## Data Science Courses Online in Bangalore

**Data Science Courses online** in Bangalore are providing better education from others. This is the best coaching institute for achieving your excellence. With the growing number of institutes offering *Data Science courses online* but you have to choose the right one. We bring here the best data science institute for you. Online IT Class is giving a short term course with 100% Job Placement. We have great infrastructure, campus, placement, reviews, experience trainers, live projects, real-time classes, etc.

**Data Science** is basically dealing with structured and unstructured data. Data Science is comprising of everything which is related to data cleansing, preparation, and data analysis. A Data Science is a professional who possesses the ability to transform raw data into useful insights to make better business decision. Data Science will provide you with extensive expertise in the booming data analytics and data science fields. Data Science is the process of using data to find solutions / to predict outcomes for a problem statement.

• Business Requirements

• Data collection

• Data Cleaning

• Data Exploration & Analysis

• Data Modelling

• Data validation

• Deployment & optimization

It is an E-commerce platform like Amazon & Flipkart. It is also the logic behind Netflix’s recommendation system now. It has made remarkable changes in today’s market. A* data scientist* is the most promising job role in India.

A **data scientist** must be in statistics expertise programming languages like R and python. You’re required to have a good understanding of processes like data extraction processing wrangling and exploration. You must also be well-versed with the different types of machine learning algorithms and how they work. Advanced machine learning concepts like deep learning is also needed, you must also possess a good understanding of different big data processing Frameworks, like Hadoop and spark and finally you must know how to visualize the data by using tools like tableau and power bi. So now its time to buckle up and kick start your career as a data scientist. A data scientist should have the following 7 skills:

• Database knowledge

• Statistics

• Programming tools

• Data Wrangling

• Data Visualization

• Big Data

• Machine Learning

At least a master’s degree in a quantitative discipline, or a bachelors degree and several** online data science courses**. Many data scientists will be heavily specialized in business, often specific segments of the economy (such as automotive or insurance) or business-related fields like marketing or pricing. These degrees will help you to learn data science include:

• Computer science

• Statistics

• Physics

• Social science

• Mathematics

• Applied math

• Economics

Yes it is possible to learn **data science online**. Online instructor come with practical knowledge and may be from any location across the globe. This allows students to be exposed to knowledge that can‘t be learned in books and see how class concepts are applied in real business situation. Using internet to attend class. Information & communication teaches skills in using technologies. Students can interact with their peers in virtual break-out rooms, take notes and screenshots easily, and access previous recordings of lectures. Online environment makes instructors more approachable. Online course development allows for a broad spectrum of content.

Assured Seamless Connectivity During the Training Period. No Disturbance Training Experience Gauranteed

Latest Course Material Created and Maintained by Industry Veterans. Full Value for the Money You Spend

Trainers and Teaching Staff – All have more than 6+ years experience – Real-Time as well as Teaching

**

**

**

**

**5/5

## DATA Science Course Content

Online it class Data Science with Python Syllabus

SECTION I — Python Basics

Lesson 1: Overview

Why do we need Python?

Program structure

Environment Setup

Python Installation

Execution Types

What is an interpreter?

Interpreters vs Compilers

Using the Python Interpreter

Interactive Mode

Running python files

Working with Python shell

Integrated Development Environments (IDES)

Interactive Mode Programming

Script Mode Programming

Lesson 2 : Basic Concepts

Basic Operators

Types of Operator

Python Arithmetic Operators

Python Comparison Operators

Python Assignment Operators

Python Bitwise Operators

Python Logical Operators

Python Membership Operators (in, not in)

Python Identity Operators (is, is not)

Python Operators Precedence

Data Types

Variables

Assigning Values to Variables

Multiple Assignment

Python Numbers

Python Strings

Accessing Values in Strings

String Special Operators

String Formatting Operator

Triple Quotes

Built-in String Operations

Python Lists

Accessing Values in Lists

Updating Lists

Delete List Elements

Basic List Operations

Indexing, Slicing, and Matrixes

Built-in List Functions & Methods

Python Tuples

Accessing Values in Tuples

Updating Tuples

Delete Tuple Elements

Basic Tuples Operations

Indexing, Slicing, and Matrixes

No Enclosing Delimiters

Built-in Tuple Functions

Python Dictionary

Accessing Values in Dictionary

Updating Dictionary

Delete Dictionary Elements

Properties of Dictionary Keys

Built-in Dictionary Functions & Methods

Lesson 3:Loops and Decision Making

if statements

if…else statements

nested if statements

while loop

for loop

nested loops

Loop Control Statements

1) break statement

2) continue statement

3) pass statement

Lesson 4 :Functions

Defining a Function

Syntax

Calling a Function

Pass by reference vs value

Function Arguments

Required arguments

Keyword arguments

Default arguments

Variable-length arguments

The return Statement

Scope of Variables

Global vs. Local variables

Lesson 5: Basic OOPs Concept

Creating class in Python

Documented String

Private Identifier

Constructor

Inheritance

Polymorphism

Lesson 6 : Python Modules and Packages

Framework vs Packages

Folium Introduction

Why are modules used?

Creating modules

The import Statement

The from…import Statement

The from…import * Statement

Locating Modules

The PYTHONPATH Variable

Namespaces and Scoping

The dir( ) Function

The globals() and locals() Functions

The reload() Function

Packages in Python

Lesson 7: Advance Python

Decorator, Iterator and Generator

Anonymous Function

Lambda

Map

Filter

Reduce

Errors and Exception Handling

Standard exceptions

Assertions in Python

The assert Statement

What is Exception?

