What is Data?

What is Data Science?

What does Data Sc. Involve?

Tools of data Sc.

What is Machine Learning?

Where is Machine Learning used?

Job Roles

Introduction to Tableau & Installation

Importing Data & User Interface

Basics of Excel

Dimension & Measure

Introduction to Tableau Commands

Discrete Vs. Continuous Data

Aggregations in Tableau

Creating Charts

Types of Charts

Bar Charts

Stacked Bar Charts

Line Charts

Scatter Plot

Area Charts

Pie Charts

Tree Maps

Heat Maps

Bubble Charts

Bullet Charts

Box & Whisker plots

Pareto Charts

Histograms

Gantt Charts

Donut Charts

Funnel Charts

Waterfall Charts

Tableau Storyline

Set, Parameters & Groups

Tableau IF Statements

Case Statements of Tableau

Tableau Functions

String Function

Table Calculations

Rank Functions

Aggregate Functions

Date Functions

Window Sum

LOD Expressions

Look Up Functions

Fixed Functions

Count Distinct

Windows Functions

Sortings

Filters

Types of Filter

Dimension Filter

Measure Filter

Visual Filter

Context Filter

Create Dashboard

Design Dashboard

Adding Filters in Tableau Dashboard

Tableau Workbook

Reporting Tools

Story with Dashboard

Interactive Filter

Data Source Filter

Parameter Filter

Edit Data Source

Unions

Joins

Data Blending

Creating Set in Tableau

Pivot in Tableau

Forecast in Tableau

Map Layers

Tableau Group By

Hierarchy

Tableau User Group

Introduction to Tableau Prep

Data Connections

Visual Analytics and Case Study Problem statement discussion

Case Study and assessment solution discussion

Dashboard & Stories, Advanced Charts and Case Study Problem statement discussion

Case Study Presentation, Revision, Interview Preparation

Introduction to SQL Server

Oracle Vs. Microsoft SQL Server

Installing and Configuring SQL Server

MS Server Management Studio Instatllation

Working with Databases and Storage

Planning and Implementing a Backup Strategy

Restoring SQL Server 2014 Databases

Importing and Exporting Data

Monitoring SQL Server 2014

Tracing SQL Server Activity

Managing SQL Server Security

Performing Ongoing Database Maintenance

Industry based Projects

SQL Server Developments (T-SQL) *Topics provided below

1. T-SQL - OVERVIEW

2. T-SQL SERVER - DATA TYPES

3. T-SQL SERVER - CREATE TABLES

4. T-SQL SERVER - DROP TABLES

5. T-SQL SERVER - INSERT STATEMENT

6. T-SQL SERVER - SELECT STATEMENT

7. T-SQL SERVER - UPDATE STATEMENT

8. T-SQL SERVER - DELETE STATEMENT

9. T-SQL SERVER - WHERE CLAUSE

10. T-SQL SERVER - LIKE CLAUSE

11. T-SQL SERVER - ORDER BY CLAUSE

12. T-SQL SERVER - GROUP BY CLAUSE

13. T-SQL SERVER - DISTINCT CLAUSE

14. T-SQL SERVER - JOINING TABLES

15. T-SQL SERVER - SUB-QUERIES

16. T-SQL SERVER - STORED PROCEDURES

17. T-SQL SERVER – TRANSACTIONS

18. T-SQL SERVER - INDEXES

19. T-SQL SERVER - SQL FUNCTIONS

20. T-SQL SERVER - STRING FUNCTIONS

21. T-SQL SERVER - DATE FUNCTIONS

22. T-SQL SERVER - NUMERIC FUNCTIONS

23. T-SQL SERVER – Create Stored Procedure

24. T-SQL SERVER – Alter Stored Procedure

25. T-SQL SERVER – Delete Stored Procedure

26. T-SQL SERVER – Create View

27. T-SQL SERVER – Alter View

28. T-SQL SERVER – Delete View

Normalizations

ER Diagrams in SQL Server

Industry based Projects

Introduction to Python

Computer Programming Data Types

Variables

Basic Input-Output Operations

Basic Operators

Boolean Values

Conditional Execution

Loops

Lists

Logical and Bitwise Operations

Functions

Tuples

Dictionaries

Data Processing

Modules

Packages

String and List Methods

Exceptions

The Object-Oriented Approach: Classes

Methods

Objects and the Standard Objective Features

Exception Handling

Working with Files

Introduction to probability 

Sampling and sampling distributions

Unions of Events and Addition Rules

Unions of Events and Addition Rules

Conditional Probability

Independence

Random Variables

Baye'S Theorem 

Average, Mean, Median, Mode

Range, Quartiles & Percentiles

Interquartiles Range

R-SQUARED

Variance  & Standard Deviation

Statistical Inference

Normal Distribution

Standard Normal Distribution

Poisson Distribution

Bernoulli Distribution

Point Estimate

Confidence level & Confidence Intervals

Margin of Error

Population Means

Hypothesis Testing

Hypothesis Testing : Two Sample Test

Hypothesis Testing Proportion & Mean

Z- Test & T-Test

One-tailed and two-tailed tests

The F distribution

The chi-square distribution

The chi-square test of independence

