Data Science Training

Making Sense of Data with Analytics

Data Science Training: Data Scientist has been called “the sexiest job of the 21st Century,”. We live in a world that’s drowning in data. Buried in these data are answers to countless questions that no one’s ever thought to ask. Data science is about evidence-based storytelling of this massive amount of data.

Objective of this course is to introduce necessary concept and techniques used in data science industry including, statistics, working with data, R programing and much more.

This course begins with a walk-through of a template data science project before diving into the R statistical programming language. You’ll learn how to load data into R and learn how to interpret and visualize the data while dealing with variables. You’ll be taught how to come to sound conclusions about your data, despite some real-world challenges. You’ll complete this course with the confidence to correctly analyze case studies, while sharing conclusions that will make a business more competitive and successful.

Course Outline

What is data science?

  • Overview of Data Science
  • Data Science as a Career
  • Data Science Team
  • What is Big Data
  • Big Data vs Business Intelligence

Field of Study

  • Why programming is Important
  • How Statistics Works
  • Importance of Mathematics

Introduction to R

  • R Graphical User Interface
  • Data Import and Export
  • Attributes and Data Types
  • Introducing Vectors, Matrices, Data Frame and Dates

Exploratory Data Analysis

  • Summarizing the Data
  • Data Distributions
  • Outlier Treatment
  • Measuring Asymmetry: Skewness and Pearson’s Median Skewness Coefficient
  • Continuous Distribution
  • Kernel Density
  • Sample and Estimated Mean, Variance and Standard Scores
  • Covariance, and Pearson’s and Spearman’s Rank Correlation

Statistical Inference

  • Points Estimates and Confidence Intervals
  • Testing Hypothesis Using Confidence Intervals
  • Testing Hypothesis Using p-values
  • Errors Types
  • Simple Linear Regression
  • Multiple Linear Regression

Data Visualization in R

  • Data Visualization Principles
  • Plots
  • Barplots
  • Heatmaps
  • Scatterplots
  • Box and Whisker plots
  • ggplot2


  • Interpretability
  • Actionable Insights
  • Visualization for Presentation
  • Reproducible Research

Course Benefits

  • Engage yourself in Data Science and boost your career
  • Understand the art and science of discovering patterns and making intelligent predictions from big data.
  • Basics of R platform, programming language concepts, common and useful R commands, and applying statistical methods.
  • Discover how to understand, interpret and convey the results of data science life cycle.

Target Audience

  • Data Analysts
  • Business Mangers
  • Project Managers
  • Operations Managers
  • Senior Managers

Decision makers from medium to large organizations from Banking, IT, Government, Media, Telecoms, Hospitality, Retail, Travel and Healthcare sectors.