Data Science Training in Chennai

Data Science Course in Chennai

Data Science Training

There is an immense rise in the scope of data science and resultantly it has paved way for data science courses in Chennai. Though there are several data science institutes that offer Datascience Training in Chennai, SLA shines in the crowd because of its unbeatable quality of training.

SLA provides the best data science course with proficient faculties who have experience of several years. Practical training is our unswerving focus and this helps to mold the skills of the candidates in a better manner. Big data, which is the application of data science, is also concentrated in our course and it will have impact on the students.

Why Should you Take Data Science Training?

Data science has turned out to be one of the important careers in the IT industry. Virtually every enterprise consist of big data and the interesting thing is that most of them want to make best use of this technology. Here comes the role of data scientist.

Data Scientists are well aware of the application of their skills in mathematics, statistics, programming etc. They make use of their knowledge to bring out solutions hidden in the data. Data Science Training Institute in Chennai as SLA provides you with in-depth training on the Data Science concepts.

Prominent Features of Data Science Course in Chennai

Softlogic Academy offers Data Science Certification Course in Chennai with a focus on quality training.

Being in the industry for over 20 years, Softlogic understands the exact requirements of the candidates. Whether it is fresher or seasoned professional, we customize the training according to the flexibility of the candidates. Besides this, listed below are the prominent features of the course offered by us:

  • 60 hours course period
  • Highly proficient faculties who are familiar with the industry standards
  • 100% job-focused training
  • Dedicated placement team
  • Small batch sizes for individual attention

Benefits of Data Science Training

The Aspiring candidate would be wondering what is the exact benefit of studying data science course. In this data-rich world where huge sets of data are generated every second, the role of data scientists is gaining momentum.The manager can arrive at swift and intelligent decisions while collaborating with data scientists.

You can expect new challenges with the help of the dynamic data. You can also set forth policies for best practices with the help of the insight got through the data and the data scientist plays an important role here. Moreover, since all the organizations aim at achieving perfection, the decisions can be tested in a meticulous manner.

Datascience is not restricted to a single domain but encompasses diverse tools and techniques so as to get the appropriate data. Both humans and machine contribute to the data science. While for some tasks humans outdo machines, for some tasks machines outdo humans.

A Data Scientist deals with big numbers and makes sense with the numbers. Here then, good understanding of mathematics and statistics is essential.

Data Science Center in Chennai as SLA helps you to commence your career as a data scientist by imparting the fundamentals of the Concept.

Data Science Course Duration and Course Fees

You can reach our counselors for More Details about Data Science Course Fees and Training duration.

Data Science Course Duration
TrackRegular TrackWeekend TrackFast Track
Course Duration30 – 40 Days5 WeekendsAccording to your Convenience
Hours2 hours a day6 hours a dayFits your Requirements
Training ModeLive ClassroomLive ClassroomLive Classroom

Prerequisites to Learn Data Science Course:

Though understanding of mathematics especially statistics is a basic prerequisite for data science course it is advantageous if you also have the following knowledge:

  • Have a sound knowledge of the basics of programming
  • Have good understanding of the basics of SQL
  • Acquainted with the fundamentals of maths, statistics and probability
  • Have sound knowledge about the data analytical tools

Top Data Science Institute in Chennai as SLA hones your skills in such a way that the above knowledge complement with the knowledge that you are gaining now.

Who Should Attend?

A data scientist can be coming from an engineering degree or he/she can even be a PhD. This doesn’t indicate that you cannot take up a data science course if you are not suiting the above requirement.

  • IT Professionals who are interested to migrate their career to Data Science and Analytics
  • Fresh graduates who are inclined to work in Data Science and Analytics
  • IT Managers, DataBase Administrators, delivery heads etc. can take up Data Science Course.

Data Science Course Syllabus

Data Science Course Syllabus in SLA is prepared keeping in mind the Requirements of the Candidates, also devised according to the industry standards.

