Brief History of Computers

Impact of transistors

Computers Scientists

Fundamentals of Binary number

How an electricity is converted to Bits

Basics of Digital logic gates

Bits and Bytes - Unit of information

What is a Computer

Major components of a Computer

Fundamentals of Computer Network

Internet Basics

Understanding World Wide Web

Request/Response Relationship

SDLC Introduction

SDLC Phases

Project Initiation

Requirements Definition

Design

Develop

Test

Deploy

Maintenance

SDLC - Methodologies

Waterfall Model

V-Shaped Model

Incremental Model

Spiral Model

Prototyping

Product Implementations

Agile Framework - Scrum

Agile Introduction

Agile History

Agile Manifesto

Agile Manifesto : 12 Principles

Agile Vs Waterfall

Agile Methodology

Agile frameworks

SCRUM

Extreme Programming

LEAN

Rapid Application Development - RAD

Scaled Agile

SCRUM Values

SCRUM Framework

SCRUM Process

JIRA

JIRA family of products

Users & Use cases

Key terms to know

Issues

Projects

Board

Workflows

Project Template Options

Roadmap (Epics)

Child Issues

Backlog

Introduction

What Is Database?

What is DBMS?

What is Relational Model ?

Introduction to RDBMS .

Brief on E.F CODD

Datatypes and Constraints

What are Datatypes ?

Types and Examples .

How to use .

What are Constraints?

Types and Examples.

How to use.

Statements in SQL

Data Definition Language (DDL)

Data Manipulation Language (DML)

Transaction Control Language (TCL)

Data Control Language (DCL)

Data Query Language (DQL)

Software installation

Installing and set up of software

Working on Oracle 10g.

Data Query Language (DQL)

Select

From

Where

Group By

Having

Order By

Operators

Types and Examples

Functions in SQL

Single Row Functions

Multi Row Functions

Max ()

Min ()

Sum ()

Avg ()

Count ()

Sub Query

Introduction to Sub Query

Working of Sub Query

Query Writing and Execution

Types of Sub Query

Single Row Sub Query

Multi Row Sub Query

Nested Sub Query.

Pseudo Columns

Introduction on Pseudo Columns

ROWID

ROWNUM

Working and Usage.

JOINS

What Is Join?

Types of Joins.

Cartesian Join

Inner Join

Outer Join

Self-Join

Queries and Examples.

Co- Related Sub Query

Working and Examples

Data Definition Language (DDL)

Create

Rename

Alter

Truncate

Drop

Data Manipulation Language (DML)

Insert

Update

Delete

Transaction Control Language (TCL)

Commit

Save point

Rollback

Data Control Language (DCL)

Grant

Revoke

Normalization

Introduction to Normalization

Types of Normal Forms

Examples.

E R Diagrams

Introduction

Install & Run

Language Syntax

Variables & Objects

Keywords

Identifiers & Variables

Assigning values

Naming Variable

type( ) method

Variables are pointers

Everything is Object

Operators

Arithmetic Operators

Bitwise Operators

Assignment Operators

Comparison Operators

Boolean Operators

Identity & Membership

Built-in Types

Numbers

Strings

Booleans

NoneType

Lists

Tuples

Range

Dictionaries

Sets

Frozensets

Control Flow

Decision Making (if-elif-else)

Loops - for & while

break, continue, pass

Functions

lambda Expressions

Iterators

range()

enumerate()

zip()

map()

filter()

