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Best Software Training Institute in Chennai
Data Science Training in OMR, Chennai

Data Science Training in Chennai

Besant Technologies offers best Data Science Training in Chennai with most experienced professionals. Our Instructors are working in Data Science and related technologies for more years in MNC’s. We aware of industry needs and we are offering Data Science Training in Chennai at OMR in more practical way. Our team of Data Science trainers offers Data Science in Classroom training, Data Science Online Training and Data Science Corporate Training services. We framed our syllabus to match with the real world requirements for both beginner level to advanced level.

Our training will be handled in either weekday or weekends programme depends on participants requirement. We do offer Fast-Track Data Science Training in Chennai at OMR and One-to-One Data Science Training in Chennai. Here are the major topics we cover under this Data Science course Syllabus Introduction to R, Understanding R data structure, Importing data, Manipulating Data, Using functions in R, R Programming, Charts and Plots, Machine Learning Algorithm and Statistics.Every topic will be covered in mostly practical way with examples.

Data Science Technical Interview Questions and Answers[Latest] Data Science Interview Questions

Best Data Science Training in Chennai

Besant Technologies located in various places in Chennai. We are the best Training Institute offers certification oriented Data Science Training in Chennai at OMR. Our participants will be eligible to clear all type of interviews at end of our sessions. We are building a team of Data Science trainers and participants for their future help and assistance in subject. Our training will be focused on assisting in placements as well. We have separate HR team professionals who will take care of all your interview needs. Our Data Science Training Course Fees is very moderate compared to others. We are the only Data Science training institute who can share video reviews of all our students. We mentioned the course timings and start date as well in below.

Upcoming Batches

Weekday Classes Regular Batch (1Hr - 1:30Hrs) / Per Session

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8.00 AM - 9.30 AM

Weekend Classes Regular (3Hrs) / Per Session

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11.00 AM - 2.00 PM

Weekend Fast-track Classes (6Hrs - 7Hrs) / Per Session

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10.00 AM - 5.00 PM

Data Science Training Syllabus

Data Science with Python Training Syllabus

Introduction

  • Why do we need Python?
  • Program structure

Execution steps

  • Interactive Shell
  • Executable or script files
  • User Interface or IDE

Memory management and Garbage collections

  • Object creation and deletion
  • Object properties

Data Types and Operations

  • Numbers
  • Strings
  • List
  • Tuple
  • Dictionary
  • Set
  • Other Core Types
  • Conversion between data types

Statements and Syntax

  • Assignments, Expressions and prints
  • If tests and Syntax Rules
  • While and For Loops
  • Break and continue

File Operations

  • Opening a file
  • Using Files – txt ,Csv, Xlsx
  • How to connect MySQL
  • Find and replace
  • Appending to file
  • Exporting file

Functions

  • Function definition and call
  • Function Scope
  • Arguments
  • Function Objects
  • Anonymous Functions
  • Packaging Importing

OOPS

  • Classes and Objects
  • Creating object
  • Working with Class and Instance Variables Together
  • Accessing Object Variables
  • Accessing Object Functions
  • Inheritance
  • Multiple Inheritance
  • Constructor
  • Operator Overloading
  • Polymorphism
  • Encapsulation
  • Abstract class and methods

Pandas Section

  • Python Pandas – Introduction
  • Introduction to Data Structures
  • Python Pandas – Series
  • Python Pandas – DataFrame
  • Python Pandas – Basic Functionality
  • Python Pandas – Descriptive Statistics
  • Python Pandas – Indexing and Selecting Data
  • Python Pandas – Function Application
  • Python Pandas – Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Python Pandas – Working with Text Data
  • Python Pandas – Options and Customization
  • Python Pandas – Missing Data
  • Python Pandas – GroupBy
  • Python Pandas – Merging/Joining
  • Python Pandas – Concatenation
  • Python Pandas – IO Tools
  • Python Pandas – Comparison with SQL
  • Python Pandas – Dates Conversion
  • Plotting Data

Machine Learning Techniques

All algorithm will be explain by

  • What is mathematics behind it
  • Which scenario want to use
  • How it is different from other algorithm
  • How to interpret with Python
  • What insights getting out from result
  • Hypothesis Testing
  • Correlation
  • Outlier Detection
  • T-test
  • Anova
  • Chi-square
  • Linear regression
  • Multiple regression
  • Logistics Regression
  • Naïve Bayes classifier
  • K means clustering
  • Decision tree
  • SVM
  • Time series forecasting Overview

For More Details About Data Science with Python Course Click Here!

