Data Science Training in OMR Chennai

Do you want to hone your skills, deepen your knowledge of R (programming language)Programming – Then you have come to the right place!

About the Data Science Course

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!

SAS Trainers in our SAS Training institute are Globally Experienced, Certified

“The best part of the course is the highly experienced and approachable mentors. They guide you in the right way and help you fulfill your aspirations about pursuing a career in analytics.”

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.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

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.