTK
• Stop words and Stemming with NLTK
• Part of Speech Tagging with NLTK
• Chunking & Chinking
• Lemmatizing with NLTK
• Wordnet with NLTK
• Converting words to Features
• Text Classification with NLTK
• Combining Algorithms with NLTK
• Creating a module for Sentiment Analysis
• Twitter Sentiment Analysis with NLTK
• Named Entity Recognition with Stanford NER Triggers
• Testing NLTK and Stanford NER Triggers for Accuracy and Speed

 

R-Programming

• Over view and basic syntax of R
• R variables and operators
• R Objects
  • Vector

• List

• Matrix

• Array

• Factor

• Data Frame

• Conditional statements of R
  • If

• Else-if

• If -else if-else

• Nested if-else

• Loops in R
  • Repeat loop

• While loop

• For loop

• Loop control statements
• Creating user defined functions in R
• Data reshaping and File handling
  • Reading excel, csv, xml files in R
• Reading Web data in R
• Connecting to Data bases in R
• R-Packages
• Data manipulation with dplyr package(Case studies on data manipulation with real time datasets)
• Data visualization with ggplot package & case studies
• Creating Interactive Dash Boards with R markdown and Shiny

 

SQL (Structured Query Language)

• Introduction to SQL
• SQL Select Statements
• Execute a basic SELECT statement

• Restricting and Sorting Data

• Limit the rows retrieved by a query

• Sort the rows retrieved by a query
• Single-Row Functions
• Describe various types of functions available in SQL
• Use character, number, and date functions in SELECT statements

• Describe the use of conversion functions
• Displaying Data from Multiple Tables
• Write SELECT statements to access data from more than one table using equality and nonequality joins

• View data that generally does not meet a join condition by using outer joins

• Join a table to itself by using a self join

• Aggregating Data Using Group Functions
  • Identify the available group functions

• Describe the use of group functions

• Group data using the GROUP BY clause

• Include or exclude grouped rows by using the HAVING clause
• Subqueries
• Manipulating Data
  • Describe each DML statement

• Insert rows into a table

• Update rows in a table

• Delete rows from a table

• Merge rows in a table

• Control transactions
• Creating and Managing Tables
• Including Constraints
  • Describe constraints

• Create and maintain constraints

 

Tableau

• Introduction to Tableau Software
• Tableau Worksheets
• Tableau Calculations
• Tableau sort and filter
• Tableau charts
• Tableau Advanced
• Case Study with Tableau