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Course duration : 40 Days


Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.

What is Data science course?

The following is a comprehensive list of Data Science courses and resources that explain or teach skills within Data Science, such as machine learning, data mining, analytics, cleaning, visualization, scraping, using APIs to make data products, artificial intelligence.

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What You'll Learn:
  • 1.How to load and clean real-world data.
  • 2.How to make reliable statistical inferences from noisy data.
  • 3.How to use machine learning to learn models for data.
  • 4.How to visualize complex data.
  • 5.How to use Apache Spark to analyze data that does not fit within the memory of a single computer.
  • 1.Provides insights About the roles of a Data scientist.
  • 2.Enable You to Analyze of Big Data.
  • 3.Learn Techniques and Tools for Transformation of Data.
  • 4.Make You Understand Data Mining.
  • 5.Familiaize You With Different Formats.
  • 6.Make You Figure Out Machine Learning Algorithms.
  • 7.Learn Data Visualization and Optimization.
  • 8.Learners are taught to understand business intelligence and business and data analytics.
  • 9.To understand the business data analysis through the powerful tools of data application.
  • 10.Learn how to apply Tableau, MapReduce, and get introduced in to R and R+.
  • 11.Understand the methods of data mining and creation of decision tree.
  • 12.Explore different aspects of Big Data Technologies.
  • 13.Learn the concepts of loop functions and debugging tools.
Prerequisites to Learn Data science:
  • 1.Math (e.g. linear algebra, calculus and probability).
  • 2.Statistics (e.g. hypothesis testing and summary statistics).
  • 3.Machine learning tools and techniques (e.g. k-nearest neighbors, random forests, ensemble methods, etc.).
  • 4.Software engineering skills (e.g. distributed computing, algorithms and data structures).
  • 5.Data mining.
  • 6.Data cleaning and munging.
  • 7.Data visualization (e.g. ggplot and d3.js) and reporting techniques.
  • 8.Unstructured data techniques.
  • 9.R and/or SAS languages.
  • 10.SQL databases and database querying languages.
  • 11.Python (most common), C/C++ Java, Perl.
  • 12.Big data platforms like Hadoop, Hive & Pig.
  • 13.Cloud tools like Amazon S3.
Average salary will be:

Data science Salary will be around 5 lakhs + package.



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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


•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
•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
•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
•Evaluating input data
•Data formats
•Data Quantity
•Data Quality
•Resolution Techniques
•Data Transformation
•File format Conversions

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


Big Data and Hadoop Introduction

•What is Big Data and Hadoop?
•Challenges of Big Data
•Traditional approach Vs Hadoop
•Hadoop Architecture
•Distributed Model
•Block structure File System
•Technologies supporting Big Data
•Fault Tolerance
•Why Hadoop?
•Hadoop Eco-System
•Use cases of Hadoop
•Fundamental Design Principles of Hadoop
•Comparison of Hadoop Vs RDBMS

Understand Hadoop Cluster Architecture

•Hadoop Cluster & Architecture
•5 Daemons
•Hands-On Exercise
•Typical Workflow
•Hands-On Exercise
•Writing Files to HDFS
•Hands-On Exercise
•Reading Files from HDFS
•Hands-On Exercise
•Rack Awareness
•Before Map Reduce

Map Reduce Concepts

•Map Reduce Concepts
•What is Map Reduce?
•Why Map Reduce?
•Map Reduce in real world.
•Map Reduce Flow
•What is Mapper?
•What is Reducer?
•What is Shuffling?
•Word Count Problem
•Hands-On Exercise
•Distributed Word Count Flow & 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?
•Choosing Right Hadoop Software?
•Hadoop Log Files?

Hadoop 2.0 & YARN

•Hadoop 1.0 Challenges
•NN Scalability
•Job Tracker Challenges
•Hadoop 2.0 New Features
•Hadoop 2.0 Cluster Architecture & Federation
•Hadoop 2.0 HA
•Yarn & Hadoop Ecosystem
•Yarn MR Application Flow


•Introduction to Pig
•What Is Pig?
•Pig’s Features & Pig Use Cases
•Interacting with Pig
•Basic Data Analysis with Pig
•Hands-On Exercise
•Pig Latin Syntax
•Loading Data
•Hands-On Exercise
•Simple Data Types
•Field Definitions
•Data Output
•Viewing the Schema
•Hands-On Exercise
•Filtering and Sorting Data
•Hands-On Exercise
•Commonly-Used Functions
•Hands-On Exercise: Pig for ETL Processing
•Processing Complex Data with Pig
•Hands-On Exercise
•Storage Formats
•Complex/Nested Data Types
•Hands-On Exercise
•Hands-On Exercise
•Built-in Functions for Complex Data
•Hands-On Exercise
•Iterating Grouped Data
•Hands-On Exercises
•Multi-Dataset Operations with Pig
•Hands-On Exercise
•Techniques for Combining Data Sets

Practice Set – 1

•Joining Data Sets in Pig
•Hands-On Exercise
•Splitting Data Sets
•Hands-On Exercise