Iterator Arguments

List Comprehensions

Generators

Object Oriented Programming

Modules & Packages

Errors & Exceptions

Regular Expressions

Standard Modules

random Module

statistics Module

requests Module

math Module

sys Module

Introduction to Data Science

Field of Data Science

Difference between Analysis and Analytics

Business Analytics, Data Analytics, and Data Science

Business Intelligence, AI & ML

Traditional Data and Big Data

Working with Traditional Data

Working with Big Data

Business Intelligence (BI) Techniques

Techniques for Working with Traditional Methods

Machine Learning (ML) Techniques

Types of Machine Learning

Programming Languages and Software Used in Data Science

Introduction to Probability

Computing Expected Values

Frequency

Events and Their Complements

Combinatorics

Uses of Permutations

Simple Operations with Factorials

Solving Variations with Repetition

Solving Variations without Repetition

Solving Combinations

Symmetry of Combinations

Solving Combinations with Separate Sample Spaces

Probability - Bayesian Inference

Sets and Events

Ways Sets Can Interact

Intersection of Sets

Union of Sets

Mutually Exclusive Sets

Dependence and Independence of Sets

The Conditional Probability Formula

The Law of Total Probability

The Additive Rule

The Multiplication Law

Bayes' Law

Fundamentals of Probability Distributions

Types of Probability Distributions

Characteristics of Discrete Distributions

Discrete Distributions: The Uniform Distribution

Discrete Distributions: The Bernoulli Distribution

Discrete Distributions: The Binomial Distribution

Discrete Distributions: The Poisson Distribution

Characteristics of Continuous Distributions

Continuous Distributions: The Normal Distribution

Continuous Distributions: The Standard Normal Distribution

Continuous Distributions: The Students' T Distribution

Continuous Distributions: The Chi-Squared Distribution

Continuous Distributions: The Exponential Distribution

Continuous Distributions: The Logistic Distribution

Probability in Finance

Probability in Statistics

Probability in Data Science

Population and Sample

Descriptive Statistics

Types of Data

Levels of Measurement

Categorical Variables - Visualization Techniques

Numerical Variables - Frequency Distribution Table

The Histogram

Cross Tables and Scatter Plots

Mean, median and mode

Skewness

Variance

Standard Deviation

Coefficient of Variation

Covariance

Correlation Coefficient

Statistics - Inferential Statistics Fundamentals

What is a Distribution

The Normal Distribution

The Standard Normal Distribution

Central Limit Theorem

Standard Error

Estimators and Estimates

Statistics - Inferential Statistics: Confidence Intervals

Confidence Intervals

Population Variance Known

Z-score

Confidence Interval Clarifications

Student's T Distribution

Margin of Error

Confidence intervals. Two means. Dependent samples

Statistics - Hypothesis Testing

Null vs Alternative Hypothesis

Rejection Region and Significance Level

Rejection Region and Significance Level

Type I Error and Type II Error

Test for the Mean. Population Variance Known

p-value

Test for the Mean. Population Variance Unknown

Test for the Mean. Dependent Samples

Introduction to Regression Analysis

Advanced Statistical Methods - Linear Regression with StatsModels

The Linear Regression Model

Correlation vs Regression

Geometrical Representation of the Linear Regression Model

Python Packages Installation

First Regression in Python

Using Seaborn for Graphs

How to Interpret the Regression Table

Decomposition of Variability

OLS

R-Squared

Advanced Statistical Methods - Multiple Linear Regression with StatsModels

Multiple Linear Regression

Adjusted R-Squared

Test for Significance of the Model (F-Test)

OLS Assumptions

A1: Linearity

A2: No Endogeneity

A3: Normality and Homoscedasticity

A4: No Autocorrelation

A5: No Multicollinearity

Dealing with Categorical Data - Dummy Variables

Making Predictions with the Linear Regression

Advanced Statistical Methods - Linear Regression with sklearn

Introduction to sklearn

Simple Linear Regression with sklearn

A Note on Normalization

Multiple Linear Regression with sklearn

Calculating the Adjusted R-Squared in sklearn

Feature Selection (F-regression)

A Note on Calculation of P-values with sklearn

Creating a Summary Table with P-values

Feature Scaling (Standardization)

Feature Selection through Standardization of Weights

Predicting with the Standardized Coefficients

Underfitting and Overfitting

Train - Test Split Explained

Advanced Statistical Methods - Practical Example: Linear Regression

A Note on Multicollinearity

Advanced Statistical Methods - Logistic Regression

Introduction to Logistic Regression

A Simple Example in Python

Logistic vs Logit Function

Building a Logistic Regression

An Invaluable Coding Tip

Understanding Logistic Regression Tables

What do the Odds Actually Mean

Binary Predictors in a Logistic Regression

Calculating the Accuracy of the Model

Underfitting and Overfitting

Testing the Model

Introduction to Cluster Analysis

Some Examples of Clusters

Difference between Classification and Clustering

Math Prerequisites

Advanced Statistical Methods - K-Means Clustering

K-Means Clustering

A Simple Example of Clustering

Clustering Categorical Data

How to Choose the Number of Clusters

Pros and Cons of K-Means Clustering

To Standardize or not to Standardize

Relationship between Clustering and Regression

How is Clustering Useful?

