Data Science - R Programming certification eLearning

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Data Science - R Programming certification eLearning

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Description

Data Science - R Programming certification eLearning

Learn how to extract knowledge and insights from structured and unstructured data

R is a programming language and free software environment for statistical computing. This data science training course teaches you various data analytics techniques using the R programming language and you will also master data exploration, visualisation, predictive and descriptive analytics techniques.

During the course, you’ll get hands-on practice by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, and many more.

ABOUT THE COURSE

This data science course forms an ideal package for aspiring data a…

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Didn't find what you were looking for? See also: R Programming, Programming (general), Science, Database Management, and Data Storage.

Data Science - R Programming certification eLearning

Learn how to extract knowledge and insights from structured and unstructured data

R is a programming language and free software environment for statistical computing. This data science training course teaches you various data analytics techniques using the R programming language and you will also master data exploration, visualisation, predictive and descriptive analytics techniques.

During the course, you’ll get hands-on practice by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, and many more.

ABOUT THE COURSE

This data science course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. You will acquire a 360-degree overview of business analytics and R, with the help of real-life projects and case studies.

LEARNING OBJECTIVES

By the end of this course you will be able to use:

  • The various graphics in R for data visualisation
  • Hypothesis testing method to drive business decisions
  • Linear, non-linear regression models, and classification techniques for data analysis
  • The various association rules and Apriori algorithm
  • Clustering methods including K-means, DBSCAN, and hierarchical clustering

You will also be able to:

  • Gain a basic understanding of business analytics and various statistical concepts
  • Install R, R-studio, and workspace setup
  • Master R programming and understand how various statements are executed in R
  • Understand data structure used in R and to import/export data
  • Define, understand and use the various apply functions and DPYR functions
  • Understand various statistical concepts and hypothesis testing

WHAT'S INCLUDED?

  • One year access to the platform
  • Seven practical projects to make the learned skills perfect
  • Simulation test paper for self-assessment
  • Duration approx. 12 hours
  • Access around the clock

To obtain a course completion certificate, you must complete the online learning course in full.

 

WHAT'S COVERED?

The course will cover the following topics:

Course introduction

Lesson 1 - Introduction to business analytics

Lesson 2 - Introduction to R programming

Lesson 3 - Data structures

Lesson 4 - Data visualisation

Lesson 5 - Statistics for Data Science-I

Lesson 6 - Statistics for Data Science-II

Lesson 7 - Regression analysis

Lesson 8 - Classification

Lesson 9 - Clustering

Lesson 10 - Association

FREE COURSE - Business analytics with Excel

FREE COURSE - Statistics essentials for data science

The data science certification course includes ten real-life, industry-based projects — successful evaluation of one of them is a part of the certification eligibility criteria. There are also a further four more projects included you to practice.

Project 1: Products rating prediction for Amazon

E-commerce: Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.

Project 2: Demand Forecasting for Walmart

Retail: Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.

Project 3: Improving customer experience for Comcast

Telecoms: Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.

Project 4: Attrition Analysis for IBM

Workforce analytics: IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.

Project 5 Healthcare: A nationwide survey of hospital costs conducted by the US Agency for Healthcare consists of hospital records of inpatient samples. The given data is restricted to the city of Wisconsin and relates to patients in the age group 0-17 years. The agency wants to analyse the data to research on the health care costs and their utilisation.

Project 6 Insurance: The data gives the details of third-party motor insurance claims in Sweden for the year 1977. In Sweden, all motor insurance companies apply identical risk arguments to classify customers, and thus their portfolios and their claims statistics can be combined. The data were compiled by a Swedish Committee on the Analysis of Risk Premium in Motor Insurance. The Committee was asked to look into the problem of analysing the real influence on the claims of the risk arguments and to compare this structure with the actual tariff.

Project 7 Retail: A high-end fashion retail store is looking to expand its products. It wants to understand the market and find the current trends in the industry. It has a database of all products with attributes, such as style, material, season, and the sales of the products over a period of two months.

Project 8 Internet: The web analytics team of www.datadb.com is interested in understanding the web activities of the site, which are the sources used to access the website. They have a database that states the keywords of time in the page, source group, bounces, exits, unique page views, and visits.

Project 9 Education: An education department in the US needs to analyse the factors that influence the admission of a student into a college. Analyse the historical data and determine the key drivers.

Project 10 E-commerce: A UK-based online retail store has captured the sales data for different products for the period of one year (Nov 2016 to Dec 6th Edition). The organisation sells gifts primarily on the online platform. The customers who make a purchase consume directly for themselves. There are small businesses that buy in bulk and sell to other customers through the retail outlet channel. Find significant customers for the business who make high purchases of their favourite products.

Project 11 Music Industry: Details of listener preferences are recorded online. This data is not only used for recommending music that the listener is likely to enjoy but also to drive a focused marketing strategy that sends out advertisements for music that a listener may wish to buy. Using the demographic data, predict the music preferences of the user for targeted advertising.

Project 12 Finance: You’ll predict whether someone will default or not default on a loan based on user demographic data. You’ll perform logistic regression by considering the loan’s features and the characteristics of the borrower as explanatory variables.

Project 13 Unemployment: Analyse the monthly, seasonally-adjusted unemployment rates for U.S. employment data of all 50 states, covering the period from January 1976 through August 2010. The requirement is to cluster the states into groups that are alike using a feature vector.

Project 14 Airline: Flight delays are frequently experienced when flying from the Washington DC area to the New York City area. By using logistical regression, you’ll identify flights that are likely to be delayed. The provided dataset helps with several variables including airports and flight times.

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