Data Scientist Training

• Understanding how to read Spreadsheet and Database Data
• Understanding what is Data Manipulation Techniques by using the R programming?
• Getting familiarity with Pig, Hive, impala, etc.
• Learning Operators, Data Types & Variables and VBA Excel Object
• Understanding what Mathematical Computing with Python (NumPy) is?
• What are Python Environment Setup and Essentials?
• Implementing the programing algorithms efficiently

• Data Integration Architects
• Hadoop Administrators and Developers
• Data Analysts
• Data Scientists
• Data Architects
• The software developers, who indulge in processing large amounts of data
• Statistical geneticist

1. Data Science With SAS Training (SAS Programmer)

SAS® Advanced

• SAS® Base
  • SAS® Programming 1: Essentials
  • Introduction to SAS® foundation
  • Introduction to SAS® programs
  • Accessing SAS® Data
  • Producing Detail Reports
  • Formatting Data Values
  • Reading SAS® Data Sets
  • Reading Spreadsheet and Database Data
  • Reading Spreadsheet and Database Data
  • Manipulating Data
  • Combining SAS®Data Sets
  • Creating Summary Reports
• SAS® Programming 2: Data Manipulation Techniques
  • Introduction
  • Controlling Input and Output
  • Summarizing Data
  • Reading Raw Data Files
  • Data Transformations
  • Debugging Techniques
  • Processing Data Iteratively
  • Restructuring a Data Set
  • Combining SAS® Data Sets
• SAS® SQL 1: Essentials
  • Introduction
  • Displaying Query Results
  • SQL Joins
  • Subqueries
  • Set Operators
  • Creating Tables and Views
  • Advanced PROC SQL Features
• SAS® Macro Language 1:Essentials
  • Introduction
  • Macro Variables
  • Macro Definitions
  • DATA Step and SQL Interfaces
  • Macro Programs

2. Data Science Certification Training - R Programming

• Essential to R programming
  • An Introduction to R
  • Introduction to the R language
  • Programming statistical graphics
  • Programming with R
  • Simulation
  • Computational linear algebra
  • Numerical optimization
• Data Manipulation Techniques using R programming
  • Data in R
  • Reading and Writing Data
  • R and Databases
  • Dates
  • Factors
  • Subscripting
  • Character Manipulation
  • Data Aggregation
  • Reshaping Data
• Statistical Applications using R programming
  • Basics
  • The R environment
  • Probability and distributions
  • Descriptive statistics and graphics
  • One- and two-sample tests
  • Regression and correlation
  • Analysis of variance and the Kruskal–Wallis test
  • Tabular data
  • Power and the computation of sample size
  • Advanced data handling
  • Multiple Regression
  • Linear models
  • Logistic regression
  • Survival analysis
  • Rates and Poisson regression
  • Nonlinear curve fitting

3. Big Data Hadoop And Spark Developer

An Introduction to Spark - Getting started

• Big Data Hadoop
  • Introduction
  • Hadoop Fundamentals
  • Introduction to Pig
  • Basic Data Analysis with Pig
  • Processing Complex Data with Pig
  • Multi-Dataset Operations with Pig
  • Extending Pig
  • Pig Troubleshooting and Optimization
  • Introduction to Hive
  • Relational Data Analysis with Hive
  • Hive Data Management
  • Text Processing with Hive
  • Hive Optimization
  • Extending Hive
  • Introduction to Impala
• Apache Spark
  • About Resilient Distributed Dataset and DataFrames
  • The Spark application programming
  • An Introduction to Spark libraries
  • AboutSpark configuration, monitoring and tuning

4. Business Analytics with Excel (with VBA)

• Macro Introduction
• Learn About Programming
• Introduction to Flow Chart & Diagram
• Getting Starte with VBA
• VBA Excel Object
• Operators, Data Types & Variables
• Conditional Statement
• Loop Statement
• VBA User Forms-Part-1
• VBA User Forms-Part-2
• Advance VBA Programming

5. Data Science with Python

• The Data Science: An Overview
• Data Analytics Overview
• Statistical Analysis and Business Applications
• Python Environment Setup and Essentials
• What is Mathematical Computing with Python (NumPy)?
• The Scientific computing with Python (Scipy)
• The Data Manipulation with Pandas
• The Natural Language Processing with Scikit Learn
• The Data Visualization in Python using matplotlib
• Web Scraping with BeautifulSoup
• Python integration with Hadoop MapReduce and Spark

Note: to know about the detailed information about the course modules please feel free to write us or give us a buzz.