data science course in bangalore

Data Science Training in Bangalore

4.89 out of 5 based on 19 customer ratings
(19 customer reviews)

Rs.52,000.00

600 Learners Enrolled

Course details:
Classroom Training: 200+ Hrs
Case Studies: 10 
Project:  12 Real Time Projects

  • S : Oct 27th Onwards
Select Batch and time
Weekend ,21st Apr ,2019,09:00 Am-12:30 pm
Weekday ,1st Apr, 2019,08:00 Am-10:30 Pm

Course description

Data Science Training in Bangalore

Learnbay Provides data science training in Bangalore from certified experts.Our course helps you to learn various data analytics techniques using R and Python programming.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.If you are looking for data science training in Bangalore, Enroll for the Free Demo Session.
This course will benefit you to master data science skills and will help you to to handle interview with more confidence if you are looking for job in data science domain.

 

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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

 

View Updated Data Science Course Info

 

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 Modules :
Python Programming
R Programming
Advance Statistics
Machine Learning
Deep Learning And Tensor Flow
SQL Basics
Hadoop/Apache Spark
Tableau/spotfire
5 Real Time Project,Interview Prep And Resume Guidance

 

Chat With Our Course Advisor Now

 

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
  • 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)
  • 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
  • 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 Classificatio

Certificates On successful Completion of Project  And Assessment.

 

 

Why Should you choose a career in Data Analytics in this era?
 
It enables you make more better and right decision. 
It helps you to solve complex problems in easy way. 
It is easy to measure the effects and after effects using data science.
It is more action oriented than theory. 
It promises you best career ahead. 
 
 
Can you become a data scientist ?
 
Answer is Yes, you can. 
Whether you are from commerce background or from science college, you can become an specialist in data analytics. 
Professionals such as business analyst want to get growth in career. 
Any developer or programmer in software, regardless of the programming language they have command in. 
Freshers- just out of college or working professional. 
 
 
What do you need to become an expert in data science ?
 
Are you passionate about growth in your career? 
Do you love studying numbers ?
Do you enjoy solving problems ? 
 
If YES !
Then sure you can learn and become an expert in data analytics/science. 
 
 
How long does it take to become certified in data science ?
 
Around 130 Hrs, ie about 4 and a half month with classroom training. 
 
‘Make an enquiry for this course’ to “Still have doubts, Contact us now”

 

Course Video

Click below link to watch sample recordings : http://www.learnbay.co/data-science-course

Course FAQ

1. What are the profiles of Trainers?
Our Trainers have relevant industry experience and are working in MNC as data scientist.

2.How many Case studies and Projects are covered in the course?
Course has multiple case studies and mini Project.Our course is designed by industry experts.
Course features many real time problems.Please refer course content for more details.

3. Do i Need to carry my own laptop?What the the softwares required?
Yes,You need to carry your own laptop.To start with ,You need to install R And R studio installed in your system.
Both Of these are open source and in first class,trainer will help you to setup the environment in your system.

4. Can i Attend a Demo Session before enrolling for the course?
Of course,You can attend a Free live Demo Session before enrolling for the Course.

5. Are the session Online or Classroom?
We provide both live Online and classroom session.You can opt for online or classroom based on your
convenience.

6. Will i Get Class Recording if i Enroll for Classroom Session?
Our live Classroom Sessions are recorded and after the session ,Class recording will be shared to you.

19 reviews for Data Science Training in Bangalore

  1. 5 out of 5

    :

    .

  2. 4 out of 5

    :

    I would like to recommend any one who wants to be a Data Scientist.Explanations are clean, clear, easy to understand. Their support team works very well such any time you have an issue they reply and help you solving the issue.Good trainer for Machine Learning and R.

  3. 4 out of 5

    :

    I opted for data Science classroom Program.Trainers are good and have industry experience in data science domain.Content is well organized and real time case studies are covered.Got very good response from support team .Thanks Abhisek for your support.

  4. 5 out of 5

    :

    I liked the data science course by learnbay. They provide both Python And R Programming .Also Course has SQL,Introduction to Apache Spark And Tableau.Many Case studies and Project helps a lot.
    I would suggest working professional who wants to start their career in data science domain can enroll for the course.

  5. 5 out of 5

    :

    Good mixture of theoretical and practical training.Course helped me in all areas and understanding the vital concepts of machine learning and statistics.Very Flexible timing.

  6. 5 out of 5

    :

    Whole experience was great.Instructor carries good knowledge in subject and has rich experience in data science domain.

