# Data Analytics Course Online

Upskill your team in data analysis to drive better business decisions with our business analytics online course. Equip your employees with the ability to conduct Data Analysis using three popular Data analysis tools – Excel, R and Tableau.

##### Key Learning Objectives:
• Explore basic statistical skills including fundamental theories and terminology
• Discover R, data cleaning, data visualisation and packages in R that can be used for data analysis
• Use Excel for descriptive statistics
• Investigate Tableau as a data analysis platform
• Interpret and present results
• Learn to analyse data and gain a solid foundation in statistics to better interpret your data for the purpose of enhancing your business making decisions.

Weeks

Modules

Lessons

## MODULE 1 Diploma in Data Analytics

###### 1.Starting your Data Analyst Journey

Your journey to becoming a data analyst will start by exploring 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

Let’s consider data in a bit more detail. Our 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 implement descriptive statistics "features" in data.

###### 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.

###### 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 analysing 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, is an important step in our journey. We will expand on what a null and alternative hypothesis is and explore the difference between a Type 1 and Type 2 error. For further enlightenment on this topic, we consider information on the central limit theorem or the law of large numbers.

###### 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.

## MODULE 2 Intermediate in Data Analytics

###### 1.Introduction to R

The objectives for this lesson is to add a new tool to your 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. The lesson will conclude 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. Thereafter, we will use R to reproduce some of the exploratory data analysis completed with the Titanic dataset in Excel in Module 1. Today's lesson will conclude with an introduction to merging and joining datasets.

###### 3.Introduction to Linear Regression

It’s time to introduce linear regression. Thereafter we will dive deeper into understanding the concept of correlation. The lesson will end by going back to basics with vectors and factors in R.

###### 4.Linear Regression Continued

Let’s broaden your understanding of linear regression and data frames. In this lesson we will consider what it means for the model to fit the data well and gain some further insight into treating data in R. The lesson will end by exploring some basics surrounding dates values in R.

###### 5.Dates and Times

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 discuss the concepts surrounding time series analysis that was introduced in the previous lesson and add some new concepts. Thereafter we will break down and analyse some of the time series models for better understanding. This lesson will end by looking at how we can apply these concepts learnt in R in a more practical sense.

###### 7.Multiple Linear Regression

Let’s elaborate on the principles of multiple linear regression. In this lesson we will elaborate on the assumptions that accompany multiple 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 a model does not fit a linear trend well, in other words, if the data is non-linear. The lesson will end by introducing logistic regression.

###### 8.Introduction to Logistic Regression

Lesson 8 will elaborate on the introduction to logistic regression from lesson 7 and now aim to identify when to utilize this model and how to interpret the outcome. Thereafter we will delve into the model fit statistics that was briefly discussed in previous lessons and end the lesson by cementing the knowledge we have gained through module 2 with a practical demonstration.