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Data Science

Course type:
E-learning
Duration:

40 hours

Delivery:
Online
Feefo rating

Data Science - R Programming E-learning

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.

Course overview

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.

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 the following six projects is a part of the certification eligibility criteria:

  • Project 1: 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 2:

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 3:

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 4:

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 5:

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 6: 

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 7:

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 2017). 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 8:

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 9:

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 10:

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 11:

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 a number of variables including airports and flight times.

Duration

40 hours

Target audience

There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We recommend this Data Science training particularly for the following professionals:

  • IT professionals looking for a career switch into data science and analytics
  • Software developers looking for a career switch into data science and analytics
  • Professionals working in data and business analytics
  • Graduates looking to build a career in analytics and data science
  • Anyone with a genuine interest in the data science field
  • Experienced professionals who would like to harness data science in their fields

Pre-requisites

There are no prerequisites for this data science online training course. 

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?

Data Science R Programming is offered by Simplilearn, a partner of ILX Group.

Materials

  • 24 hours of self-paced video
  • 10 real-life industry projects in retail, insurance, finance, airlines, and other domains

Duration of access

12 months online access to accredited e-learning

Exam information

  • Complete any one project out of the four provided in the course. 
  • Score a minimum of 60% in any one of the two simulation tests
  • Pass the online exam with a minimum score of 80%

You have a maximum of three attempts to pass the Data Science - R Programming certification exam. Simplilearn provides guidance and support for learners to help them pass the exam. 

Exam format

Online self-learning:

  • Complete 85% of the course
  • Complete one project