Course description

Data Science Training in Bangalore

Learnbay Provides data science training in Bangalore from certified experts.Our data science courses helps you to learn various data analytics techniques Python,R,SAS and Tableau.Data Science Course content is designed by experts to match with the real world requirements for both beginner and advance level.Many real world problems and case studies are implemented throughout the course and discussed in the class with tons of assignments for practice.Course is best suitable for working professionals who are looking for classroom data science training in Bangalore and start their career in data science.We are rated as one of the best Institute for providing data science courses and training in Bangalore.

This course will benefit you to master data science skills and will help you to handle interview with more confidence if you are looking for job in data science domain.If you are a working professional and looking for classroom data science training in Bangalore to start your career in data science domain ,This course is best suited for you.

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Data Science Course Features in Detail:

Learnbay Data science course is especially designed for working professionals having experience in any other domain or technologies and wants to change domain to data analytics.Our Data science course features 200+ hours of Instructor led classroom data science training in Bangalore With Real Time Project And Job Assistance.Our data science training courses helps you to learn essential Machine Learning and Artificial Intelligence Skills which makes you job ready and Start your career in Data Science and Analytics Domain.All Our Instructors And Project Mentors are working as data scientist and have Real Time Industry experience.

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Modules and Tools Covered in Data science Course:

Python For Data Science

R Programming

Statistics For Data Science

Machine Learning

Tensor-Flow & Deep Learning

SAS

Time Series Forecasting

Hadoop And Spark Analytics

SQL And Mongodb

Deployment of Machine Learning Algorithms Using Google Cloud ML Engine

12+ Real Time Industry Project

Data Science Course Completion and Project Experience Certificate ( Industry accredited certifications )

Resume Preparation And Interview guidance, Mock Interview

Job Referrals and Placement Support

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Course Highlights:

200+ hours of classroom data science training in Bangalore from Industry expert

12+ Real Time Industry Projects

300+ hours of coding Assignment & Case Studies

Weekends and Weekdays data science courses for working professional

One Year of Unlimited Flexi Classroom training Subscription

Option To Attend Multiple Batches and Backup Class

Option to attend online data science training as well.

Lifetime Access To LMS and Recorded Sessions.

Special Assistance To Non programming/Non Technical Domain.

Job Referrals and Assistance Program.

Interview Preparation and Mock Interview for data science domain

Data Analytics and Artificial Intelligence Certification from Industry

0% EMI on Major Credit Card

Card-less EMI(6 Months) Available(Subjected to Loan Approval)

Different Modes and Batch Schedule of data Science Training in Bangalore:

We provide Job Oriented Classroom data science training in Bangalore for working professionals With industry accredited certification ,Real Time Projects and Job Assistance.Apart from attending training classroom, you get option to attend training live online mode. We arrange a backup classes in case you have missed any sessions in your batch.Also ,Data science training subscription make sure that you have access to classroom sessions for one year.Recordings of the data science sessions are shared to you which will help you to revise the concepts.Lifetime Access of LMS is provided.Multiple batch option to choose from weekdays and Weekend sessions.

About Data Science training Courses in Bangalore by Learnbay

Data Science is a multidisciplinary field of study involving uncovering findings from unprocessed data. These data are used to mine and understand complex behaviours, customer trends, and business inferences. It’s all about unearthing hidden insights that can help enable businesses make smarter decisions.

Requirement of college freshers and working professionals is different.Working Professional having experience in any other domain need a course which includes relevant data science modules, data analytics tools and real time projects from multiple domains like finance, Banking, Insurance, Healthcare, Pharma ,Retail.As a working Professional to change your domain to data science ,you need to be proficient in Python ,R, SAS, Tableau ,Tensor-flow and Machine Learning Concepts.We at Learnbay has designed our data science training in Bangalore which is suitable for professionals.We offer industry certification ,Real Time projects and job referrals to tier 1 companies and banks for data science roles.Course helps you to learn end to end data science concepts from python basics to Machine learning ,Deep learning techniques and Deployment of Machine learning models on Cloud using google cloud platform engine.

What is data science?

Its a Data-driven world out there. Companies are investing in data-driven technologies to reach their target audience. Be it AI or automation, the world is becoming smarter and the companies have to keep up with the world. As a matter of fact, companies are hiring Data Scientists, to generate valuable information from raw big data. According to a recent research by IBM, the demand for Data Scientists will increase by 28% by 2020. Data science is a boon to businesses, but why is it so?

