Learn Data Science Courses in Bangalore

Are you looking for data science courses in Bangalore with live projects? We have the perfect course for you! Our data science course includes live projects so you can learn by doing. With our course, you’ll be able to learn the skills you need to land a job in data science. Enroll now and get started today!

Best Data Science Institute in Bangalore

Indras Academy data science course in Bangalore is one of the most comprehensive and well-rounded courses available in the city. It covers all the essential topics related to data science, such as data mining, machine learning, statistical analysis, and more. The course is taught by experienced professionals who have years of experience in the field. Additionally, the course is also very affordable, making it a great option for those looking to get started in data science.

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Learn how to use Python for data science and machine learning, and get started with hands-on exercises and real-world data sets.

This course is perfect for those who want to learn more about data science and how to use R programming to analyze data.

Tableau is an all-purpose tool that is used in data analysis and data interpretation.

Under our expert guidance, you’ll become a Pro Data Scientist who has sound knowledge and practical experience in statistics.

Learn how to program in R and Python, how to use machine learning algorithms, and how to build your own projects

Natural Language Processing is an integral part of machine learning and it helps to develop heuristic programming.

SQL

Predictive Analytics helps to provide better predict and forecast on the basis of historical data.

Data visualization helps the data to be changed into an easily interpretable format. Become a data scientist.

Learn the distinguishing factors between Data Analytics, Data Science

Why Institute of Data Science?

Institute of Data Science is a Specialized Educational Center that Provides the best Data Science Courses in Bangalore.

OUR FAQS

Frequently Asked Questions

Data science is one of the best career options in 2020 as the need for data science professionals is very high. A beginner in data science earns around 4.5 Lakhs per annum while the pay for experienced people depends on the number of years and the projects one has worked.

You can earn more than ₹8 lakhs with around 4-5 years of experience. One can easily earn ₹12 Lakhs per annum after 7 to 8 years of experience in data science. Not only is the pay high in this field, but even the job satisfaction is great in the field of data science and data analytics.

The present decade will also witness an exponential rise in the number of data science jobs. Hence, data science courses are one of the best courses to learn in 2020.

  • Hewlett Packard
  • International Business Machines Corp
  • Amazon
  • Flipkart
  • SAP
  • Honeywell
  • ExxonMobil
  • Western Digital
  • Diageo
  • Mindtree
 

Although anyone working professional or students with interest can take up this Data Science course, it requires certain prior experience in coding and developing. Individuals with a good understanding of mathematics, statistics, Python, R programming languages will be able to master the course easily. Work experience is not necessary but it will be of great help during the course as it helps in understanding the course topics and materials in a better way.

Data Science and Machine Learning courses can be finished in a period of 3 months. The course is
specifically designed for working professionals. So, it is scheduled to provide maximum learning
within a short time.

Data science and machine learning courses are held in two modes.

  1. Online classes
  2. Weekdays / Weekend classroom sessions.

If you are a student of online classes, you can attend the online classes at a time that is convenient to you. If you are taking the course in classrooms, then you can attend the class along with other batches.

Note: Although alternative arrangements can be made, it is expected of a candidate to attend all the classes without fail.

Data science is a specialized field that converges at the center of data collection, statistics, data interpretation and extrapolation. It has become an all-important field of science in the present day
world. Data is created by everything around us and by every action that we do. This huge data that is being constantly created has to be collected, segregated, and properly classified for easier
interpretation of data. The need for the above mentioned skills are there in all sectors of the industry.
Organizations large and small have a huge requirement for skilled data scientists to draw out practical insights from the data collected. Multinational companies like Amazon, Flipkart, HP, IBM, etc., provide a high compensation for data scientists. It makes Data science a suitable career option for anyone who is interested in a worry free profession.

Data Science Course Syllabus

Data Science is the sexiest job of the 21st century. Are you interested in getting started with data science? Our course will help you get started. Sign up for our data science course today!

