Hands-On Data Analytics with R [Video]

More Information
  • Utilize a variety of R's machine learning and statistical techniques, interpret their outputs, and present these insights effectively and with confidence
  • Use dimensionality reduction to overcome this issue without too many explanatory variables
  • Mine data with common techniques such as hierarchical and K-means clustering
  • Approach data using inferential statistics to implement effective hypothesis-testing techniques, providing strong evidence for a claim about your data
  • Implement popular predictive analytic techniques that allow for the extraction of deep and hidden patterns within data
  • Ensure that the models and techniques used are appropriate; checking a model's effectiveness and reliability will make you confident in its predictive capabilities
  • Discover neat and visually effective ways of presenting your findings and predictions

This course will expand your understanding of statistics so you can* create analytic models in R. High-level data science techniques will be presented to you in a practical manner, to help you bridge the gap between the questions you wish to answer, the data used for analysis, and how to create some of the classic models used in data analytics.

You will start off by understanding dimensionality reduction and data mining in R and learning how to simplify complex datasets and unearth patterns from data. Moving on, you will understand hypothesis testing and p-values. You will also demonstrate one-sample and two-sample tests and the benefits they provide as very sophisticated analytical techniques. You will understand how data can give you predictive insights into the future and will conclude by presenting data in a way that will allow you to answer questions with data-driven confidence.

By the end of the course, you will be capable of utilizing R's statistical prowess to analyze a variety of datasets and present these insights effectively.

All the code files for this course are available on Github at - https://github.com/PacktPublishing/Hands-On-Data-Analytics-with-R--V-

Style and Approach

This course is designed to provide knowledge in those techniques most frequently used by covering the most commonly encountered areas of statistics and machine learning utilized in business and science today. Here, you will gain the skills necessary to analyze, predict, and present insights gathered by using these techniques in a practical setting.

  • Accelerate your data analytic capabilities from a basic understanding to being able to apply and interpret results effectively, providing deep insight into a plethora of data-driven scenarios
  • Contains the maximum amount of practical examples and mini-tests with minimal lecturing to encourage a natural and self-rewarding progression
  • Perform and interpret results from the most commonly used statistical and ML techniques used by data professionals such as linear models, k-means clustering, and Principal Component Analysis.
Course Length 2 hours 15 minutes
ISBN 9781789134667
Date Of Publication 28 Mar 2019


Rahul Tiwari

Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as Big Data, Data Science, Machine Learning, and Cloud Computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to better make sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

Rahul Tiwari trains and consults organizations and individuals on Business Analytics, Data Science, and Machine Learning (Using R and Python). For 12 years, he has been helping students and organizations in various domains (such as retail, telecom, life sciences, finance, and more) solve their business problems using Data Science, Business Analytics, and Machine Learning. He has implemented machine learning algorithms in R extensively. He worked on various classification and regression models for his clients using R and Python. He has a sound knowledge of statistics as well, which is very much necessary for Data Science projects.

After starting his career 12 years back in data warehousing, he moved on to the Data Science domain and held various roles. Mostly working with CTOs, key IT decision makers, and students, he has always focused on building capacity, knowledge, and solutions in Data Science, Business Analytics, and Machine Learning.

He is a certified Tableau and Teradata associate. His core expertise is in R, Python, Tableau, Power BI, and SQL.