Data Science Training

What is Data Science?

Data Science is a new interesting software technology, which is used to apply critical analysis, provide the ability to develop sophisticated models, for massive data sets and drive the business insights. DS utilizes the potential and scope of Hadoop, R programming and machine learning implementation, by making use of Mahout.

Interesting Facts about DS…

  • The term Data Science is used interchangeably with Datalogy.
  • DS employs its theories and techniques from physics, mathematics, nanotechnologies and this list goes till 23 fields.
  • DS is considered to be a part of many academic and research areas.
  • DS has been employed fraud monitoring and security.

Who can be a Data Scientist?

In fact, literally any one can become Data Scientist, with a strong aspiration to become so. However, the aspirants with better exposure of core Java along with better mathematical aptitude can become the Data Scientists sooner, while the rest of the aspirants need to take additional time to grab these basic prerequisites.

Who is eligible?

Data Science can be learnt for machine learning implementation, through implementation of programming by R language and interested to apply the machine learning techniques on the Big Data. So, the professionals, with the following professional skills and academics will be eligible to learn this course.

  1. SAS professionals and SPSS professionals, who are interested to work for better understanding of Big Data Analytics
  2. Software developers, who are interested to become ‘Data Scientists’
  3. Business Analysts, who are willing to learn the Machine Learning Techniques
  4. R professionals, who are interested to gain fair knowledge and expertise in Big Data analysis
  5. Experienced analytics managers, who work with and lead the team of analysis
  6. Hadoop experts, who wish to cover both Machine Learning implementation and R
  7. Statisticians, who are interested in implementing the techniques of statistics of huge data on Big Data
  8. Information architects, who are willing to obtain proficiency in Predictive Analysis
  9. Analysts, who are interested to gain fair knowledge in the methodologies of Data Science

Academically, you should have completed any of the graduations like, BCA, B-Tech, MCA, M-Tech.

Data Science Syllabus

Introduction to Data Science

  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis
  • What is Data Mining
  • What is Machine Learning
  • Analytics vs Data Science
  • Value Chain
  • Types of Analytics
  • Lifecycle Probability
  • Analytics Project Lifecycle


  • Basis of Data Categorization
  • Types of Data
  • Data Collection Types
  • Forms of Data & Sources
  • Data Quality & Changes
  • Data Quality Issues
  • Data Quality Story
  • What is Data Architecture
  • Components of Data Architecture
  • OLTP vs OLAP
  • How is Data Stored?

Big Data

  • What is Big Data?
  • 5 Vs of Big Data
  • Big Data Architecture
  • Big Data Technologies
  • Big Data Challenge
  • Big Data Requirements
  • Big Data Distributed Computing & Complexity
  • Hadoop
  • Map Reduce Framework
  • Hadoop Ecosystem

Data Science Scop

  • What Data Science is
  • Why Data Scientists are in demand
  • What is a Data Product
  • The growing need for Data Science
  • Large Scale Analysis Cost vs Storage
  • Data Science Skills
  • Data Science Use Cases
  • Data Science Project Life Cycle & Stages
  • Map Reduce Framework
  • Hadoop Ecosystem
  • Data Acquisition
  • Where to source data
  • Techniques
  • Evaluating input data
  • Data formats
  • Data Quantity
  • Data Quality
  • Resolution Techniques
  • Data Transformation
  • File format Conversions

Intro to R Programming [Programming]

  • Introduction to R
  • Business Analytics
  • Analytics concepts
  • The importance of R in analytics
  • R Language community and eco-system
  • Usage of R in industry
  • Installing R and other packages
  • Perform basic R operations using command line
  • Usage of IDE R Studio and various GUI

R Programming Concepts [Programming]

  • The data types in R and its uses
  • Built-in functions in R
  • Subsetting methods
  • Summarize data using functions
  • Use of functions like head(), tail(), for inspecting data
  • Use-cases for problem solving using R

Data Import Techniques in R [Programming]

  • Import data from spreadsheets and text files into R
  • Importing data from statistical formats
  • Packages installation for database import
  • Connecting to RDBMS from R using ODBC and basic SQL queries in R
  • Web Scraping
  • Other concepts on Data Import Techniques

Data Visualization in R [Programming]

  • Story telling with Data
  • Principle tenets
  • Elements of Data Visualization
  • Infographics vs Data Visualization
  • Data Visualization & Graphical functions in R
  • Plotting Graphs
  • Customizing Graphical Parameters to improvise the plots
  • Various GUIs
  • Spatial Analysis
  • Other Visualization concepts

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