Handling an exception

Section II — Statistics and Data Science Overview

Lesson8: Data Science Overview

Data Science Disciplines

Data Science and Business Buzzwords Why are there so many

What is the difference between Analysis and Analytics

An Introduction–Business Analytics, Data Analytics, and Data Science

Data Science Diagram

Introduction — BI, ML and AI

Careers in Data Science Fields

Data Overview

What is Data

Measuring Data

Measurement of Central Tendency

Measurements Dispersion

Measurement Quartile

Bi-variate Data and Co-variance

Pearson Correlation Coefficient

Lesson 9 : Probability

What is Probability

Permutations

Combinations

Intersections Unions and Complements

Independent and Dependent Events

Conditional Probability

Addition and Multiplication Rules

Bayes Theorem

Lesson 10: Distributions

Introduction to Distributions

Uniform Distribution

Binomial Distribution

Poisson Distribution

Normal Distribution

Lesson 11:Statistics

What is Statistics

Sampling

Central Limit Theorem

Standard Error

Hypothesis Testing

Hypothesis Testing Example Exercise

Type 1 and Type 2 Errors

Students T Distribution

Practical Example Descriptive Statistics Exercise

What are Confidence Intervals

Correlation Matrix

Lesson 12:Anova

Introduction to ANOVA

Two Way ANOVA Overview

F- Distribution

Lesson 13:Chi Square Analysis

Chi-Square Analysis

Chi Squared Analysis – Exercise Example

Section III — Python for Data Analysis

Lesson 14 : Python: Environment Setup and Essentials

Introduction to Anaconda

Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux

Jupyter Notebook Installation

Jupyter Notebook Introduction

Lesson 15:Data Analysis- Numpy

Introduction to Numpy

Numpy Array

Numpy Indexing

Numpy Operations

Broadcasting Numpy Array

Lesson 16:Data Analysis — Pandas

Introduction to Pandas

Series

Data Frames

Missing Data

Groupby

Operations

Merging, Joining and concatenating

Missing Data

Data Input and Output

Lesson 17:Pandas Exercise

Salaries Exercise

Ecommerce Purchases Exercise

Lesson 18:Numpy Exercise

Solving Linear System

Problem Set

Section IV — Python for Data Visualization

Lesson 19:Matplotlib

Introduction

Matplotlib Drawing Graph — Histogram, Plotting, Box Plot etc

Exercise

Lesson 20:Seaborn

Introduction

Distribution

Categorical Plots

Matrix Plots

Regression Plots

Grids

Style and Colors

Exercise

Lesson 21:Data Visualization with Pandas

Pandas Built-in Data Visualization

Pandas Data Visualization Exercise

Lesson 22:Data Visualization – Geographical Plotting

Introduction to Geographical Plotting

Choropleth Maps – Part 1 – USA

Choropleth Maps – Part 2 – World

Choropleth Exercises

Capstone Project I

Calls Data Capstone Project

Finance Project

Lesson 23:Time Series Analysis

Pandas for Time Series

Introduction to Time Series with Pandas

Date time Index

Time Re-sampling

Time Shifts

Pandas Rolling and Expanding

Time Series Analysis

Introduction to Time Series

Time Series Basics

Introduction to Statsmodel

Capstone Project II

Stock Market Analysis Project

Lesson 24:Scientific computing with Python (Scipy)

SciPy and its Characteristics

SciPy sub-packages

SciPy sub-packages –Integration

SciPy sub-packages – Optimize

Linear Algebra

SciPy sub-packages – Statistics

SciPy sub-packages – Weave

SciPy sub-packages – I O

Lesson 25: Data Science with Python Web Scraping

Web Scraping

Common Data/Page Formats on The Web

The Parser

Importance of Objects

Understanding the Tree

Searching the Tree

Navigating options

Modifying the Tree

Parsing Only Part of the Document

Printing and Formatting

Encoding

Section V — Machine Learning

Lesson 26: Machine Learning with Python (Scikit–Learn)

Introduction to Machine Learning

Machine Learning Approach

How Supervised and Unsupervised Learning Models Work

Scikit-Learn

Supervised Learning Models – Linear Regression

Supervised Learning Models: Logistic Regression

K Nearest Neighbors (K-NN) Model

K Means Algorithm

SVMs

Unsupervised Learning Models: Clustering

Unsupervised Learning Models: Dimensionality Reduction

Pipeline

Model Persistence

Model Evaluation – Metric Functions

Lesson 27: Natural Language Processing with Scikit-Learn

NLP Overview

NLP Approach for Text Data

NLP Environment Setup

NLP Sentence analysis

NLP Applications

Major NLP Libraries

Scikit-Learn Approach

Scikit – Learn Approach Built – in Modules

Scikit – Learn Approach Feature Extraction

Section VI: Project Works

Project 1– Board Game Review Prediction— To perform a Linear Regression Analysis by predicting the average reviews in a board game

Project 2 — Credit Card Fraud Detection — To focus on Anomaly Detection by using probability densities to detect credit card fraud

Project 3 – Stock Market Clustering – Learn how to use the K-means clustering algorithm to find related companies by finding correlations among stock market movements over a given time span

Project 4 – Getting Started with Natural Language Processing in Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.