Data science in recent years has created a huge impact in almost all the industries and sectors like banking, finance, insurance, healthcare, manufacturing, retail etc.As a result data analytics and artificial intelligence became top priority in all the organisations. With advancement in technology ,there are terabytes of data that are being generated and collected over internet.These data are very useful for business if analysed properly.Data Analytics and data science helps to take business decision and predict the outcome by analysing the data.More advance techniques are machine learning ,Deep learning and artificial intelligence.

As data science is new domain and there is a shortage of resources in the field of Data science, finding a job is easier and its less difficult compared to any other domain and technologies.Assurance of Job is very much there in the Data Science domain. If you are very good data science, then there are many jobs available like data scientist ,Data Analyst ,Machine learning engineer, Artificial Intelligence engineer, Data engineer , Data Architect etc.

Why Data Science ? Job Opportunities And Career Options in data science domain.

With many job openings , the role of a data scientist has become the hottest job of the decade.Data Science Job roles and designations are the most predominant in the job market currently. Because they are ‘High Demand, Less Supply’. There are shortage of resources in data science domain. Because the salaries are higher compared to other domain and technologies. Because it is easy to start a career in data analytics and artificial intelligence.

How Learnbay Data Science training in Bangalore help you?

There are many institutes in Bangalore for data science and machine learning certification but We at Learnbay offers instructor led classroom data science training in Bangalore with industry accredited certifications, real time project and job assistance.Our instructors are industry experts and working as data scientist.Our data science course is designed for working professionals and apart from classroom ,its available in live online mode also so that in case you are not able to travel to classroom due to some issues ,you can attend the training in live online mode from your home. Learnbay provides you the practical and job oriented data science training in Bangalore with placement assistance which will help you to crack data science interview and start your career in this new domain.All the modules like Python programming, Statistics ,R, Machine learning are covered in depth with real time use cases and project.



Real Time Projects in Data Science course:

Project 1: Marketing Domain:

Customer Conversion / Segmentation

Problem: A bank Facing Challenges With Lead Conversion

Description: Identify the leads’ segments having a higher conversion ratio (lead to buying a product) so that organisation can specifically target these potential customers through additional channels and re-marketing.

Project 2 : Banking Domain

Credit Risk Analytics

Problem: efficiently build or validate in-house machine learning models for credit risk management.

Description:

Create a classifier that leverages financial information from bank accounts to estimate customer risk.

Project 3 : Project on Natural Language Processing

Problem : training a machine learning model that classifies a given line of text as belonging to one of the books/Articles. developing a machine learning model (deep learning preferred) for the same.

Project 4 : Price Analytics

This is data science project for Creating auto calculating pricing model

Problem: build an algorithm that automatically suggests the right product prices

Project 5 : Classifying Loan Application

Problem : Work With credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications

Project 6 : Identify And Predict Customer churn in telecom industry

Description:Data science project to Understand the customer behaviour and reasons for churn.Apply multiple classification models to predict the customer churn in telecom industry

Project 7:Retail Domain

Data Science Project For Coupon Purchase Prediction Project

Description:Understand Retail Transactional Data set And Using past purchase and browsing behaviour of customers ,create a machine learning model which Predict which coupons a customer will buy in a given period of time.

Project 8 : Predict Credit Default

Description:predict borrowers chance of defaulting on credit loans by building a credit score prediction model.Develop a good prediction model for a bank so that they can provide maximum credit to individual without exceeding the risk threshold.

Project 9 : Manufacturing And Production

Data Science Project to Predict Internal Failures Using Production Line Dataset

Description:Understanding about Manufacturing domain and its failures. Use production line dataset to predict internal failures using thousands of measurements/tests made for each component along the assembly line

Project 10 : Insurance Purchase Prediction

Description:Predicting which insurance option the customer will choose.Building machine learning models and Using a customer’s shopping history, can you predict what policy they will end up choosing?

Project 11 : HR Analytics Employee Attrition & Performance

Description:Data Science project to Predict attrition of valuable employees of an organisation .

Project 12 : Implement Back-Propagation Algorithm for Classification Problems

Description:Implement Back-propagation Algorithm from scratch for classification problems.After this project you will have understanding of “How to apply the back-propagation algorithm to a real-world predictive modeling problem.”

All these 12 Projects are included in our data science course.

Why to opt for our data science training in Bangalore?

Bangalore is also known as Silicon Valley of India and is the major hub for IT companies.Since there are many companies from IT , consulting, Manufacturing ,Pharma industry there are many jobs available in Bangalore especially in IT industry compared to rest of India. So, If you are pursuing data science courses in Bangalore, you get more job opportunities and also you get a chance to get trained from experienced instructor and data scientists.So,Opting for Data science training in Bangalore is a good decision in terms of job opportunities and quality.