Introduction to Python

Computer Programming Data Types

Variables & Data Types

Basic Input-Output Operations

Basic Operators

Boolean Values

Strings

Lists & Tuples

Dictionary

Sets

Conditional Expressions

Loops

Logical and Bitwise Operations

Functions & Recursions

File Input & Output

Exceptions handling

Object Oriented Programming

Virtual environment & Python libraries

Some special functions in python e.g Lambda, bin, format, map, filter, reduce

Web scraping

Ipython

Jupyter

NumPy Array

Sorting Array

NumPy Universal Functions

Array Indexing: Accessing Single Elements 

Array Slicing: Accessing Subarrays

Reshaping of Arrays

Array Concatenation and Splitting 

NumPy Arrays: Universal Functions 

Aggregations: Min, Max, and Everything in Between 

Sorting Arrays

Fast Sorting in NumPy: np.sort and np.argsort 

Partial Sorts: Partitioning 

Structured Data: NumPy’s Structured Arrays 

Pandas Series Object 

Pandas DataFrame Object 

Pandas Index Object

Data Indexing and Selection

Operating on Data in Pandas 

Handling Missing Data

Hierarchical Indexing

Combining Datasets: Concat and Append 

Combining Datasets: Merge and Join

Aggregation and Grouping 

Pivot Tables 

Vectorized String Operations

Working with Time Series 

Motivating query() and eval(): Compound Expressions 

pandas.eval() for Efficient Operations 

DataFrame.eval() for Column-Wise Operations 

DataFrame.query() Method 

Visualization with Matplotlib

 Line Plots 

Scatter Plots

Scatter Plots with plt.scatter

Visualizing Errors 

Density and Contour Plots

Histograms, Binnings, and Density

Customizing Plot Legends, Colorbars

Multiple Subplots 

Text and Annotation

Configurations and Stylesheets

Three-Dimensional Plotting in Matplotlib

Basemap

Visualization with Seaborn

Matplotlib Vs. Seaborn

Introduction

Types of Machine Learning

Supervised Vs. Unsupervised Learning

Instance-Based Vs. Model-Based Learning

Insufficient quantity of Training Data

Nonrepresentative Training Data

Poor-Quality Data

Irrelevant Features

Overfitting the Training Data

Underfitting the Training Data

Supervised Learning & Types

Classifiction & Types

Regression & Types

Acuracy Metrics

Training a Binary Classifier

Performance Measures

Measuring Accuracy Using Cross-Validation

Confusion Matrix

Precision and Recall

Multiclass Classification

Error Analysis

Multilabel Classification

Multioutput Classification

Projection & Manifold Learning

Principal Component Analysis

Data wrangling

Gradient Descent

Polynomial Regression

Learning Curves

Ridge, Lasso Regression

Elastic Net & Early Stopping

Logistic Regression

Training and Cost Function

Sigmoid Probability

Accuracy Matrix

Decision Boundaries

Linear SVM & Classification

SVM : Linear Separability

SVM : Mathematical Representation

Nonlinear SVM & Classification

SVM Regression

Kernal Trick

Decision Function Classifier

Overfitting

Random Forest Classifier

Performance Measure Confusion Matrix & Cost Matrix

Estimating Class Probabilities

The CART Training Algorithm

Gini Impurity or Entropy?

Regularization Hyperparameters

Instability

Overview & Applications

Types of Unsupervised Learning

Overview

Hierarchical Clustering

Ensemble Learning

Bagging and Pasting with Scikit-Learn

Out-of-Bag Evaluation

Random Patches and Random Subspaces

Random Forests

AdaBoost

Gradient Boosting

XGBoost & Parameters

Model Selection

Overview of Time Series Modeling

Time Series Patterns & Types

White Noise

Stationarity

Removal of Non-Stationarity

Time Series Models

Purpose of Recommender Systems

Paradigms of Recommender Systems

Collaborative Filtering

Association Rule Mining

Text Mining: Overview & Significance

Application

Text Extraction & Preprocessing

Introduction

Why Tensorflow

Installation

Node Value

Linear Regression with Tensorflow

Gradient Descent

Training Machine Learning Program with Tensorflow API

Training Deep Neural Nets

Distributing Tensorflow across servers

Convolutional Neural Network

Recurrent Neural Network

Transfer Learning

Autoincoders

Reinforcement Learning

Quantitative Reasoning

Logical Reasoning 

Critical Reasoning

Integrated Reasoning

Verbal Reasoning

 Integrated Reasoning

​How to build Resume

How to write Cover Letter

LinkedIn Profile Building Session

Git Hub Profile Making

How to do Job Application 

How to give PPT Presentation

Telephonic Etiquette

Email Etiquette

Mock Interviews