Introduction to Data warehousing

  • Types of Scripts
  • Difference between Script & Programming Languages
  • Features of Scripting
  • Limitation of Scripting
  • Types of programming Language Paradigms

Introduction to Python

  • Who Uses Python?
  • Characteristics of Python
  • History of Python
  • What is PSF?
  • Install Python with Diff IDEs
  • Features of Python
  • Limitations of Python
  • Python Applications

Different Modes in Python

  • Python File Extensions
  • Python Sub Packages
  • Uses of Python in Data Science
  • Working with Python in Unix/Linux/Windows/Mac/Android

Python New IDEs

  • PyCharm IDE
  • How to Work on PyCharm
  • PyCharm Components
  • Debugging process in PyCharm
  • PYTHON Install Anaconda
  • What is Anaconda?
  • Coding Environments
  • Spyder Components
  • General Spyder Features
  • Spyder Shortcut Keys
  • Jupyter Notebook
  • What is Conda?
  • Conda List?
  • Jupyter and Kernels
  • What is PIP?

Python Sets

  • How to create a set?
  • Iteration Over Sets
  • Python Set Methods
  • Python Set Operations
  • Union of sets
  • Built-in Functions with Set
  • Python Frozenset

Python Dictionary

  • How to create a dictionary?
  • Python Dictionary Methods

Python OS Module

  • Shell Script Commands
  • Various OS operations in Python
  • Python File System Shell Methods

Python Exception Handling

  • Python Errors
  • Common Run Time Errors in PYTHON
  • Exception Handling
  • Ignore Errors
  • Assertions
  • Using Assertions Effectively

More Advanced PYTHON

  • Python Iterators
  • Python Generators
  • Python Closures
  • Python Decorators
  • Python @property

Python XML Parser

  • What is XML?
  • Difference between XML and HTML
  • Difference between XML and JSON and Gson
  • How to Parse XML
  • How to Create XML Node
  • Python vs JAVA
  • XML and HTML


  • What is Multi-Threading
  • Threading Module
  • Defining a Thread
  • Thread Synchronization

Web Scrapping

  • The components of a web page
  • Beautiful Soup
  • Urllib2
  • HTML, CSS, JS, jQuery
  • Dataframes
  • PIP
  • Installing External Modules Using PIP

Sequence or Collections in Python

  • Strings
  • Unicode Strings
  • Lists
  • Tuples
  • buffers
  • xrange

Python Lists

  • Lists are mutable
  • Getting to Lists
  • List indices
  • Traversing a list

Python TUPLE

  • Advantages of Tuple over List
  • Packing and Unpacking
  • Comparing tuples
  • Creating nested tuple
  • Using tuples as keys in dictionaries
  • Deleting Tuples
  • Slicing of Tuple
  • Tuple Membership Test

Advanced Python

Python Modules

  • The import Statement
  • The from…import Statement
  • Creating User defined Modules
  • Python Module Search Path

Packages in Python

  • What is a Package?
  • Introduction to Packages?
  • py file
  • Importing module from a package
  • Creating a Package
  • Creating Sub Package
  • Importing from Sub-Packages
  • Popular Python Packages

File Handling

  • What is a data, Information File?
  • File Objects
  • File Different Modes
  • file Object Attributes
  • Directories in Python
  • Working with CSV files

Python Class and Objects

  • Object Oriented Programming System
  • Define Classes
  • Creating Objects
  • Access Modifiers
  • Python Namespace
  • Self-variable in python
  • Garbage Collection
  • Python Multiple Inheritance
  • Overloading and Over Riding
  • Polymorphism
  • Abstraction
  • Encapsulation

Python Regular Expressions

  • What is Regular Expression?
  • Regular Expression Syntax
  • Understanding Regular Expressions
  • Regular Expression Patterns
  • Literal characters
  • Finding Pattern in Text (
  • Using re.findall for text
  • Python Flags
  • Methods of Regular Expressions

Unit Testing with PyUnit

  • What is Testing?
  • Types of Testings and Methods?
  • What is Unit Testing?
  • What is PyUnit?
  • Test scenarios, Test Cases, Test suites

Introduction to Python Web Frameworks

  • Django – Design
  • Advantages of Django
  • MVC and MVT
  • Installing Django
  • Designing Web Pages
  • HTML5, CSS3, AngularJS

GUI Programming-Tkinter

  • Introduction
  • Components and Events
  • Adding Controls
  • Entry Widget, Text Widget, Radio Button, Check Button
  • List Boxes, Menus, Combo Box


  • What are Data Analysis, Data Analytics and Data Science?
  • Business Decisions
  • Case study of Walmart