Note: Data Science Course Update : Tensor Flow And Deep Learning With Projects is added to data science Course

Who Should Attend:

Those who want to become master in data science and Data Analytics in R Programming.

Business Analysts who want to learn machine learning

Data Analysts who wants to improve their skills.

Developers aspiring to become data scientist.

Freshers/Experienced Professional,Managers,IT professional

Prerequisite For Data Science Course:

There is no prerequisite for this course.If you are new to data science, this course is best for you.Basic Understanding of statistics/Linear Algebra/Probability and R/Python will help.

Duration Of Course – 200+ Hours ( Around 6 months) Instructor led Classroom Training

Course Features:

Live Classroom data science training in Bangalore by industry experts Classes with 40% theory and 60% hands on Trainers having more than 10+ years of experience in multiple domains like finance,Healthcare ,Retails.Practical Approach With Mini Projects And Case studies.

Job Assistance And Placement Support After end of Course.

TABLE OF CONTENTS

Introduction to data science: What is data Science? – Introduction.

Importance of Data Science.Demand for Data Science Professional.

Brief Introduction to Big data and Data Analytics.

Lifecycle of data science.

Tools and Technologies used in data Science.

Business Intelligence vs Data Science.

Role of a data scientist.

PART A – R PROGRAMMING BASICS

1. Introduction to R R Basics, background.

Comprehensive R Archive Network

Demo of Installing R On windows from CRAN Website

Installing R Studios on Windows OS

Setting Up R Workspace.

Getting Help for R-How to use help system

Installing Packages – Loading And Unloading Packages 2. Starting with R : Getting familiar with basics Operators in R – Arithmetic,Relational,Logical and Assignment Operators

Variables,Types Of Variables,Using variables

Conditional statements, if-else(),switch

Loops: For Loops,While Loops,Using Break statement,Switch 3. The R Programming Language- Data Types And Functions Use R for simple maths, creating data objects from the keyword.

How to make different type of data objects.

Understand the various data types that the language supports.

Introduction to Functions in R

Types of data structures in R

Arrays And Lists- Create Access the elements

Vectors – Create Vectors, Vectorised Operations,Power of Vectorised Operations

Matrices- Building the first matrices,Matrix Operations, Subsetting, visualising subset,Visualising with Matplot

Factors – Creating a Factor

Data Frames- create and filter data frames,Building And Merging data frames. 4. Functions And Importing data into R Function Overview – Naming Guidelines

Arguments Matching,Function with Multiple Arguments

Additional Arguments using Ellipsis,Lazy Evaluation

Multiple Return Values

Function as Objects,Anonymous Functions

Importing and exporting Data into R- importing from files like excel, csv and minitab.

Import from URL and excel Files

Import from database. 5. Data Descriptive Statistics,Tabulation,Distribution Summary Statistics for Matrix Objects. apply() Command.

Converting an Object into a Table Histograms, Stem and Leaf Plot, Density Function. Normal Distribution 6. Graphics in R – Types of graphics Bar Chart,Pie Chart,Histograms- Create and edit.

Box Plots- Basics of Boxplots- Create and Edit

Visualisation in R using ggplot2.

More About Graphs: Adding Legends to Graphs Adding Text to Graphs, Orienting the Axis Label



PART B – INTRODUCTION TO SQL

1. Introduction to SQL Server and RDBMS Covers an overview of using relational databases.

You’ll learn basic terminology used in future modules,

SQL Server Management Studio is the primary tool used to create queries and manage objects in SQL Server databases 2. SQL Operations 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 and MANY MORE….. 3. SQL Advance -Operations Data Aggregations and summarising the data

Ranking Functions: Top-N Analysis

Advanced SQL Queries for Analytics

PART C- PYTHON FOR DATA SCIENCE

1. Python Programming Basics Installing Jupyter Notebooks

Python Overview

Python 2.7 vs Python 3

Python Identifiers

Various Operators and Operators Precedence

Getting input from User,Comments,Multi line Comments. 2. Making Decisions And Loop Control Simple if Statement, if-else Statement

if-elif Statement.

Introduction To while Loops.

