This course covers the concepts and tools you’ll need throughout the entire data science life cycle, It’s the most comprehensive Data Science course, covering all the aspects of the Data Science process from Data Integration, Data Manipulation, Descriptive Analytics and Visualization to Statistical Analysis, Predictive Analytics and Machine Learning models, using the most in-demand tools like R, Python. It will enable you with the knowledge in all three elements of Data Science – Statistics, Tools, and Business Knowledge.  In the lab, you’ll apply the skills learned by building a data product using real-world data.

Course Overview

  • Current Challenges
  • What is Data Science
  • Components and Characteristics of Data science
  • Role of Data Scientist
  • Data Science Architecture
  • Data Science Lifecycle
  • Data Analysis and Business Intelligence
  • Platforms and Tools Used in Data Science
  • Data Science Modelling
  • Business Value Articulation using Data Science
  • Data Science in Various Industry Verticals and Functional Areas
  • Integrated view – Predictive Analytics, Big Data Analytics and Machine Learning
  • What is Big Data
  • Characteristics of Big Data
  • Difference between Big Data and Traditional Data
  • Big Data Landscape /Techniques
  • Machine Learning
  • Role of Machine Learning in Data Science
  • Machine Learning using Python and R

 

Data Science Course Content

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

Map Reduce Concepts

  • Map Reduce Concepts
  • What is Map Reduce?
  • Why Map Reduce?
  • Map Reduce in real world and Map Reduce Flow
  • What is Mapper, Reducer, and Shuffling?
  • Word Count Problem
  • Hands-On Exercise
  • Distributed Word Count Flow and Solution
  • Log Processing and Map Reduce
  • Hands-On Exercise

Advanced Map Reduce Concepts

  • What is Combiner?
  • Hands-On Exercise
  • What is Partitioner?
  • Hands-On Exercise
  • What is Counter?
  • Hands-On Exercise
  • InputFormats/Output Formats
  • Hands-On Exercise
  • Map Join using MR
  • Hands-On Exercise
  • Reduce Join using MR
  • Hands-On Exercise
  • MR Distributed Cache
  • Hands-On Exercise
  • Using sequence files & images with MR
  • Hands-On Exercise
  • Planning for Cluster & Hadoop 2.0 Yarn
  • Configuration of Hadoop
  • Choosing Right Hadoop Hardware and Software?
  • Hadoop Log Files?

Projects

  • Social Media Final Project
  • Hadoop Project
  • Objective
  • Problem Definition
  • Solution
  • Discuss datasets and specifications of the project

Project in Healthcare Domain

  • Hadoop Project in Healthcare
  • Objective
  • Problem Definition
  • Solution
  • Discuss datasets and specifications of the project

Project in Finance/Banking Domain

  • Hadoop Project in Banking Domain
  • Objective
  • Problem Definition
  • Solution
  • Discuss datasets and specifications of the project

Statistics + Machine Learning

Statistics

What 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 and Regression

Machine Learning

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 solving 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

Python

Getting Started with Python

  • Python Overview
  • About Interpreted Languages
  • Advantages/Disadvantages of Python pydoc
  • Starting Python
  • Interpreter PATH
  • Using the Interpreter
  • Running a Python Script
  • Python Scripts on UNIX/Windows, Editors and IDEs
  • Using Variables
  • Keywords
  • Built-in Functions
  • StringsDifferent Literals
  • Math Operators and Expressions
  • Writing to the Screen
  • String Formatting
  • Command Line Parameters and Flow Control

Sequences and File Operations

  • Lists
  • Tuples
  • Indexing and Slicing
  • Iterating through a Sequence
  • Functions for all Sequences
  • Using Enumerate()
  • Operators and Keywords for Sequences
  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets

Deep Dive – Functions Sorting Errors and Exception Handling

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections, Dictionaries and Lists in Place
  • Errors and Exception Handling
  • Handling Multiple Exceptions
  • The Standard Exception Hierarchy
  • Using Modules
  • The Import Statement
  • Module Search Path
  • Package Installation Ways

Regular Expressionist’s Packages and Object – Oriented Programming in Python

  • The Sys Module
  • Interpreter Information
  • STDIO
  • Launching External Programs
  • path directories and Filenames
  • Walking Directory Trees
  • Math Function
  • Random Numbers
  • Dates and Times
  • Zipped Archives
  • Introduction to Python Classes
  • Defining Classes
  • Initializers
  • Instance Methods
  • Properties
  • Class Methods and Data Static Methods
  • Private Methods and Inheritance
  • Module Aliases and Regular Expressions

Debugging, Databases and Project Skeletons

  • Debugging
  • Dealing with Errors
  • Using Unit Tests
  • Project Skeleton
  • Required Packages
  • Creating the Skeleton
  • Project Directory
  • Final Directory Structure
  • Testing your Setup
  • Using the Skeleton
  • Creating a Database with SQLite 3
  • CRUD Operations
  • Creating a Database Object.

Machine Learning Using Python

  • Introduction to Machine Learning
  • Areas of Implementation of Machine Learning
  • Why Python
  • Major Classes of Learning Algorithms
  • Supervised vs Unsupervised Learning
  • Learning NumPy
  • Learning Scipy
  • Basic plotting using Matplotlib
  • Machine Learning application

Supervised and Unsupervised learning

  • Classification Problem
  • Classifying with k-Nearest Neighbours (kNN)

Algorithm

  • General Approach to kNN
  • Building the Classifier from Scratch
  • Testing the Classifier
  • Measuring the Performance of the Classifier
  • Clustering Problem
  • What is K-Means Clustering
  • Clustering with k-Means in Python and an

Application Example

  • Introduction to Pandas
  • Creating Data Frames
  • GroupingSorting
  • Plotting Data
  • Creating Functions
  • Converting Different Formats
  • Combining Data from Various Formats
  • Slicing/Dicing Operations.

Python Project Work

  • Real world project