•Hive Fundamentals & Architecture
•Loading and Querying Data in Hive
•Hands-On Exercise
•Hive Architecture and Installation
•Comparison with Traditional Database
•HiveQL: Data Types, Operators and Functions,
•Hands-On Exercise
•Hive Tables ,Managed Tables and External Tables
•Hands-On Exercise
•Partitions and Buckets
•Hands-On Exercise
•Storage Formats, Importing Data, Altering Tables, Dropping Tables
•Hands-On Exercise
•Querying Data, Sorting and Aggregating, Map Reduce Scripts,
•Hands-On Exercise

Practice Set – 2

•Joins & Sub queries, Views
•Hands-On Exercise
•Integration, Data manipulation with Hive
•Hands-On Exercise
•User Defined Functions,
•Hands-On Exercise
•Appending Data into existing Hive Table
•Hands-On Exercise
•Static partitioning vs dynamic partitioning
•Hands-On Exercise


•CAP Theorem
•HBase Architecture and concepts
•Introduction to HBase
•Client API’s and their features
•HBase tables The ZooKeeper Service
•Data Model, Operations

Practice Set – 3

•Programming and Hands on Exercises


•Introduction to Sqoop
•MySQL Client & server
•Connecting to relational data base using Sqoop
•Importing data using Sqoop from Mysql
•Exporting data using Sqoop to MySql
•Incremental append
•Importing data using Sqoop from Mysql to hive
•Exporting data using Sqoop to MySql from hive
•Importing data using Sqoop from Mysql to hbase
•Using queries and sqoop

Flume & Oozie

•What is Flume?
•Why use Flume, Architecture, configurations
•Master, collector, Agent
•Twitter Data Sentimental Analysis project
•What is Oozie, Architecture, configurations?
•Oozie Job Submission
•Oozie properties
•Hands on exercises


•Social Media Final Project
•Hadoop Project
•Problem Definition
•Discuss data sets and specifications of the project.

Project in Healthcare Domain

•Hadoop Project in Healthcare
•Problem Definition
•Discuss data sets and specifications of the project.

Project in Finance/Banking Domain

•Hadoop Project in Banking Domain
•Problem Definition
•Discuss data sets and specifications of the project.


Apache Spark

•Introduction to Apache Spark
•Why Spark
•Batch Vs. Real Time Big Data Analytics
•Batch Analytics – Hadoop Ecosystem Overview,
•Real Time Analytics Options,
•Streaming Data – Storm,
•In Memory Data – Spark, What is Spark?,
•Spark benefits to Professionals
•Limitations of MR in Hadoop
•Components of Spark
•Spark Execution Architecture
•Benefits of Apache Spark
•Hadoop vs Spark


•Features of Scala
•Basic Data Types of Scala
•Val vs Var
•Type Inference
•Objects & Classes in Scala
•Functions as Objects in Scala
•Anonymous Functions in Scala
•Higher Order Functions
•Lists in Scala
•Pattern Matching
•Traits in Scala
•Collections in Scala

Spark Architecture

•Spark & Distributed Systems
•Spark for Scalable Systems
•Spark Execution Context
•What is RDD
•RDD Deep Dive
•RDD Dependencies
•RDD Lineage
•Spark Application In Depth
•Spark Deployment
•Parallelism in Spark
•Caching in Spark

Spark Internals

•Spark Transformations
•Spark Actions
•Spark Cluster
•Spark SQL Introduction
•Spark Data Frames
•Spark SQL with CSV
•Spark SQL with JSON
•Spark SQL with Database

Spark Streaming

•Features of Spark Streaming
•Micro Batch
•Transformations on Dstreams
•Spark Streaming Use Case

Statistics + Machine Learning


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

Machine Learning Introduction

•ML Fundamentals
•ML Common Use Cases
•Understanding Supervised and Unsupervised Learning Techniques
•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


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
•Python Editors and IDEs.
•Using Variables
•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

•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

•Function Parameters
•Global Variables
•Variable Scope and Returning Values. Sorting
•Alternate Keys
•Lambda Functions
•Sorting Collections of Collections
•Sorting Dictionaries
•Sorting 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 Expressionsit’s Packages and Object Oriented Programming in Python

•The Sys Module
•Interpreter Information
•Launching External Programs
•PathsDirectories and Filenames
•Walking Directory Trees
•Math Function
•Random Numbers
•Dates and Times
•Zipped Archives
•Introduction to Python Classes
•Defining Classes
•Instance Methods
•Class Methods and DataStatic Methods
•Private Methods and Inheritance
•Module Aliases and Regular Expressions.

Debugging and Project Artefacts

•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)


•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
•Plotting Data
•Creating Functions
•Converting Different Formats
•Combining Data from Various Formats
•Slicing/Dicing Operations.

Scikit and Introduction to Hadoop

•Introduction to Scikit-Learn
•Inbuilt Algorithms for Use
•What is Hadoop and why it is popular
•Distributed Computation and Functional Programming
•Understanding MapReduce Framework Sample MapReduce Job Run.

Hadoop and Python

•PIG and HIVE Basics
•Streaming Feature in Hadoop
•Map Reduce Job Run using Python
•Writing a PIG UDF in Python
•Writing a HIVE UDF in Python
•Pydoop and MRjob Basics.

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