Programming Basics & Environment Setup

  • Installing Anaconda
  • Anaconda Basics and Introduction
  • Get familiar with version control, Git and GitHub.
  • Basic Github Commands.
  • Introduction to the Jupyter Notebook environment.
  • Basics Jupyter notebook Commands.
  • Programming language basics.
  • Python Overview
  • Python 2.7 vs Python 3
  • Writing your First Python Program Lines and Indentation
  • Python Identifiers
  •  Various Operators and Operators Precedence
  • Getting input from User, Comments, Multi line Comments.
  • Working with Numbers, Booleans and Strings
  • String types and formatting 
  • String operations Simple if Statement, if-else Statement if-elif Statement.
  • Introduction to while Loops.
  •  Introduction to for Loops
  • Using continue and break.
  • Class hands-on: programs/coding exercise on string, loop and conditions in the classroom.
  • List, Tuples, Dictionaries Python Lists, Tuples, Dictionaries 
  • Accessing Values, Basic Operations Indexing, Slicing, and Matrixes Built-in Functions & Methods.
  • Exercises on List, Tuples And Dictionary
  • Class hands-on: Program to convert tuple to dictionary Remove Duplicate from Lists
  • Python program to reverse a tuple Program to add all elements in list.
  • Introduction To Functions
  • Why Defining Functions
  • Calling Functions
  • Functions With Multiple Arguments.
  • Anonymous Functions
  • Lambda Using Built-In Modules
  • User-Defined Modules
  • Module Namespaces
  • Iterators And Generators.
  • Opening and Closing Files
  • open Function, file Object Attributes close() Method ,Read, write,seek. 
  • Exception Handling, try-finally Clause Raising an Exceptions, User-Defined Exceptions.
  • Regular Expression- Search and Replace Regular Expression Modifiers Regular Expression Patterns.
  • Introduction to Numpy.
  • Array Creation,Printing Arrays
  • Basic Operation -Indexing, Slicing and Iterating
  • Shape Manipulation – Changing shape,stacking and splitting of arrays.
  • Vector stacking, Broadcasting with Numpy, Numpy for Statistical Operation. 
  • Pandas : Introduction to Pandas Importing data into Python
  • Pandas Data Frames, Indexing Data Frames
  • ,Basic Operations With Data frame,Renaming Columns,Subletting and filtering a data frame.
  • Introduction,plot(),Controlling Line
  • Properties,Subplot with Functional Method, MUltiple Plot, Working with Multiple Figures,Histograms Seaborn :
  • Intro to Seaborn And Visualizing statistical relationships
  • Import and Prepare data
  • Plotting with categorical data and Visualizing linear relationships
  • Seaborn Exercise

Installation Setup

Quick guide to RStudio

User Interface RStudio’s GUI3

Changing the appearance in RStudio

Installing packages in R and using the library

Development Environment

Overview Introduction to R 

basics Building blocks of R

Core programming principles Fundamentals of R

Creating an object Data types in R

Coercion rules in R

Functions and arguments Matrices Data Frame

Data Inputs and Outputs with R

Vectors and Vector operation

Advanced Visualization Using the script vs. using the console

Data transformation with R – the Dplyr package – Part

Data transformation with R – the Dplyr package – Part Sampling data with the Dplyr package

Using the pipe operator in R Tidying data in R – gather() and separate()

Tidying data in R – unite() and spread()

Intro to data visualization

Introduction to ggplot2

Building a histogram with ggplot2

Building a bar chart with ggplot2

Building a box and whiskers plot with ggplot2

Building a scatter plot with ggplot2

Connecting To Datasource Creating dashboard pages

How to create calculated columns Different charts

Hands on on connecting data source and data cleansing

Hands on various charts

Getting Started With Visual Analytics Sorting and grouping

Working with sets

set action Filters: Ways to filter, Interactive Filters Forecasting andClustering

Hands on deployment of Predictive model invisualization

Working in Views with Dashboards and Stories

Working with Sheets Fitting Sheets

Legends and Quick Filters Tiled and Floating Layout Floating Objects

Coordinate points

Plotting Latitude and Longitude

Custom Geocoding

Polygon Maps

WMS and Background Image

Introduction to Text Analytics

Introduction to NLP

What is Natural Language Processing?

What Can Developers Use NLP Algorithms For?

NLP Libraries

Need of Textual Analytics Applications of Natural Language Processing

Word Frequency Algorithms for NLP Sentiment Analysis

Need of Pre-Processing

Various methods to Process the Text data

Tokenization, Challenges in Tokenization

Stopping, Stop Word Removal Stemming – Errors in Stemming Types of Stemming Algorithms – Table lookup Approach ,N-Gram Stemmers

String Similarity

Cosine Similarity

Mechanism – Similarity between Two text documents Levenshtein distance – measuring the difference between two sequences Applications of Levenshtein distance LCS(Longest Common Sequence )

Problems and solutions ,LCS Algorithms

Information Retrieval – Precision, Recall, F- score TF-IDF

KNN for document retrieval K-Means for document retrieval Clustering for document retrieval

Fundamentals of Math and Probability

  • Basic understanding of linear algebra, Matrics, vectors.
  • Addition and Multiplication of matrices.
  • Fundamentals of Probability
  • Probability distributed function and cumulative distribution function.
  • Class Hand-on Problem solving using R for vector manipulation.
  • Problem solving for probability assignments.

The mean,median,mode, curtosis and skewness Computing Standard deviation and Variance.

Types of distribution.

Class Handson: 5 Point summary BoxPlot Histogram and Bar Chart Exploratory analytics R Methods.

What is inferential statistics Different types of Sampling techniques Central Limit Theorem.

Point estimate and Interval estimate.

Creating confidence interval for population parameter Characteristics of Z-distribution and T- Distribution.

Basics of Hypothesis Testing Type of test and rejection region Type of errors in Hypothesis resting.

Type-l error and Type-ii errors P-Value and Z-Score Method T-Test, Analysis of variance(ANOVA) and Analysis of Covariance(ANCOVA) Regression analysis in ANOVA.

Class Hands-on: Problem solving for C.L.T Problem solving Hypothesis Testing Problem solving for T-test, Z-score test Case study and model run for ANOVA.

Basics of Hypothesis Testing Type of test and Rejection Region

Type o errors-Type 1 Errors,Type 2 Errors

P value method,Z score Method. 