Advanced Statistical Methods - Other Types of Clustering

Types of Clustering

Dendrogram

Heatmaps

What is a Matrix?

Scalars and Vectors

Linear Algebra and Geometry

Arrays in Python

Tensor

Addition and Subtraction of Matrices

Errors when Adding Matrices

Transpose of a Matrix

Dot Product of Matrices

Uses of Linear Algebra

Neural Networks

Training the Model

Types of Machine Learning

The Linear Model

The Linear Model with Multiple Inputs

The Linear model with Multiple Inputs and Multiple Outputs

Graphical Representation of Simple Neural Networks

What is the Objective Function?

Common Objective Functions: L2-norm Loss

Common Objective Functions: Cross-Entropy Loss

Optimization Algorithm: 1-Parameter Gradient Descent

Optimization Algorithm: n-Parameter Gradient Descent

Deep Learning - How to Build a Neural Network from Scratch with NumPy

NN Example

TensorFlow 2.0: Introduction

TensorFlow Outline and Comparison with Other Libraries

TensorFlow 1 vs TensorFlow 2

A Note on TensorFlow 2 Syntax

Types of File Formats Supporting TensorFlow

Outlining the Model with TensorFlow 2

Interpreting the Result and Extracting the Weights and Bias

Customizing a TensorFlow 2 Model

Digging Deeper into NNs: Introducing Deep Neural Networks

Layer

Deep Net

Digging into a Deep Net

Non-Linearities and their Purpose

Activation Functions

Softmax Activation

Backpropagation

Overfitting Introduction

Underfitting and Overfitting for Classification

What is Validation?

Training, Validation, and Test Datasets

N-Fold Cross Validation

Early Stopping or When to Stop Training

Initialization Introduction

Types of Simple Initializations

State-of-the-Art Method - (Xavier) Glorot Initialization

Digging into Gradient Descent and Learning Rate Schedules

Stochastic Gradient Descent

Problems with Gradient Descent

Momentum

Learning Rate Schedules, or How to Choose the Optimal Learning Rate

Learning Rate Schedules Visualized

Adaptive Learning Rate Schedules (AdaGrad and RMSprop )

Adam (Adaptive Moment Estimation)

Preprocessing Introduction

Types of Basic Preprocessing

Standardization

Preprocessing Categorical Data

Binary and One-Hot Encoding

Classifying on the MNIST Dataset

MNIST: The Dataset

MNIST: How to Tackle the MNIST

MNIST: Importing the Relevant Packages and Loading the Data

MNIST: Preprocess the Data - Create a Validation Set and Scale It

MNIST: Preprocess the Data - Shuffle and Batch

MNIST: Outline the Model

MNIST: Select the Loss and the Optimizer

MNIST: Learning

MNIST: Testing the Model

Business Case Example

Quantitive Appitude

Numbers

Average Mixture Allegation

Percentage, Profit & Loss

Time & Work

Pipes & Cistern

Geometry

Height & Distance

Progressions

Time Speed & Distance

Boats & Streams

Blood relations

Permutations & Combinations

Probability

Clocks & Calender

Simple Equations

Problems on Ages

Direction Sense

Analytical Reasoning

Data sufficiency

Data Interpretations

Verbal Ability

Parts of Speech

Tenses

Subject-Verb Agreement

Phrases & Idioms

Sentence Correctoion

Vocabulary

Usage of Parts of Speech

Articles

Group Discussion

Mock Interviews

Effective Communication

Resume Building

How to apply for a job