Various analytics tools

  • Descriptive
  • Predictive
  • Web Analytics
  • Google Analytics

Various Analytics tools

  • R and features
  • Evolution of R?
  • Big data Hadoop and R

Working with R & RStudio

  • R & R Studio Installation

Data Types

  • Scalar
  • Vectors
  • Matrix
  • List
  • Data frames
  • Factors
  • Handling date in R
  • Conversion of data types
  • Operators in R

Importing Data

  • CSV files
  • Database data (Oracle 11g)
  • XML files
  • JSON files
  • Reading & Writing PDF files
  • Reading & Writing JPEG files
  • Saving Data in R

Manipulating Data

  • Cbind, Rbind
  • Sorting
  • Aggregating
  • dplyr

Conditional Statements

  • If …else
  • For loop
  • While loop
  • Repeat loop


  • Apply()
  • sApply()
  • rApply()
  • tApply

Statistical Concepts

  • Descriptive Statistics
  • Inferential Statistics
  • Central Tendency (Mean,Mode,Median)
  • Hypothesis Testing
  • Probability
  • tTest
  • zTest
  • Chi Square test
  • Correlation
  • Covariance
  • Anova

Predictive Modelling

  • Linear Regression
  • Normal distribution
  • Density

Data Visualization in R using GGPlot

  • Box Plot
  • Histograms
  • Scatter Plotter
  • Line chart
  • Bar Chart
  • Heat maps

Data Visualization using Plotly

  • 3D-view
  • Geo Maps

Misc. functions

  • Null Handling
  • Merge
  • Grep
  • Scan

Advance Topics in R

  • Text Mining
  • Exploratory Data Analysis
  • Machine Learning with R (concept)

Started Using SAS Software

  • The SAS Language
  • SAS Data Sets
  • The Two Parts of a SAS Program
  • The DATA Step’s Built-in Loop
  • Choosing a Mode for Submitting SAS Programs
  • Windows and Commands in the SAS Windowing Environment
  • Submitting a Program in the SAS Windowing Environment
  • Reading the SAS Log
  • Viewing Your Results in the Output Window
  • Creating HTML Output
  • SAS Data Libraries
  • Viewing Data Sets with SAS Explorer
  • Using SAS System Options

Getting Your Data into SAS

  • Methods for Getting Your Data into SAS
  • Entering Data with the Viewtable Window
  • Reading Files with the Import Wizard
  • Telling SAS Where to Find Your Raw Data
  • Reading Raw Data Separated by Spaces
  • Reading Raw Data Arranged in Columns
  • Reading Raw Data Not in Standard Format
  • Selected Informats
  • Mixing Input Styles
  • Reading Messy Raw Data
  • Reading Multiple Lines of Raw Data per Observation
  • Reading Multiple Observations per Line of Raw Data
  • Reading Part of a Raw Data File
  • Controlling Input with Options in the INFILE Statement
  • Reading Delimited Files with the DATA Step
  • Reading Delimited Files with the IMPORT Procedure
  • Reading PC Files with the IMPORT Procedure
  • Reading PC Files with DDE
  • Temporary versus Permanent SAS Data Sets
  • Using Permanent SAS Data Sets with LIBNAME Statements
  • Using Permanent SAS Data Sets by Direct Referencing
  • Listing the Contents of a SAS Data Set

Working with Your Data

  • Creating and Redefining Variables
  • Using SAS Functions
  • Selected SAS Functions
  • Using IF-THEN Statements
  • Grouping Observations with IF-THEN/ELSE Statements
  • Subsetting Your Data
  • Working with SAS Dates
  • Selected Date Informats, Functions, and Formats
  • Using the RETAIN and Sum Statements
  • Simplifying Programs with Arrays
  • Using Shortcuts for Lists of Variable Names

