Data Science with R Programming

Here’s a simple List for you to assess if you have all the pre-requisites to learn data science and become an enterprise data scientist-

Have a Master’s /PhD/Graduate Degree in any of the STEM fields.
Know the ABC’s of programming.
Know the basics of SQL
Have passion to develop business acumen
Curious about playing with data
Familiar with the basic math and statistic concepts

KEY FEATURES

• 100% Job Assistance
• Learn from the basics and advance level of Data Science
• Personal attention given to each & every participant during the training program
• Batches with limited seats
• Weekend batches as per your suitability of candidate.
• Learn Data Science the most needed technology for IT Professionals from industrial experienced trainer
• you’ll learn how to use the entity data framework
• Two training centers for your convenience: Kothrud and Wakad
  • If you are located in Kothrud or nearby areas like Karve Nagar, Warje-Malwadi, Bhusari Colony, Shivaji Nagar etc. then our branch in located a short distance away from you i.e. Vanaz Corner, Kothrud.
  • If you are located in Pimpri Chinchwad or nearby areas like Pimple Saudagar, Wakad, Chinchwad, Balewadi, Sangvi, Dapodi etc. then you can visit our branch in Wakad near Kalewadi Phata.

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

Data

• 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 [Just a Brief There is a Separate Course on this]

• 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 Deep Dive

• 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
• Anonymization

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 Manipulation in R [Programming]

• Various phases of Data Cleaning
• Functions used in Inspection
• Data Cleaning Techniques
• Uses of functions involved
• Use-cases for Data Cleaning 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

Exploratory Data Analysis (EDA) using R [Programming]

• What is EDA?
• Why do we need EDA?
• Goals of EDA
• Types of EDA
• Implementing of EDA
• Boxplots, cor() in R
• EDA functions
• Multiple packages in R for data analysis
• Some fancy plots
• Use-cases for EDA using R

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