Data Science Courses | Data Science Training with R

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30+

Hours of live, interactive sessions

12+

Lab exercises

5+

Hands-on assignments

Why learn Data Science?

Emerging from the need to analyse and leverage large volumes of data more effectively, enterprises are turning to data science and data scientists to explore the potential of Big Data. While traditionally data is gathered from a single source, data science takes it a step further, analysing data across multiple sources and platforms to gain insight into how an enterprise can enhance their business model. Moreover, data science enables enterprise to have a fresh perspective on data trends that might not be immediately obvious. Data science is set to dominate every industry, from retail to healthcare, finance and the public sector. With Data science teams playing a critical role in business strategies today, enterprises can now make informed decisions that better their operational efficiency.

The Data Science sector is expected to grow by at least 5% by 2019

Embracing data science has allowed enterprises to increase profits by 6%

Data-driven decisions facilitated a 5% increase in productivity

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Learning Outcomes

  • Analyze data find relative patterns to predict outcomes
  • Analyze continuous data in varying scenarios
  • Analyze and find patterns in data by applying various techniques
  • Expert in Confirmatory Data analysis
  • Analyze continuous data by applying various testing, regression and correlation scenarios
  • Implement key components specific to text mining and analytics aided by the real world datasets and text mining
  • Demonstrate expert knowledge in predicting outcomes

Data Science with R @ IIHT

IIHT’s Data Science with R course is designed to help learners master data analysis by deploying various techniques, algorithms by understanding and applying these features in real-time scenarios. Upon completing the course, Learners will be able to analyze data and find relative patterns to predict outcomes on a variety of data sets. Learners also pick up the skills to analyze continuous data in various scenarios by obtaining the right set of text and apply required analytics.

What you will learn in Data Science with R

• Introduction to R programming
• Programming with R
• Data Manipulation
• Data Aggregation
• Data visualization

  • Introduction
  • Mean, Median Mode
  • Variance, Standard Deviation
  • Covariance
  • Correlation
  • Standard Error
  • Noramal distribution and Fisher’s Distribution
  • Business Application Analysis

• Euclidean Norm and Distance
• Dot Product and Projection
• Mean Vector, Covariance Matrix, Precision Matrix
• Making stratified samples
• Mahalanobis Distance
• Multivariate Normal Distribution
• Bootstrapping and sub-setting
• Making samples from the Data
• Business Application Analysis
• Hands-on: working on some dataset using R/python

• Events and their Probabilities
• Rules of Probability
• Conditional Probability and Independence
• Distribution of a Random Variable
• Moment Generating functions Central
• Limit Theorem
• Expectation
• Business Application Analysis
• Hands-on: working on some dataset using R/python

• Sample and Population
• Formulate the Hypothesis
• Select an Appropriate Test
• Choose level of Significance
• Calculate Test Statistics
• Determine the Probability
• Compare the Probability and Make Decision
• Hands-on: working on some dataset using R/python

• Proportional Test
• Chi Square Test
• Fisher’s Exact Test
• Mantel Henszel test
• Business Application Analysis
• Hands-on: working on some dataset using R/python

• One Sample T-Test
• Two Independent Samples Tests
• Paired T-test
• Wilcoxon Test
• Anova
• Kruskal Wallis Test
• Hands-on: working on some dataset using R/python

• Label Encoding
• One Hot Encoding
• Finding Missing Values
• Feature Selection Parameters
• Outliers Detection in Realtime
• Miscellaneous Data Cleaning Techniques

• Web Scrapping
• Extract & Process Unstructured Text Data from Social Media
• How to Choose Correct Statistical Tools for Text Analysis
• Hands-on: working on some dataset using R/python

• Regression Model
• Multicoliniearity
• Perfromance Analysis
• Logistic Regression
• ROC Curve
• Classification Performance Metrics

What do you gain from IIHT’s Blended Learning ?

IIHT’s learning model is integrated with the latest Learning trends to ensure that the audience remains engaged and their overall learning experience is flexible, convenience and productive. What more? We provide you a unique and engaging content on a user friendly and immersive learning platform that helps you to not only attend the training sessions, but watch Learning videos, read Learning Materials, interact with fellow students, write to the faculty members, practice labs, 24x7 support from a single window that makes learning effective. The assignments and assessments designed as part of the course ensures you develop right capability to prove your worth in your existing job or with prospective employer. Our state of the art learning system helps you to connect with fellow learners who are mostly working professionals that helps you to learn through collaboration and knowledge sharing.

Key concepts will be explained by Online / Live Instructor led sessions, where syllabus material will be presented and the subject matter will be illustrated with demonstrations and examples. Tutorials and/or labs and/or group discussions (including online forums) focused on projects and problem solving will help one practice in the application of theory and procedures, allow exploration of concepts with mentors and other fellow students. You get regular feedback on your progress and understanding; assignments, as described in Overview of Assessment (below), requiring an integrated understanding of the subject matter; and private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual and technical problems.You get access to informative Learning videos from Global Experts that helps you to get larger perspective from real time perspective that you would not get in any other Live session.

FAQS

Course participants need to have a strong base in mathematics and Python programming. A good knowledge of statistics also helps.

• Big Data Specialist, business intelligence professionals and business analytics.
• Freshers who are passionate about data science
• Developers who aspire to learn Machine Learning Techniques
• People wanting to take up roles of Data Scientist
• Information Architects who want to learn up Predictive Analytics
• Statisticians are would like to improve skills in Big Data statistics

All your classes will be recorded and made available through the learning management system. You can view these videos later at your convenience.

Yes! IIHT offers an exclusive placement portal for all learners who meet certain criteria. The requirements for availing placement assistance will be notified in advanced, giving you ample time to work towards it.

You can register for the course of your choice directly from our website or head to your closest IIHT centre. You can also speak to the learning consultants, who will guide you through the process.

You can pay online. We accept net banking, UPI and most credit and debit cards. Our payment gateway also offers an EMI option if you would like to pay in installments.

To initiate a refund you may write to us at support@iiht.com and a representative will get in touch with you soon.

When you sign up for a course, you are eligible for a discount on your next course. The discount percentage will increase with every consecutive signup.  The objective of this program is to ensure that learners have an incentive to learn more without having to worry about spending too much. And hey, it is also to show you how much we treasure your association!

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