Introduction to Data Science


Introduction to Data Science

  1. Need for Data Scientists
  2. Foundation of Data Science
  3. What is Business Intelligence
  4. What is Data Analysis, Data Mining, and Machine Learning
  5. Analytics vs Data Science
  6. Value Chain
  7. Types of Analytics
  8. Lifecycle Probability
  9. Analytics Project Lifecycle


  1. Basis of Data Categorization
  2. Types of Data
  3. Data Collection Types
  4. Forms of Data and Sources
  5. Data Quality, Changes and Data Quality Issues, Quality Story
  6. What is Data Architecture
  7. Components of Data Architecture
  8. OLTP vs OLAP
  9. How is Data Stored

Big Data

  1. What is Big Data?
  2. 5 Vs of Big Data
  3. Big Data Architecture, Technologies, Challenge and Big Data Requirements
  4. Big Data Distributed Computing and Complexity
  5. Hadoop
  6. Map Reduce Framework
  7. Hadoop Ecosystem

Data Science Deep Dive

  1. What is Data Science?
  2. Why are Data Scientists in demand?
  3. What is a Data Product
  4. The growing need for Data Science
  5. Large-Scale Analysis Cost vs Storage
  6. Data Science Skills
  7. Data Science Use Cases and Data Science Project Life Cycle & Stages
  8. Map-Reduce Framework
  9. Hadoop Ecosystem
  10. Data Acquisition
  11. Where to source data
  12. Techniques
  13. Evaluating input data
  14. Data formats, Quantity and Data Quality
  15. Resolution Techniques
  16. Data Transformation
  17. File Format Conversions
  18. Anonymization

Intro to R Programming

  1. Introduction to R
  2. Business Analytics
  3. Analytics concepts
  4. The importance of R in analytics
  5. R Language community and eco-system
  6. Usage of R in industry
  7. Installing R and other packages
  8. Perform basic R operations using command line
  9. Usage of IDE R Studio and various GUI

R Programming Concepts

  1. The datatypes in R and its uses
  2. Built-in functions in R
  3. Subsetting methods
  4. Summarize data using functions
  5. Use of functions like head(), tail(), for inspecting data
  6. Use-cases for problem solving using R

Data Manipulation in R

  1. Various phases of Data Cleaning
  2. Functions used in Inspection
  3. Data Cleaning Techniques
  4. Uses of functions involved
  5. Use-cases for Data Cleaning using R

Data Import Techniques in R

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

Exploratory Data Analysis (EDA) using R

  1. What is EDA?
  2. Why do we need EDA?
  3. Goals of EDA
  4. Types of EDA
  5. Implementing of EDA
  6. Boxplots, cor() in R
  7. EDA functions
  8. Multiple packages in R for data analysis
  9. Some fancy plots
  10. Use-cases for EDA using R

Data Visualization in R

  1. Storytelling with Data
  2. Principle tenets
  3. Elements of Data Visualization
  4. Infographics vs Data Visualization
  5. Data Visualization & Graphical functions in R
  6. Plotting Graphs
  7. Customizing Graphical Parameters to improvise the plots
  8. Various GUIs
  9. Spatial Analysis
  10. Other Visualization concepts