Data Science with SAS Training Syllabus

Introduction

  • Why do we need Python?
  • Program structure

Execution steps

  • Interactive Shell
  • Executable or script files
  • User Interface or IDE

Memory management and Garbage collections

  • Object creation and deletion
  • Object properties

Data Types and Operations

  • Numbers
  • Strings
  • List
  • Tuple
  • Dictionary
  • Set
  • Other Core Types
  • Conversion between data types

Statements and Syntax

  • Assignments, Expressions and prints
  • If tests and Syntax Rules
  • While and For Loops
  • Break and continue

File Operations

  • Opening a file
  • Using Files – txt ,Csv, Xlsx
  • How to connect MySQL
  • Find and replace
  • Appending to file
  • Exporting file

Functions

  • Function definition and call
  • Function Scope
  • Arguments
  • Function Objects
  • Anonymous Functions
  • Packaging Importing

OOPS

  • Classes and Objects
  • Creating object
  • Working with Class and Instance Variables Together
  • Accessing Object Variables
  • Accessing Object Functions
  • Inheritance
  • Multiple Inheritance
  • Constructor
  • Operator Overloading
  • Polymorphism
  • Encapsulation
  • Abstract class and methods

Pandas Section

  • Python Pandas – Introduction
  • Introduction to Data Structures
  • Python Pandas – Series
  • Python Pandas – DataFrame
  • Python Pandas – Basic Functionality
  • Python Pandas – Descriptive Statistics
  • Python Pandas – Indexing and Selecting Data
  • Python Pandas – Function Application
  • Python Pandas – Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Python Pandas – Working with Text Data
  • Python Pandas – Options and Customization
  • Python Pandas – Missing Data
  • Python Pandas – GroupBy
  • Python Pandas – Merging/Joining
  • Python Pandas – Concatenation
  • Python Pandas – IO Tools
  • Python Pandas – Comparison with SQL
  • Python Pandas – Dates Conversion
  • Plotting Data

Machine Learning Techniques

All algorithm will be explained by

  • What is the mathematics behind it
  • Which scenario want to use
  • How it is different from other algorithms
  • How to interpret with Python
  • What insights getting out from the result
  • Hypothesis Testing
  • Correlation
  • Outlier Detection
  • T-test
  • Anova
  • Chi-square
  • Linear regression
  • Multiple regression
  • Logistics Regression
  • Naïve Bayes classifier
  • K means clustering
  • Decision tree
  • SVM
  • Time series forecasting Overview

For More Details About Data Science with SAS Course Click Here!

Data Science with R Training Syllabus

Introduction to Data Analytics

Objectives

  • This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information.
  • You can also learn how R can play an important role in solving complex analytical problems.
  • This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
  • Also, you will learnuseof ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.

Topics

  • Business Analytics, Data, Information
  • Understanding Business Analytics and R
  • Compare R with other software in analytics
  • Install R
  • Perform basic operations in R usingcommandline
  • Learn the use of IDE R Studio
  • Use the ‘R help’ feature in R

Introduction to R programming

Objectives

  • This module startsfromthe basics of R programming like datatypes and functions.
  • In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
  • You will also learn how to apply the ‘join’ function in SQL.

Topics

  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R
  • Factors

Data Manipulation in R

Objectives

  • In this module, we start with a sample of a dirtydata setand perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
  • Thus using and exploring the popular functions required to clean data in R.

Topics

  • Data sorting
  • Find and remove duplicates record
  • Cleaning data
  • Recoding data
  • Merging data
  • Slicing of Data
  • Merging Data
  • Apply functions

Data Import techniques in R

Objectives:

  • This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a CSV file to the data scraped from a website.
  • This module teaches you various data importing techniques in R.

Topics

  • Reading Data
  • Writing Data
  • Basic SQL queries in R
  • Web Scraping

Exploratory Data Analysis

Objectives

  • In this module, you will learn that exploratory data analysis is an important step in the analysis.
  • EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.

Topics

  • Box plot
  • Histogram
  • Pareto charts
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing Graphs

Basics of Statistics & Linear & Logistic Regression

Objectives

  • This module touches the base of Descriptive and Inferential Statistics and Probabilities & ‘Regression Techniques’.
  • Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.

Topics

  • Basics of Statistics
  • Inferential statistics
  • Probability
  • Hypothesis
  • Standard deviation
  • Outliers
  • Correlation
  • Linear & Logistic Regression

Data Mining: Clustering techniques, Regression & Classification

Objectives

  • Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
  • The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types.
  • We will also discuss the process involved in ‘K-means Clustering’, the various statistical measures you need to know to implement it in this module.

Topics

  • Introduction to Data Mining
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms
  • K- means clustering

Project work

  • 2 Real-time project
Having Query with the Course Want Some Help? Talk to Advisor.

Data Science Training in Chennai

Our Data Science Trainers

  • More than 10 Years of experience in Data Science Technologies
  • Has worked on multiple realtime Data Science projects
  • Working in a top MNC company in Chennai
  • Trained 2000+ Students so far
  • Strong Theoretical & Practical Knowledge
  • Data Science certified Professionals

Batch Size of Data Science Training in OMR, Chennai

Regular Batch ( Morning, Day time & Evening)

  • Seats Available : 8 (maximum)

Weekend Training Batch( Saturday, Sunday & Holidays)

  • Seats Available : 8 (maximum)

Fast Track batch

  • Seats Available : 5 (maximum)

Our Students are working in

Avnet
Contus Support
Cognizant
NTTDATA
Prodapt
Span Technologies

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