Sorting, Printing, and Summarizing

  • Using SAS Procedures
  • Subsetting in Procedures with the WHERE Statement
  • Sorting Your Data with PROC SORT
  • Printing Your Data with PROC PRINT
  • Changing the Appearance of Printed Values with Formats
  • Selected Standard Formats
  • Creating Your Own Formats Using PROC FORMAT
  • Writing Simple Custom Reports
  • Summarizing Your Data Using PROC MEANS
  • Writing Summary Statistics to a SAS Data Set
  • Counting Your Data with PROC FREQ
  • Producing Tabular Reports with PROC TABULATE
  • Adding Statistics to PROC TABULATE Output
  • Enhancing the Appearance of PROC TABULATE Output
  • Changing Headers in PROC TABULATE Output
  • Specifying Multiple Formats for Data Cells in PROC TABULATE Output
  • Producing Simple Output with PROC REPORT
  • Using DEFINE Statements in PROC REPORT
  • Creating Summary Reports with PROC REPORT
  • Adding Summary Breaks to PROC REPORT Output
  • Adding Statistics to PROC REPORT Output

Enhancing Your Output with ODS

  • Concepts of the Output Delivery System
  • Tracing and Selecting Procedure Output
  • Creating SAS Data Sets from Procedure Output
  • Using ODS Statements to Create HTML Output
  • Using ODS Statements to Create RTF Output
  • Using ODS Statements to Create PRINTER Output
  • Customizing Titles and Footnotes
  • Customizing PROC PRINT Output with the STYLE= Option
  • Customizing PROC REPORT Output with the STYLE= Option
  • Customizing PROC TABULATE Output with the STYLE= Option
  • Adding Traffic-Lighting to Your Output
  • Selected Style Attributes

Modifying and Combining SAS Data Sets

  • Modifying a Data Set Using the SET Statement
  • Stacking Data Sets Using the SET Statement
  • Interleaving Data Sets Using the SET Statement
  • Combining Data Sets Using a One-to- One Match Merge
  • Combining Data Sets Using a One-to- Many Match Merge
  • Merging Summary Statistics with the Original Data
  • Combining a Grand Total with the Original Data
  • Updating a Master Data Set with Transactions
  • Using SAS Data Set Options
  • Tracking and Selecting Observations with the IN= Option
  • Writing Multiple Data Sets Using the OUTPUT Statement
  • Making Several Observations from One Using the OUTPUT Statement
  • Changing Observations to Variables Using PROC TRANSPOSE
  • Using SAS Automatic Variables

Writing Flexible Code with the SAS Macro Facility

  • Macro Concepts
  • Substituting Text with Macro Variables
  • Creating Modular Code with Macros
  • Adding Parameters to Macros
  • Writing Macros with Conditional Logic
  • Writing Data-Driven Programs with CALL SYMPUT
  • Debugging Macro Errors

Basic Statistical Procedures

  • Examining the Distribution of Data with PROC UNIVARIATE
  • Producing Statistics with PROC MEANS
  • Testing Categorical Data with PROC FREQ
  • Examining Correlations with PROC CORR
  • Using PROC REG for Simple Regression Analysis
  • Reading the Output of PROC REG
  • Using PROC ANOVA for One-Way Analysis of Variance
  • Reading the Output of PROC ANOVA
  • Graphical Interfaces for Statistical Analysis

Exporting Your Data

  • Methods for Exporting Your Data
  • Writing Files Using the Export Wizard
  • Writing Delimited Files with the EXPORT Procedure
  • Writing PC Files with the EXPORT Procedure
  • Writing Raw Data Files with the DATA Step
  • Writing Delimited and HTML Files using ODS
  • Sharing SAS Data Sets with Other Types of Computers

Debugging Your SAS Programs

  • Writing SAS Programs That Work
  • Fixing Programs That Don’t Work
  • Searching for the Missing Semicolon
  • Note: INPUT Statement Reached Past the End of the Line
  • Note: Lost Card
  • Note: Invalid Data
  • Note: Missing Values Were Generated
  • Note: Numeric Values Have Been Converted to Character (or Vice Versa)
  • DATA Step Produces Wrong Results but No Error Message


  • Introduction To SAS/ SQL
  • Features
  • Uses
  • Terminology
  • Data Types, Key Words, & Operators
  • Functions, Predicates
  • Formatting Output
  • Group By Clause
  • Order By Clause
  • Having Clause
  • Case Expression And Conditional Logic
  • Creating ,Populating & Deleting Tables
  • Alter Table Statement
  • Changing Column’s Length
  • Joins
  • Constraints
  • Renaming A Table & Columns
  • Views
More About Data Science with Python, Data Science with R Training, Data Science with SAS Training

Job Opportunities for Data Scientists

There is a substantial number of careers available to those people who are trained in data science. Getting trained in SLA that provides Best Datascience Training in Chennai, helps you to become a data science specialist who assists the companies in gaining insight into the big amount of data that is being generated.