Introduction To for Loops,Using continue

and break 3. Python Data Types: 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 4. Functions And Modules 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 5. File I/O And Exceptional Handling Opening and Closing Files

open Function, file Object Attributes

close() Method ,Read, write, seek.Exception handling, the try-finally Clause

Raising an Exceptions,User-Defined Exceptions

Regular Expression- Search and Replace

Regular Expression Modifiers

Regular Expression patterns, re module 6. Numpy Introduction to numpy. Array Creation,Printing Arrays

Basic Operations- Indexing, Slicing and Iterating

Shape Manipulation – Changing shape, stacking and splitting of array

Vector stacking 7. Pandas And matplotlib 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.

matplotlib – Introduction, plot(),Controlling Line Properties,Working with Multiple Figures,Histograms

PART D- INTRODUCTION TO STATISTICS

1. Fundamentals of Math and Probability Basic understanding of linear algebra, matrices, vectors

Addition and multiplication of matrices

Fundamentals of Probability

Probability distributed function and cumulative distributed function.

Class Hand-on

Problem solving using R for vector manipulation

Problem solving for probability assignments 2 Descriptive Statistics Describe or summarise a set of data

Measure of central tendency and measure of dispersion.

The mean, median, mode, curtosis and skewness

Computing Standard deviation and Variance.

Types of distribution.

Class Hands-on:

5 Point summary BoxPlot

Histogram and Bar Chart

Exploratory analytics R Methods 3. Inferential Statistics 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

T-Distribution

Basics of Hypothesis Testing

Type of test and rejection region

Type of errors in Hypothesis resting , Type-l error and Type-ll errors

, Type-l error and Type-ll errors P-Value and Z-Score Method

T-Test, Analysis of variance(ANOVA)

Analysis of Co variance( 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, ANCOVA 4. Hypothesis Testing Hypothesis Testing

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

PART E- UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING

1. Introduction To Machine Learning What is Machine Learning?

What is the Challenge?

Introduction to Supervised Learning,Unsupervised Learning

What is Reinforcement Learning? 2. Linear Regression 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 3. Logistic Regression 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, Odd’s Ratio And ROC Curve

Advantages And Disadvantages of Logistic Regression.

Case Study:To classify an email as spam or not spam using logistic Regression. 4. Decision Trees And Supervised Learning Decision Tree – data set

How to build decision tree?

Understanding Kart Model

Classification Rules- Overfitting Problem

Stopping Criteria And Pruning

How to Find final size of Trees?

Model A decision Tree.

Naive Bayes

Random Forests and Support Vector Machines

Interpretation of Model Outputs

Case Study:

1 Business Case Study for Kart Model

2 Business Case Study for Random Forest

3 Business Case Study for SVM 5. Unsupervised Learning Hierarchical Clustering

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

Principal Component Analysis(PCA)- Data

Independent components analysis(ICA)

Independent components analysis(ICA) Anomaly Detection

Recommender System-collaborative filtering algorithm

Case Study– Recommendation Engine for e-commerce/retail chain 6. Introduction to Deep Learning Neural Network

Understanding Neural Network Model

Understanding Tuning of Neural Network

Case Study:

Case study using Neural Network 7. Natural language Processing Introduction to natural Language Processing(NLP).

Word Frequency Algorithms for NLP

Sentiment Analysis

Case Study :

Twitter data analysis using NLP 8. Apache Spark Analytics What is Spark

Introduction to Spark RDD

Introduction to Spark SQL and Data-frames

Using R-Spark for machine learning

Hands-on :

: installation and configuration of Spark

Hands on Spark RDD programming

Hands on of Spark SQL and Data-frame programming

Using R-Spark for machine learning programming 9. Introduction to Tableau/Spotfire Connecting to data source

Creating dashboard pages

How to create calculated columns

Different charts

Hands-on:

Hands on on connecting data source and data cleansing

Hands on various charts

Hands on deployment of Predictive model in visualisation

PART F – Deep Learning And TensorFlow

1. Introduction to Deep Learning And Tensor Flow Neural Network

Understanding Neural Network Model

Installing TensorFlow

Installing TensorFlow Simple Computation , Constants And Variables

Types of file formats in TensorFlow

Creating A Graph – Graph Visualisation

Creating a Model – Logistic Regression Model Building

TensorFlow Classification Examples 2. Convolutional Neural Network(CNN)

Convolutional Layer Motivation

Convolutional Layer Application

Architecture of a CNN

Pooling Layer Application

Deep CNN

Understanding and Visualising a CNN. 3. Understanding Of TFLearn APIs Getting Started With TFLearn

High-Level API usage -Layers,

Built-in Operations,Training and Evaluation – Customising the Training Process, Visualisation APIs

Sequential And Functional Composition

Fine tuning,

Using TensorBoard with TFLearnProjects And Case StudiesBuilding a CNN for Image Classification

Certificates On successful Completion of Project And Assessment.

Data Science Course FAQ