Data Science online training

Data Science Training

Data Science Course Content Overview

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

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