Data Science Training in Chennai


World-class Certified Data Science course in Chennai using R, Python, SAS, Stata, Hadoop, Advanced SAS (SQL and Macros) and Machine Learning for Data Science and Analytics.

Best Data Scientist training in Chennai classes taught by experienced Cheif Data Scientist with extensive real-world experience in leading analytical tools.

Awarded as the Best Data Science Training Center in Chennai - We Guarantee Your Data Science Training Success in Chennai.


About The Trainer

- Karthik work as an Data Scientist & Instructor, He has over 11 years of analytics experience working with companies like Capital One, Walmart, ICICI Lombard etc and recognized expert in analytics using R & Python.

Mr. Karthik specializes in SAS, R, Python, Machine leaning and algorithms, MATLAB, Big Data: Hadoop (Hive,Pig,casandra,Hbase), SPSS and Advanced SAS (SQL and Macros).

Qualification: M.S. in Statistics

Membership American Statistical Association


Flexible Timings / Weekend classes Available.

Talk to the Trainer @ +91-8939915577

Certified Data Science and Machine Learning Training Course Fee

FREE Demo Session: Try two FREE CLASS to see for yourself the quality of training.



Data Science Training Course Syllabus in Chennai


  • Data Science with SAS
  • Data Science with R
  • Certified Data Scientist with Python course
  • Data Science with Advanced SAS: Macros & SQL
  • Machine Learning for Data Science and Analytics


Data Science course in Chennai using R


Every case study gives you a chance to work on large, real-life business data sets in the advanced R lab.


  • Programming with R
  • Advanced R Features
  • Using R Data Frames to solve complex tasks
  • Use R to handle Excel Files
  • Web scraping with R
  • Connect R to SQL
  • Use ggplot2 for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with R, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Data Mining Twitter
  • Neural Nets and Deep Learning
  • Support Vectore Machines
  • and much, much more!

Enroll in the course and become a data scientist today!



Data Science training in Chennai with SAS


World-class Certified SAS Training Institute in Chennai for Base SAS, Advanced SAS, Clinical SAS, Analytics, Business Intelligence, Data Management, and Predictive Analytics


  1. Base SAS
  2. Advance SAS
  3. Clinical SAS Programming
  4. Predictive Modeling
  5. Clinical Data Manager
  6. Platform Administrator
  7. SAS Visual Analytics
  8. SAS Data Integration Studio
  9. BA Data Mining with Statistics
  10. Business Intelligence
  11. Data Management and Reporting


Data Science training course in Chennai with Python


Learn to use Python as your Data Science tool of choice This course teaches you Python as a tool for data science, and specifically for implementing an advanced Machine Learning algorithm with Python


I. Introduction and Setting Up Your Integrated Analysis Environment

Setting Up Your Integrated Analysis Environment & Tools Overview

  • IPython Shell
  • Custom environment settings
  • Jupyter Notebooks
  • Script editor
  • Packages: NumPy, SciPy, scikit-learn, Pandas, Matplotlib, Seaborn, etc.

Once you complete this module, you will understand some of the unique benefits of using Python for data science / what features make Python particularly well-suited for data science, you will be able to set up a fully functioning Python-based analysis environment, and you will know what each tool is used for in the data science workflow.

II. Using Python to Control and Document Your Data Science Processes

Python Essentials

  • Data types and objects
  • Loading packages, namespaces
  • Reading and writing data
  • Simple plotting
  • Control flow
  • Debugging
  • Code profiling

Once you complete this module, you will be able to use the Python standard library plus Canopy tools to write, run, debug, and profile programs that control your data science processes (which draw on the scientific packages).

III. Accessing and Preparing Data

Data, Data, Everywhere...

Acquiring Data with Python

  • Loading from CSV files
  • Accessing SQL databases

Cleansing Data with Python

  • Stripping out extraneous information
  • Normalizing data
  • Formatting data

Once you complete this module, you will know how to load data from common types of data sources, including structured text files and SQL databases. and you will know some of the common tools used in Python to cleanse and prepare your data for analysis.

IV. Numerical Analysis, Data Exploration, and Data Visualization with NumPy Arrays,
Matplotlib, and Seaborn

NumPy Essentials

  • The NumPy array
  • N-dimensional array operations and manipulations
  • Memory mapped files

Data Visualization

  • 2D plotting with Matplotlib
  • Advanced data visualization with Seaborn

Once you complete this module, you will understand how to use NumPy arrays for efficient numerical processing and how to use NumPy methods such as slicing to write code that is both compact and easy to read and understand. You will know how to use Matplotlib, Seaborn, and NumPy together to explore and visualize your data.

V. Exploring Data with Pandas

Searching for Gold in a Pile of Pyrite

  • Data manipulation with Pandas
  • Statistical analysis with Pandas
  • Time series analysis with Pandas

At the end of this module, you will know how to access some of the core tools used for statistical analysis and data exploration in Python.

VI. Machine Learning with scikit-learn

Predicting the Future Can Be Good for Business

  • Input: 2D, samples, and features
  • Estimator, predictor, transformer interfaces
  • Pre-processing data
  • Regression
  • Classification
  • Model selection

At the end of this module you will have a working understanding of what machine learning tools are available in scikit-learn and how to use them.



