Introduction to Data Science

• Need for Data Scientists
• Foundation of Data Science
• What is Business Intelligence
• What is Data Analysis, Data Mining, and 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 and Sources
• Data Quality, Changes and Data Quality Issues, 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, Technologies, Challenge and Big Data Requirements
• Big Data Distributed Computing and Complexity
• Hadoop
• Map Reduce Framework
• Hadoop Ecosystem

Data Science Deep Dive

• What is Data Science?
• Why are Data Scientists 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 and Data Science Project Life Cycle & Stages
• Map-Reduce Framework
• Hadoop Ecosystem
• Data Acquisition
• Where to source data
• Techniques
• Evaluating input data
• Data formats, Quantity and Data Quality
• Resolution Techniques
• Data Transformation
• File Format Conversions
• Anonymization

Intro to R 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

• The datatypes 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

• 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

• 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

• 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

• Storytelling 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