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Data science Training

Data science is the hottest field of the century. Learn more about why data science, artificial intelligence (AI) and machine learning are revolutionizing the way people do business and research around the world. In this course, we will meet some data science practitioners and we will get an overview of what data science is today

Languages of Data Science

Learn tools and languages used for data analysis - Excel, SQL, Python & Tableau. These tools will be used throughout the program. You will get content access to learn them even before the course officially begins!

Data Preparation

Data that you extract is usually not suitable for analysis right away. You need to clean, polish and prepare it before you start using it.

Case Study- Investments

Let's get our hands dirty! Your first Data science Project. Find sectors in which your company should invest based on given parameters.

COURSE SYLLABUS

Module 1 - Defining Data Science

• What is data science?
• There are many paths to data science
• Any advice for a new data scientist?
• What is the cloud?
• "Data Science: The Sexiest Job in the 21st Century"

Module 2 - What do data science people do?

• A day in the life of a data science person
• R versus Python?
• Data science tools and technology
• "Regression"

Module 3 - Data Science in Business

• How should companies get started in data science?
• Tips for recruiting data science people
• "The Final Deliverable"

Module 4 - Use Cases for Data Science

• Applications for data science
• "The Report Structure"

Module 5 -Data Science People

• Things data science people say
• "What Makes Someone a Data Scientist?"

Data Science With R Programming

• Introduction
• What are Data Analysis, Data Analytics and Data Science?
• Business Decisions
• Case study of Walmart

Various Analytics Tools

• Descriptive
• Predictive
• Web Analytics
• Google Analytics

Fundamentals Of R

• R and features
• Evolution of R?
• Bigdata Hadoop and R

Working With R & RStudio

• R & RStudio Installation

Data Types

• Scalar
• Vectors
• Matrix
• List
• Data frames
• Factors
• Handling date in R
• Conversion of data types
• Operators in R

Importing Data

• JSON files
• CSV files
• Database data (Oracle 11g)
• XML files
• Reading & Writing PDF files
• Reading & Writing JPEG files
• Saving Data in R

Manipulating Data

• Sorting
• Cbind, Rbind
• Aggregating
• Dplyr

Conditional Statements

• For loop
• If …else
• While loop
• Repeat loop

Functions

• tApply ()
• Apply ()
• sApply ()
• rApply ()

Statistical Concepts

• Descriptive Statistics
• Inferential Statistics
• Central Tendency (Mean,Mode,Median)
• Hypothesis Testing
• Probability
• tTest
• zTest
• Chi Square test
• tTest
• Correlation
• Covariance
• Anova

Predictive Modelling

• Linear Regression
• Normal Distribution
• Density

Data Visualization In R Using GGPlot

• Box Plot
• Histograms
• Scatter Plotter
• Line chart
• Bar Chart
• Heat maps

Data Visualization Using Plotly

• 3D-view
• Geo Maps

Misc. Functions

• Null Handling
• Merge
• Grep
• Scan

Advance Topics In R

• Text Mining
• Exploratory Data Analysis
• Machine Learning with R (concept)

Data Science With R

• Algorithms
• Classifications
• Clustering
• Supervised learning
• Unsupervised learning
• Bayesian
• Boosting
• K-means
• Nearest Neighbours (KNN)
• Page Rank
• Support Vector Machines
• Random forest