Machine Learning for Data Science and Analytics


Learn the principles of machine learning and the importance of algorithms.


  1. Base SAS
  2. Advance SAS
  3. Clinical SAS Programming
  4. Predictive Modeling
  5. Clinical Data Manager
  6. Platform Administrator
  7. SAS Visual Analytics
  8. SAS Data Integration Studio
  9. BA Data Mining with Statistics
  10. Business Intelligence
  11. Data Management and Reporting


Data Science training in Chennai with Advanced SAS: Macros & SQL


Study advanced techniques in SAS - the most popular analytics language

Data Scientists, grab the opportunity to learn advanced data manipulation using SQL and Macros. Leap ahead in SAS by mastering the use of SQL as a data manipulation tool, the components of the SAS macro facility, the designing and debugging of macro systems, and more.


Oracle SQL Training Course Contents :

Introduction

  • Describe the features of Oracle Database 12c
  • Describe the salient features of Oracle Cloud 12c
  • Explain the theoretical and physical aspects of a relational database
  • Describe Oracle servers implementation of RDBMS and object relational database management system (ORDBMS)

1. Basic DATABASE Concept and SQL

  • Basic history of database concept: DBMS, RDBMS, ORDBMS
  • Advantage of ORACLE database and version information
  • Interface tools usage: sqlplus, isqlplus, sqldeveloper, Toad
  • SQL Language overview : DQL, DML, DDL, DCL, TCL
  • What is the usage of ANSI standard.
  • SELECT Command - Column Alias Rules, String data,
  • Concatenations with various data
  • Null Value handling with number and characters,
  • Arithmetic Operator
  • Concatenation Operator,
  • Eliminating Duplicate Rows

2. Restricting and Sorting Data

  • WHERE Clause - Character Strings and Dates, number
  • General Comparison Conditions = > >= < <= <>
  • Other Comparison BETWEEN , IN , LIKE , NULL
  • Logical Conditions AND OR NOT
  • ORDER BY Clause, Sorting by Column Alias , Column Position, Multiple Columns

3. Single-Row Functions

  • Character Functions: UPPER, LOWER, INITCAP, LENGTH, SUBSTR, INSTR, LPAD, RPAD, CONCAT, LTRIM, RTRIM, TRIM, REPLACE, TRANSLATE, REVERSE
  • Number Functions: ROUND, TRUNC, MOD, POWER, CEIL , FLOOR, ABS
  • Dates Functions: SYSDATE, MONTHS_BETWEEN, NEXT_DAY, LAST_DAY, ADD_MONTHS, ROUND, TRUNC, Arithmetic on Date
  • Conversion Functions: Implicit Data-Type Conversion & Explicit Data-Type Conversion, TO_CHAR ,TO_NUMBER ,TO_DATE
  • General Functions: NVL , NVL2 , NULLIF, COALESCE
  • CASE Expression, DECODE
  • Nested function with real-time usage

4. JOINS

  • EQUI JOIN / SIMPLE JOIN / NORMAL JOIN
  • ANSI JOIN, LEFT OUTER, RIGHT OUTER, FULL OUTER
  • NATURAL JOIN, NATURAL OUTER JOINS
  • INNER JOIN, JOIN ... USING clause, JOIN ... ON clause,
  • CROSS JOIN, NON-EQUI JOIN, SELF JOIN
  • ORACLE STANDARD OUTER JOINS.
  • Multi table Joins, Complex Joins How to simplified complex joins.

5. Multi-row Functions

  • Group Functions Rules, SUM, MIN, MAX, COUNT, AVG
  • Creating Groups of Data: GROUP BY Clause
  • Filtering Group Results: The HAVING Clause

6. Sub-queries

  • Single-Row Subqueries- Rules, Operators : = > >= < <= <>
  • Null Values in a Subquery
  • Multi-Row Subqueries- Rules, Operators : IN, ANY , ALL

7. Reporting data using interface commands

  • pagesize, linesize , column heading , column format , colsep
  • tTitle , bTitle , break on column, spool , CSV file generation, Text file generation

8. Data Manipulation Language DML and Transaction Control Language TCL

  • DML : INSERT, UPDATE, DELETE, MERGE
  • TCL : COMMIT, ROLLBACK, SAVEPOINT

9. Data Definition Language - DDL

  • DDL : CREATE, ALTER, RENAME, DROP, TRUNCATE
  • DEFAULT OPTION.
  • Constrain table copy

10. Constraints

  • NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY, CHECK
  • Column Level Constraint, Table Level Constraint Naming constraints and usage
  • Adding a Constraint, Dropping a Constraint,
  • Disabling Constraints, Enabling Constraints
  • Validating Constraints

11. Views

  • Simple Views and Complex Views , Create, Drop, Source Code
  • Rules for Performing DML Operations on a View
  • WITH CHECK OPTION , WITH READ ONLY
  • Inline Views
  • Materialized View , Create, Refresh, Drop - Usage