  7. 5 out of 5

    :

    I pursued a course on Data science at Learnbay. Even though was from a non programming background, the instructors at the institute made sure i understood the concepts of programming(Python and R) very clear. The syllabus is also neatly structured starting from the basics of Python and R followed by in depth study of statistics. Utkarsh Sir was amazing in his teaching and the way he approached the concept was very intriguing. The major part of this course involves Machine learning and Utkarsh Sir did an amazing job in making us understanding the concept. We also worked on real time data sets and applied Machine learning algorithms to it. It’s an amazing opportunity for people who aspire to start their career as a Data scientist and learn more about this diverse field

  8. 5 out of 5

    :

    Very Good Institute to start your career in Data Science field. “Highly recommended”. Have completed the training on data science from “Learnbay” and can say only one word that those who wants to learn they should join the “LEARNBAY”. Trainers are industry experts and provide detailed explanation on every topic with real time examples and case studies by Utkarsh Sir. Utkarsh is really good faculty member in learn bay because he face actual problems in machine learning at company level. he realize ,what happen in Data Sciences and very knowledgeable person in Data sciences truly i realize.

  9. 5 out of 5

    :

    Joined Learn Bay for Data Science using Python and R.
    Very good institute with supportive Instructors. They make an effort to clear your doubts and questions. Staff members are also very supportive.
    Provides online/youtube recorded sessions which is helpfull to go thorugh again and again for some clarification.
    You can attend sessions from home as well, if you cannot make it to classroom.

    About Utkarsh sir

    – His teching is very simple and effective
    – Considering Machine Learning he has the ability to explain complex concepts in simple way which helps most of us to understand
    – Correlats examples and case studies with real time scenerios
    – Covers real time interview questions considering topics during the class itself which is helpfull
    – Covers Resume tips, additional information/developments about Machine Learning, current indusrty requirements whenever required and this is important for clearing interviews
    – Helped to work on couple of Algorithms with real Data set

    Finally Thankfull to Learnbay and Utkarsh sir

  10. 5 out of 5

    :

    Would like to recommend any one who wants to switch to data science domain and looking for a classroom training in Bangalore.Course content and trainers are top notches.I like the way of teaching of Python And Machine Learning trainer.Number of case studies from every machine learning Algorithms and real time business problems and examples helped me to understand the concept easily.Course is very organised from starting till the end.
    Overall very Happy and Would definitely recommend others

  11. 5 out of 5

    :

    I joined data science course with Learnbay .Course is well designed for beginners.Materials and course content is good.Trainer’s are good and his methods of explaining complex machine learning problem is awesome.Coming from non programming background ,they helped me to learn python and R programming from basics.Assignments are well designed.There are lots of class hands-on which will help you to learn the concepts properly
    Trainers are explaining the real life scenario using modeling and statistics techniques.

  12. 5 out of 5

    :

    Enrolled for data science course at learnbay in July 2018.Learnbay helped me to learn data science with real time project which helped me to land a job in data science domain in reputed MNC.The best thing I liked about them is option to attend multiple batches and projects so that you can revise important topics in other batches also.
    Thanks Learnbay

  13. 5 out of 5

    :

    I took course on data science with learnbay .Course was well organised and content is excellent .Instructor is very knowledgeable and good. Real time projects and case studies helps a lot in interviews.Would suggest to add few projects on deep learning and computer vision.
    Overall good experience

  14. 5 out of 5

    :

    Course is actually quite good. I learned a lot and some of the instructors are exceptionally well and if you really pay attention, it can boost your career by great extent.Course content is very well structured and detailed.I Would recommend Learnbay to IT professionals who are looking to change their domain to data science.
    Thanks Learnbay for great learning experience.

  15. 5 out of 5

    :

    I took Data Science Course from Learnbay .The training program is really nice. Course content is good .Instructor is experienced and having patience to clear all the doubts in class itself. Projects are good.Best thing about course is you can attend multiple classroom batch and finish the course at your pace.Also ,You have option to attend the training online from home if you are not able to travel to classroom.Instructor utkarsh is really good and he will teach complex machine learning algorithm in very simple way.
    Overall good experience.
    Thanks Learnbay

  16. 5 out of 5

    :

    I have completed Data Science Training from Learnbay. This is job oriented course with updated course modules as per the industry requirement.Course is more practical with project .I would definitely recommend to working professionals who is serious about learning and changing his current domain to data science.

  17. 5 out of 5

    :

    Learnbay helped me to learn data science and helped to build my portfolio with real-time projects.I got a job in reputed IT company as data analyst.Course is suitable for working professionals.

  18. 5 out of 5

    :

    Learnbay is a great platform to study Data Science Course. Both online as well as Class room training sessions had awesome learning atmosphere. The Content of the course is well designed according to the industry needs. Even the project sessions helped me a lot to gain experience and understand the concepts.

  19. 5 out of 5

    :

    I have joined data science course at Learnbay in the month of Jan. The way the course modules are structured is great. They have very well thought about the changes in the industry and designed the course accordingly. The trainers here are also great for the way they train. I have taken up classes from Mr. Pankaj, Mr. Amritansh and Mr. Utkarsh and the way they train is what i really like about.

  20. :

    Thanks Tarun.

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