Data Analytics

32 Hours / Access Length: 180 Days / Delivery: Online, Self-Paced

Course Overview:

Learn to analyze data to make better decisions and gain a solid foundation in statistics to better interpret your data in this online data analytics training course.

Students will:

• Basic statistical skills such as fundamental theories and terminology
• Get an introduction to R, data cleaning, data visualization 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 Outline:

Module 1:

Lesson 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.

Lesson 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 implement descriptive statistics "features" in data.

Lesson 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).

Lesson 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.

Lesson 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!

Lesson 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.

Lesson 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.

Lesson 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:

Lesson 1: Introduction to R         

The objectives for this lesson are to add a new tool to your data analyst toolkit, called R. You 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.

Lesson 2: Data Wrangling             

The first topic will introduce the brilliant package, tidyverse. Thereafter, you will use R to reproduce some of the exploratory data analysis you have done with the Titanic dataset in Excel in Module 1. Today's lesson will conclude with an introduction to merging and joining datasets.

Lesson 3: Introduction to Linear Regression        

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

Lesson 4: Linear Regression Continued  

This lesson will continue to broaden your understanding of linear regression and data frames. You will understand 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.

Lesson 5: Dates and Times          

In this lesson, we 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.

Lesson 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. This lesson will end by looking at how we can apply these concepts learnt in R in a more practical sense.

Lesson 7: Multiple Linear Regression      

In this lesson, we will elaborate on the principles of multiple linear regression. 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 your 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.

Lesson 8: Introduction to Logistic Regression      

Lesson 8 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.

All necessary course materials are included.

• Cable and DSL internet connections are recommended for the best experience.

• CPU: 1 GHz or higher
• RAM: 2 GB or higher
• Resolution: 1280 x 720 or higher
• Speakers / Headphones
• Microphone (Webinar / Live Online sessions)

• Microsoft Windows 7 or 10 (Home, Pro)
• Mac OSX 10 or higher.
• Latest Chrome OS
• Latest Linux Distributions

NOTE: While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.

• Latest Google Chrome is recommended for the best experience.
• Latest Mozilla FireFox
• Latest Microsoft Edge
• Latest Apple Safari

• Office suite software (Microsoft Office, OpenOffice, or LibreOffice)
• PDF reader program (Adobe Reader, FoxIt)
• Courses may require other software that is denoted in the above course outline.