12. Other Database Objects

  • Sequence- NEXTVAL and CURRVAL
  • Index - When to Create an Index, When Not to Create an Index.
  • Synonyms

13. DCL COMMANDS

  • Creating Users
  • Granting / Revoking Privileges
  • Creating and Granting Privileges to a Role

14. Dictionary Tables

  • Tables, Views, Synonyms, Index, Sequence, Constrains, Source and other Dictionary

15. SET Operators

  • UNION ,
  • UNION ALL ,
  • INTERSECT ,
  • MINUS

16. Advanced Date-time Functions

  • TIME ZONES ,
  • SYSDATE, SYSTIMESTAMP,
  • CURRENT_DATE , CURRENT_TIMESTAMP
  • SESSIONTIMEZONE ,
  • Storing time zone data in Table
  • EXTRACT ,
  • TO_YMINTERVAL

17. Advanced GROUP BY Clause

  • Group by with ROLLUP,
  • Group by with CUBE,
  • GROUPING SETS

18. Advanced Subqueries

  • Pairwise Comparison Subquery ,
  • Nonpairwise Comparison Subquery
  • Correlated Subqueries,
  • Correlated UPDATE,
  • Correlated DELETE
  • EXISTS , NOT EXISTS Operator

19. Hierarchical Retrieval

  • Walking the Tree: From the Bottom Up , From the Top Down
  • LEVEL Pseudo column,
  • Connect by prior,

20. Multi-table Insert

  • Unconditional INSERT ALL
  • Conditional INSERT ALL
  • Conditional FIRST INSERT

21. DATA LOADER

  • SQLLDR Loading CSV file / Flat file into ORACLE table.

22. Analytic Functions

  • WM_CONCAT, LAG, LEAD, RANK, DENSE_RANK
  • Query_by partition_clause with sum, min, max, avg, count,
  • order_by_clause with sum, min, max, avg, count,
  • Psudo column : Rownum, Rowid, - Elimination duplicate data
  • Connect by rownum , Connect by Level Generating random numbers, random dates,
  • Quote Operator syntax and usage

23. Backup

  • Export / Import SCHEMA

24. General discussion

  • What is migration?
  • Migration Estimating, Planning, Preparation Simple Scenario / Complex Scenario.


Data Science training in Chennai with Excel Macro


Creating Record Macros

  1. Recording a Macro
  2. Running a Macro
  3. Running a Macro from the Macros Dialog Box
  4. Creating a Short-cut key to run a macro
  5. Running a Macro with a Shortcut key
  6. Assigning a Macro to a Menu or Toolbar
  7. Editing a Macro with Visual Basic

Visual Basic Editor

  1. Objects
  2. Methods
  3. Properties
  4. Programming Tools
  5. The Menu Bar

The Project Explorer

  1. Using the Project Explorer

Using Constants

  1. Excel Constants
  2. Variable Constants

Excel Objects

  1. Objects, Properties and Methods
  2. Getting & Setting Properties
  3. Calling Methods
  4. Passing Arguments
  5. Singular Objects & Collections of Objects

Using Visual Basic Functions

  1. InputBox Function
  2. MsgBox Function
  3. Using a Set Statement

Building Formular Control Structures

  1. If Then Janision Structures
  2. Logical Operators
  3. Select Case Janision Structures
  4. Case Else
  5. Comparison Operators with Select Case Structure
  6. For Loops
  7. Do Loops
  8. While...Wend Statement

Running Code

  1. Run mode and Design mode
  2. Running Code from the development environment
  3. Running Code from the host application

The Properties Window

  1. Changing a Property

Variables

  1. Dimensioning a variable
  2. Using variables in routines
  3. Object Variables

UserForms

  1. Designing & Creating Forms
  2. Working with Controls
  3. Creating Custom Dialog Boxes
  4. Userform Properties Methods & Events

Advanced VBA Programming Techniques

  1. Developing Excel Utilities with VBA
  2. Error Handling
  3. Using In-Built Excel Features in VBA
  4. Working with Pivot Tables
  5. Working with Charts
  6. Understanding Excel's Events
  7. Interacting with Other Applications
  8. Creating and Using Add-Ins

Developing Applications

  1. Working with the Ribbon
  2. Working with Shortcut Menus
  3. Providing Help for Your Applications
  4. Developing User Oriented Applications

Event Handling

  1. VBA Editing & Debugging
  2. Auto Macros
  3. Error Handling

Connecting to Database

  1. Connect to MS Access
  2. Access Database & Retrieve Data from MS Access
  3. Create Table & Query Data Filter DB using Query
  4. Update and Modify DB
  5. Create Single and Multiple Mapping Tables
  6. Filter DB using Query
  7. Query data grouping
  8. Retrieve Data using Logical Access Query

Connect Excel to Outlook

  1. Connect to Outlook using VBA
  2. Query to retrieve Mail From, to and CC address
  3. Retrieve Mail Subject and Time
  4. Retrieve Mail Attachment


Data Science training in Chennai Reviews


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Greens Technologys Overall Reviews


Greens Technologys Overall Reviews 4.9 out of 5 based on 91 ratings. 91 user reviews.
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