The Chi-Square Test of Independence Regression

Factorial Analysis of Variance

Pearson Correlation Coefficients in Depth Statistical Significance, Effect Size, and Confidence Intervals

Introduction to Data Cleaning

Data Preprocessing What is Data Wrangling?

How to Restructure the data? What is Data Integration?

Data Transformation

EDA : Finding and Dealing with Missing Values.

What are Outliers? Using Z- scores to Find Outliers. 

Introduction to Bivariate Analysis,Scatter Plots and Heatmaps.

 Introduction to Multivariate Analysis

Introduction To Machine Learning

What is Machine Learning? 

Introduction to Supervised and Unsupervised Learning

Introduction to SKLEARN (Classification, Regression, Clustering, Dimensionality reduction, Model selection, Preprocessing)

What is Reinforcement Learning?

Machine Learning applications

Difference between Machine Learning and Deep Learning

Support Vector Machines Linear regression

Logistic Regression Naive Bayes

Linear discriminant analysis Decision tree

k-nearest neighbor algorithm Neural Networks (Multilayer perceptron)

Similarity learning

 Introduction to Linear Regression

 Linear Regression with Multiple Variables

Disadvantage of Linear Models

Interpretation of Model Outputs

Understanding Covariance and Collinearity

Understanding Heteroscedasticity

Case Study – Application of Linear Regression for Housing Price Prediction

Introduction to Logistic Regression.

Why Logistic Regression .

Introduce the notion of classification

Cost function for logistic regression

Application of logistic regression to multi-class classification.

Confusion Matrix, Odds Ratio And ROC Curve

Advantages And Disadvantages of Logistic Regression.

Case Study:To classify an email as spam or not spam using logisticRegression.

Decision Tree – data set How to build a decision tree?

Understanding Kart Model

Classification Rules- Overfitting Problem

Stopping Criteria And Pruning 

How to Find the final size of Trees?

Model A Decision Tree.

Naive Bayes

Random Forests and Support Vector Machines

Interpretation of Model Outputs

Hierarchical Clustering

k-Means algorithm for clustering – groupings of unlabeled data points.

Principal Component Analysis(PCA)- Data

Independent components analysis(ICA) Anomaly Detection

Recommender System-collaborative filtering algorithm

Case Study– Recommendation Engine for e-commerce/retail chain

Introduction to Natural Language Processing (NLP).

Word Frequency Algorithms for NLP Sentiment Analysis

Case Study : Twitter data analysis using NLP

Basics of Time Series Analysis and Forecasting ,Method Selection in Forecasting

Moving Average (MA) Forecast Example,Different Components of Time Series Data ,Log Based Differencing, Linear Regression For Detrending

Introduction to ARIMA Models,ARIMA Model Calculations,Manual ARIMA Parameter Selection,ARIMA with Explanatory Variables

Understanding Multivariate Time Series and Their Structure,Checking for Stationarity and Differencing the MTS

Case Study : Performing Time Series Analysis on Stock Prices

Introduction to Deep Learning And TensorFlow

 

Neural Network

Understanding Neural Network

Model Installing

TensorFlow Simple Computation ,Constants And Variables

Types of file formats in TensorFlow

Creating A Graph – Graph Visualization

Creating a Model – Logistic Regression

Model Building using tensor flow

TensorFlow Classification Examples

Installing TensorFlow

Simple Computation , Contants And Variables

Types of file formats in TensorFlow

Creating A Graph – Graph Visualization

Creating a Model – Logistic Regression

Model Building

TensorFlow Classification Examples

Basic Neural Network Single Hidden Layer Model

Multiple Hidden Layer Model

Backpropagation – Learning Algorithm and visual representation

Understand Backpropagation – Using Neural

Network Example TensorBoard

Project on backpropagation

Convolutional Layer Motivation

Convolutional Layer Application
Architecture of a CNN

Pooling Layer Application

Deep CNN

Understanding and Visualizing a CNN

Introduction To RDBMS Single Table Queries – SELECT,WHERE,ORDER

BY,Distinct,And ,OR Multiple Table Queries: INNER, SELF, CROSS, and OUTER, Join, Left Join, Right Join, Full Join, Union.

Advance SQL Operations: Data Aggregations and summarizing the data

Ranking Functions: Top-N Analysis Advanced SQL Queries for Analytics.

Topics – What is HBase? HBase Architecture, HBase Components,

Storage Model of HBase, HBase vsRDBMS

Introduction to MongoDB, CRUD Advantages of MongoDB over RDBMS

Use cases

Mathematical Functions

Variables

Conditional Logic Loops

Custom Functions Grouping and Ordering Partitioning

Filtering Data Subqueries

COURSE OPTIONS

A leading online and classroom data science training provider in Bangalore. Our data science training is highly industry-relevant, and gives you the opportunity to work with real-world data.

Live Virtual

Instructor Led Live Online

Classroom

In - Person Classroom Training

Course Duration and Fees

 data science is the hot new field today. online and classroom data science courses for beginners and advanced. learn data science from the best data science experts.

Course Fees :

₹ 28000 ₹ 39,990

There are 3 batches we are taking classes:

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