Data Science Online Training Modules Overview

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

• The 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 the 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

Hadoop

• 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
• Replication
• 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 and 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 the 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 a 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?

Hadoop 2.0 and YARN

• Hadoop 1.0 Challenges
• NN Scalability, SPOF, and HA
• 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

PIG

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

HIVE

• Hive Fundamentals and 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

Joins & Subqueries, 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

HBASE

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

Programming and Hands-on Exercises

SQOOP

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

Flume and Oozie

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

Job Opportunities in Data Science

Who wouldn’t prefer a job that assures a fast paced global career, higher than average perks? The job opportunities in the domain of Data Science are quite plenty. And with the increase in the colossal demand for the qualified experts across the top industries, more & more number of aspirants are planning towards securing their career in this domain. And also in response to the whooping salary packages for the certified professionals in this domain most of the professionals who are working in other prominent technologies are working towards making a career transition into this domain. Data Science Online Training by SacrosTek Systems will set you on the right career path of achieving success in this domain.

SacrosTek Systems offer certification programs for Data Science. Certificates are issues on successful completion of the course and the assessment examination. Students are requested to participate in the real-time project program to get first-hand experience on the usage and application of the Data Science. The real-time projects are designed by our team of industry experts to help students get best possible exposure to the Data Science and its applications.