Moreover, the application range for data science is very exhaustive and you can work in any domain that boosts your personal interest. Data scientists are most sought after professionals in the IT domain and big data industry.

Both startups and established companies require the skills of a data scientist. SLA is a top institute that offers the Best Course for Data Science in Chennai.

Job profiles of Data Science

The field of data science is a lucrative one and those who want to prove their mettle can have a wide spectrum of roles to choose from. Business and strategy knowledge, proficiency in mathematics and technical skills are the major specializations that a Data Scientist Needs. Lets see some of the Job Profiles of a person who has learned data science:

  • Data Scientist
  • Big Data Engineer
  • Data Engineer
  • Statistician
  • Data Administrator
  • Data Architect
  • Business Analyst
  • Business Intelligence Manager
  • Data Analyst
  • Data Manager
  • Analytics Manager
Datascience Certification

Getting trained in SLA for data science is the right step for both freshers and experienced professionals who want to migrate their career to data science. Our Data Science Certification Course in Chennai focuses on practical-oriented sessions to a great extent.

Once you get the certificate you will be ahead of the crowd. Small batch sizes, individual attention, and reasonable fees are some factors that makes SLA shine in the crowd.

Placements support is a major strength of SLA because we believe in moulding the skills of the candidate in tune with the career. SLA offers the Best Course for Data Science and the syllabus complements well with the latest advancements.

Data Science Training in Chennai from SLA trains you in the niche technologies are that are in demand. You can also get hold of the lucrative data science job title with big data skills and specialization in R Programming, SAS and Python Analytics. You might aspire to become a Google data scientist and get the best salary. But for this you require a strong foundation. It’s the best time that you learn data science from SLA which is the right destination to kick start your amazing data science journey.

Data Science Certifications Across the World

Following are some of the Data Science Certifications Available Across the World:

  • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Certified Analytics Professional (CAP)
  • Big Data Certification, UC San Diego Extension School
  • Cloudera Certified Professional: CCP Data Engineer
  • Cloudera Certified Associate – Data Analyst
  • Certification in Data Science, Georgetown University School of Continuing Studies
  • Certification of Professional Achievement in Data Sciences, Columbia University
  • Data Science A-Z: Real Life Data Science Exercises
  • Data Science Certificate, Harvard Extension School
  • Dell EMC Proven Professional
  • Data Science for Executives, Columbia University
  • Microsoft Professional Program in Data Science
  • Microsoft Certified Solutions Expert
  • Springboard Introduction to Data Science
  • SAS Academy for Data Science
Students Speak

I was doing Data Science Training Course in Chennai, and SLA is recommended by many friends! Since I heard this institute was the best, I joined here and took up a data science training! The classes were absolutely wonderful! And they do offer strong placement assurance for freshers who are looking for jobs. They offer complete coaching, coupled with interview training! I can’t think of another institute which offer wholesome training experience than SoftLogic academy!

— Malathi

Highly recommend Softlogic Academy as this is for sure a very good Data Science Training Institute in Chennai! Their teaching methodology is really good and also, the instructors are very knowledgeable! If you are a fresher, they take so much consideration and also ensure your basics are done right! I was in advanced level, and I am now able to handle corporate projects by own! Their post training support is the best, I have ever seen!

— Kayalvizhi

Data Science Training Centre in Chennai with reasonable fees and excellent placement assistance. After I had my corporate training, I enrolled into an online training program to fit my schedule. Excellent course content and the training sessions were totally excellent! So happy I came to know about this institute through my corporate training program!

— Riyaz

I was looking for Data Science with Python Training in Chennai, and I am so glad to have chosen this training institute for doing my data science course. I was initially very sceptical about joining here, but I have to say that that tutors and the way the classes were handled were really awesome! One of the things I noticed during the training session was that, this place is a great centre for both beginners and advanced professionals! They inspire so much confidence and make the classes very lively! Awesome place to learn if you are looking for Best Data Science Training in Chennai!

— Krishnan J