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.

Best Data Science Online Classes | Bangalore
Best Data Science Institute | Bangalore
Data Science Training | Bangalore

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

I came here with zero confidence to this institue but now going out with full confidence.
Soumya Nayak

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.

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