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What you'll learn

  • Basic statistical skills such as fundamental theories and terminology
  • Get an introduction to R, data cleaning, data visualisation and packages in R that can be used for data analysis
  • How to use Excel for descriptive statistics
  • An introduction to Tableau
  • How to interpret and present results

Course Content

module 1

Diploma in Data Analytics




  1. Starting your Data Analyst journey

    Your journey to becoming a data analyst will start by understanding more about data and the analysis thereof. This lesson is geared to help you understand why data analysis is an important skill as well as where it can be used to enhance your business making decisions. Each one of our lessons is carefully balanced between theory and practical and in this lesson, you can expect to learn how to import and clean data using a variety of methods and tools. We will focus on logical checks to help guide you towards thinking about data in a more logical fashion.

  2. Exploring data

    In this lesson we will understand the data in a bit more detail. The aim is to assist you in understanding the different data types (such as categorical vs numerical) as well as understanding graphically represented data. You will also learn how to describe data (i.e. descriptive statistics) and how to use them.

  3. Probability

    As our journey continues, you will learn how to install the Data Analysis Toolpak together with some descriptive stats. We will touch briefly on the basics of probability (with specific reference to Bayes theorem) and delve into the details of mean and variance of random variables. This topic very neatly ties together a concept that we have covered (the mean) with one we are yet to cover (variance).

  4. Distributing data

    Lesson 4 is all about distributing data. You will learn about the various data distributions (with reference to the Central Limit Theorem) and understand how to use mean, median and standard deviation to know how your data is distributed. Lastly, we will also expand on skewness and kurtosis, so tune in if you are dying to know what this means for your data.

  5. How confident are you in the sample?

    Being confident in your sample is important. This lesson will focus on understanding the difference between a sample and a population as well as when to use variance or standard deviation for each. We will also cover confidence intervals in more detail, and by the end of this lesson you will be well on your way to feeling more confident!

  6. Hypothesizing about the outcome

    Understanding what a hypothesis is an important step in your journey. We will expand on what a null and alternative hypothesis is and explore the difference between a Type 1 and Type 2 error. This lesson will also include more information on the Central limit theorem/ the law of large numbers to enlighten us on this topic.

  7. Testing for differences: categorical vars

    Our penultimate lesson is focused on testing for differences (categorical vars). We will explore one sample tests, the difference between 2 means of 2 populations as well as Chi-square tests.

  8. Testing for differences: numerical vars

    Finally, we wrap up with understanding testing for differences (numerical vars). In this lesson one sample tests, the difference between 2 means of 2 populations and T-tests will be covered and by the end of this lesson you will have a firm and complete understanding of the basics of data and data analysis. The journey does not stop here and in Module 2 you can expect more complex concepts and a deeper understanding of the topic.

module 2

Intermediate in Data Analytics



  1. Introducing R

  2. Looking back at the journey

  3. Joining and tidying datasets

  4. Transformations

  5. Dates and times

  6. Linear regression

  7. More on linear regression

  8. Multiple linear regression and bringing everything together

module 3

Advanced in Data Analytics



  1. Intro to classification

  2. Logistic regression

  3. Logistic regression part 2

  4. Shrinkage methods

  5. Dimension reduction methods

  6. Subset selection

  7. Time series analysis

  8. Time series analysis part 2

module 4

Proficient in Data Analytics



  1. Intro to Tableau

  2. Building a business savvy dashboard

  3. Integrating R and Tableau

  4. Functions in Tableau

  5. Segmentation and cohort analysis

  6. Scenario and what-if analysis

  7. Time series and predictive analysis

  8. Presenting your findings and tying it all together

Certified by
Globally recognised by
  • Weeks
    4 Weeks


  • lessons
    8 Lessons

    Plus assessments

  • modules
    1 Modules


  • course
    Globally Recognised


Avg. Rating



It's Amazing, and the timing is perfect for the middle east guys like me.


The teachers at Shaw Academy are absolutely amazing, thank you so much for everything you have helped me with so far!


Plenty of good and practical information! I know I'm gonna return to Shaw Academy for other courses.


Best ever available resource to get educated , To fulfill your dream


A quick overview about Data that can be really useful to get a better understanding on this topic & this course does a good job..

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