Shaw Academy

Your Future, Your Way!

Course Catalogue

Get 4 Weeks FreeLogin


All Levels


Most of this week’s places have been filled.

Start your course now to guarantee your place.


What you'll learn

  • Basic statistical skills such as fundamental theories and terminology
  • 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
  • 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 lesson in this module is carefully balanced between theory and practicals, and in this lesson, you will learn how to import and clean data using a variety of methods and tools. You will focus on logical checks to help guide you towards thinking about data in a more logical fashion.

  2. Exploring Data

    In this lesson, you will begin to understand 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 your journey continues, you will learn how to install the Data Analysis Toolpak together with some descriptive stats. This lesson 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 you have previously covered (the mean) with one you 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, you will also learn about skewness and kurtosis.

  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. You 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. Hypothesising About the Outcome

    Understanding what a hypothesis is is an important step in your journey. This lesson 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.

  7. Testing for Differences: Categorical Vars

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

  8. Testing for Differences: Numerical Vars

    This module will wrap up with an understanding of testing for differences (numerical vars). In this lesson, one-sample tests, the difference between two means of two populations, and T-tests will be covered. By the end of this lesson, you will have a firm and complete understanding of the basics of data and data analysis. However, 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

    In this lesson, we add a new tool to our data analyst toolkit, called R. We will go through the basic steps of downloading and installing the tool and start exploring some of the packages that are available today in R. We will end the lesson by introducing another common method to estimate population parameters, the maximum likelihood method.

  2. Data Wrangling

    The first topic will introduce the brilliant package tidyverse by hadley wickham, the chief data scientist at rstudio. Thereafter, we will use R to reproduce some of the exploratory data analysis we have done with the titanic dataset in excel in module 1. We will end this lesson with a short and sweet introduction to merging and joining datasets.

  3. Introduction to Linear Regression

    The first topic for this lesson will introduce linear regression, thereafter we will dive deeper into understanding the concept of correlation. We will end this lesson by going back to basics with vectors and factors in R.

  4. Linear Regression Continued

    This lesson will continue to broaden our understanding of linear regression and data frames. We will understand what it means for the model to fit the data well and gain some further insight into treating data in R. We will end the lesson by exploring some basics surrounding dates values in R.

  5. Dates and Times

    Lesson 5 will continue to broaden our understanding of dealing with dates and times in r. Many datasets contain dates and times and we need to make the step of data handling dates and times as simple and effective in our data analytics arsenal as possible. Therefore, we will continue building on dates and times data wrangling throughout a large part of this lesson. We will end the lesson by introducing time series analysis concepts.

  6. Time Series Analysis

    This lesson will delve deeper into time series analysis. We will further discuss the concepts surrounding time series analysis that we introduced in the previous lesson and add some new concepts to that. Thereafter we will break down and understand some of the time series models a bit better. We will end today’s lesson by looking at how we can apply these concepts learnt in R in a more practical sense.

  7. Multiple Linear Regression

    In this lesson, we will elaborate on the principles of multiple linear regression. We will talk more about the assumptions that accompany linear regression, how to simplify a multiple linear regression model, and problems that can occur when fitting a multiple linear regression model to the data. Thereafter, we will discuss what happens if your model does not fit a linear trend well, in other words, if the data is non-linear. The lesson will end with an introduction to logistic regression.

  8. Introduction to Logistic Regression

    In this lesson, we will elaborate on the introduction to logistic regression from lesson 7. We will better understand when to utilize this model and how to interpret the outcome. Thereafter we will elaborate on the model fit statistics we have briefly touched on in previous lessons, such as the AIC and BIC statistics. We will end the lesson by cementing in all the knowledge we have gained through module 2 with a practical demonstration.

module 3

Advanced in Data Analytics



  1. Intro to Classification

    The first lesson of Module 3 is aimed at introducing you to classification. This will cover what it is and what types of data it's used for.

  2. Logistic Regression

    Part one of logistic regression introduces linear and logistic models, GLM() in R and predication and odds ratio.

  3. More on Logistic Regression

    Part two of logistic regression explains probabilities and log odds ratios, confusion matrix as well as accuracy, sensitivity and specificity.

  4. Skrinkage Methods

    This lesson on skrinkage methods delves into lasso and ridge regression.

  5. Dimension Reduction Methods

    Lesson 5 is geared towards helping you grasp the concepts of principle component analysis and partial least squares.

  6. Subset Selection

    We then move to subset selection where we explain stepwise selection as well as forward and backward stepwise regression in more detail.

  7. Time Series Analysis

    Nearing the end of the advanced module, explore time series analysis and more specifically manipulating time series data, autoregression and moving averages.

  8. More On Time Series Analysis

    The final lesson for this module details the ARIMA models and will help you to visualise time series data more effectively.

module 4

Proficient in Data Analytics



  1. Intro to Tableau

    In lesson 1 of the final module, you will be introduced to Tableau (including tips on how to install it).

  2. Building a Business Savvy Dashboard

    This lesson will take you through the steps and best practices of creating a business savvy dashboard for your specific needs.

  3. Integrating R and Tableau

    As you understand this program in more detail, we will help you to draw appropriate insights and models from R into Tableau.

  4. Functions in Tableau

    This lesson is focused on helping you to understand all the statistical functions available in Tableau, helping you get one step closer to mastering this program.

  5. Segmentation and Cohort Analysis

    Start to unpack segmentation and cohort analysis.

  6. Scenario and What-if Analysis

    In lesson 6, unpack scenarios and what-if analysis (if then else statements using Tableau).

  7. Time Series and Predictive Analysis

    Nearing the end of the final module, we will take a deep dive into time series and predictive analysis.

  8. Presenting Your Findings and Tying It All Together

    To finalise your diploma in Data Analytics, we bring all the learnings together and help you identify the best way to present your findings based on your audience.

Certified by
Globally recognised by
  • Weeks
    16 Weeks


  • lessons
    32 Lessons

    Plus assessments

  • modules
    4 Modules


  • course
    Globally Recognised





Just finished 1st class of Data Analytics & I found it easy to follow & informative!


I really liked how the instructor explained everything from the core of what is data analysis.


I am not well versed in data analytics, but this class explained it a way that anyone can understand. I love that you can invite other people to learn as well!


Nice UI. Knowledgeable instructors. Great analytics and stats about individual's performance. Overall an amazing platform for learning.


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

You might also like

Course Benefits

Flexible online classes
You pick the schedule.
Pause course
Take a break any time.
Educator support
Always get your answers.
Offline mode
Download class recordings.
Globally Recognised Courses
International focus to curriculum.
One plan - All content.

Start building your future, your way.

Access to all 100+ courses including:
  • Live educator chat support
  • 20+ hours of learning per course
  • Exams & continuous assessments
  • Unique lessons curated by in-house experts
  • Lifetime access to all lessons
  • Globally accredited certifications

$49.99 / month

FREE for 4 weeks

Start your free course

No commitment

FREE for 4 weeks

No Commitment