Data Science Certification

This Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on ‘R’ capabilities.With companies across industries striving to bring their research and analysis (R&A) departments up to speed, the demand for qualified data scientists is rising.

 

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Leading Tools

Master Data Science using leading tools such as SAS, R, Python and Tableau

Flexible Schedule

Set and maintain flexible deadlines.

 

Experiential Learning

Hands-on learning through 6 industry projects, across multiple tools and industries

100% online courses

Start instantly and learn at your own schedule.

 

Get career assistance​ & certificate

Extensive support via resume building, interview prep, mentorship and interview opportunities

World class faculty

Learn From Industry Experts With Years Of Data Science Experience & Excellent Teaching Skills

Advantages of This Course

Learn how to analyze large amounts of data to bring out insights
Gain hands-on knowledge through the problem solving based approach of the course along with working on a project at the end of the course
Relevant examples and cases make the learning more effective and easier

This course is for students pursuing their graduation/post-graduation and for working professionals

Learn to analyze data using SQL, R, SAS, Python, Predictive Analytics & Machine Learning
Become one of the most in-demand Data Analytics in the world today

Course Curriculum

Getting Started With Data Science And Recommender Systems

  • Data Science Overview
  • Reasons to use Data Science
  • Project Lifecycle
  • Data Acquirement
  • Evaluation of Input Data
  • Transforming Data
  • Statistical and analytical methods to work with data
  • Machine Learning basics
  • Introduction to Recommender systems
  • Apache Mahout Overview

Reasons To Use, Project Lifecycle

  • What is Data Science?
  • What Kind of Problems can you solve?
  • Data Science Project Life Cycle
  • Data Science-Basic Principles
  • Data Acquisition
  • Data Collection
  • Understanding Data- Attributes in a Data, Different types of Variables
  • Build the Variable type Hierarchy
  • Two Dimensional Problem
  • Co-relation b/w the Variables- explain using Paint Tool
  • Outliers, Outlier Treatment
  • Boxplot, How to Draw a Boxplot

Acquiring Data

  • Discussion on Boxplot- also Explain
  • Example to understand variable Distributions
  • What is Percentile? – Example using Rstudio tool
  • How do we identify outliers?
  • How do we handle outliers?
  • Outlier Treatment: Using Capping/Flooring General Method
  • Distribution- What is Normal Distribution
  • Why Normal Distribution is so popular
  • Uniform Distribution
  • Skewed Distribution
  • Transformation

Machine Learning In Data Science

  • Discussion about Box plot and Outlier
  • Goal: Increase Profits of a Store
  • Areas of increasing the efficiency
  • Data Request
  • Business Problem: To maximize shop Profits
  • What are Interlinked variables
  • What is Strategy
  • Interaction b/w the Variables
  • Univariate analysis
  • Multivariate analysis
  • Bivariate analysis
  • Relation b/w Variables
  • Standardize Variables
  • What is Hypothesis?
  • Interpret the Correlation
  • Negative Correlation
  • Machine Learning

Statistical And Analytical Methods Dealing With Data, Implementation Of Recommenders Using Apache Mahout And Transforming Data

  • Correlation b/w Nominal Variables
  • Contingency Table
  • What is Expected Value?
  • What is Mean?
  • How Expected Value is differ from Mean
  • Experiment – Controlled Experiment, Uncontrolled Experiment
  • Degree of Freedom
  • Dependency b/w Nominal Variable & Continuous Variable
  • Linear Regression
  • Extrapolation and Interpolation
  • Univariate Analysis for Linear Regression
  • Building Model for Linear Regression
  • Pattern of Data means?
  • Data Processing Operation
  • What is sampling?
  • Sampling Distribution
  • Stratified Sampling Technique
  • Disproportionate Sampling Technique
  • Balanced Allocation-part of Disproportionate Sampling
  • Systematic Sampling
  • Cluster Sampling
  • 2 angels of Data Science-Statistical Learning, Machine Learning

Testing And Assessment, Production Deployment And More

  • Multi variable analysis
  • linear regration
  • Simple linear regration
  • Hypothesis testing
  • Speculation vs. claim(Query)
  • Sample
  • Step to test your hypothesis
  • performance measure
  • Generate null hypothesis
  • alternative hypothesis
  • Testing the hypothesis
  • Threshold value
  • Hypothesis testing explanation by example
  • Null Hypothesis
  • Alternative Hypothesis
  • Probability
  • Histogram of mean value
  • Revisit CHI-SQUARE independence test
  • Correlation between Nominal Variable

Business Algorithms, Simple Approaches To Prediction, Building Model, Model Deployment

  • Machine Learning
  • Importance of Algorithms
  • Supervised and Unsupervised Learning
  • Various Algorithms on Business
  • Simple approaches to Prediction
  • Predict Algorithms
  • Population data
  • sampling
  • Disproportionate Sampling
  • Steps in Model Building
  • Sample the data
  • What is K?
  • Training Data
  • Test Data
  • Validation data
  • Model Building
  • Find the accuracy
  • Rules
  • Iteration
  • Deploy the model
  • Linear regression

Getting Started With Segmentation Of Prediction And Analysis

  • Clustering
  • Cluster and Clustering with Example
  • Data Points, Grouping Data Points
  • Manual Profiling
  • Horizontal & Vertical Slicing
  • Clustering Algorithm
  • Criteria for take into Consideration before doing Clustering
  • Graphical Example
  • Clustering & Classification: Exclusive Clustering, Overlapping Clustering, Hierarchy Clustering
  • Simple Approaches to Prediction
  • Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity
  • Clustering Algorithm in Mahout
  • Probabilistic Clustering
  • Pattern Learning
  • Nearest Neighbor Prediction
  • Nearest Neighbor Analysis

Integration Of R And Hadoop

  • R introduction
  • How R is typically used
  • Features of R
  • Introduction to Big data
  • R+Hadoop
  • Ways to connect with R and Hadoop
  • Products
  • Case Study
  • Architecture
  • Steps for Installing RIMPALA
  • How to create IMPALA packages