Data Science

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

• 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 Acuqisition
• Where to source data
• Techniques
• Evaluating input data
• Data formats
• Data Quantity
• Data Quality
• Resolution Techniques
• Data Transformation
• File format Conversions
• Annonymization

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

  • 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

  Statistics + Machine Learning 

  Statistics; Whats is Statistics

  • Descriptive Statistics
  • Central Tendency Measures
  • The Story of Average
  • Dispersion Measures
  • Data Distributions
  • Central Limit Theorem
  • What is Sampling
  • Why Sampling
  • Sampling Methods
  • Inferential Statistics
  • What is Hypothesis testing
  • Confidence Level
  • Degrees of freedom
  • what is pValue
  • Chi-Square test
  • What is ANOVA
  • Correlation vs Regression
  • Uses of Correlation & Regression

  Machine Learning Introduction

  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques
  • Clustering
  • Similarity Metrics
  • Distance Measure Types: Euclidean, Cosine Measures
  • Creating predictive models
  • Understanding K-Means Clustering
  • Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
  • Case study

  Implementing Association rule mining

  • Case study

  Understanding Process flow of Supervised Learning Techniques

  • Decision Tree Classifier
  • How to build Decision trees
  • Case study

  Random Forest Classifier

  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Case study

  Naive Bayes Classifier.

  • Case study

  Project Discussion

  • Problem Statement and Analysis
  • Various approaches to solve a Data Science Problem
  • Pros and Cons of different approaches and algorithms.

  Linear Regression

  • Case study

  Logistic Regression

  • Case study

  Text Mining

  • Case study

  Sentimental Analysis

  • Case study