Predictive analytics courses you can take online this summer

03 Jul 2019

New technologies such as artificial intelligence (AI), machine learning and big data are growing at an incredible rate.

Because of this, data scientist jobs are increasingly in demand and will be long into the future, as companies struggle to keep up and make sense of all the data that can now be collected and stored through the development of cloud-based technologies.

Employers are increasingly looking for employees who are well-versed in different components of data science, depending on the business.

Forrester Research analyst Brandon Purcell said that organisations increasingly rely on insights driven by data.

“Data scientists are crucial in turning the massive amount of data companies capture into action. They’ve always been in high demand, but until recently, only large enterprises and digital natives were willing to make the significant investment. Now, almost everyone is.”

In eCommerce, predictive analytics is an essential tool that’s also growing at an unprecedented rate. Predictive analytics uses statistics, modeling, machine learning and data mining to make predictions for the future.

Companies use predictive analytics to better understand consumer behavior and engagement, as well as to build more sustainable business practices.

Why retailers are flocking to predictive analytics https://t.co/I8tkbLJ6aW

— Samuel Wong (@SamueL_WonG_) June 26, 2019

If you’re already enrolled in a degree programme or have have graduated from a subject that isn’t data science-related, it’s not too late to acquire this highly sought-after skill.

There are many online courses in predictive analytics open for you to take, and some are even free. It’s a great way to learn the basics about this emerging field, and for you to find out if it’s something you’re interested in doing.

Here are three popular online courses by reputable online education providers in predictive analytics that you can take this summer.

Practical Predictive Analytics: Models and Methods

Through this Coursera programme, you can choose select courses to start from or go straight ahead with the whole course.

The Data Science at Scale Specialization, offered by Washington University through Coursera, includes the Practical Predictive Analytics: Models and Methods course in which learners design statistical experiments and analyse the results through modern methods.

According to the website, “You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.”

The course has flexible deadlines and takes approximately four weeks of study to complete (6-8 hours per week).

Customer Analytics

As part of the Business Analytics Specialization offered by WhartonOnline via Coursera, this course is ideal for those interested in how predictive analytics works for retail and consumer behaviour.

After completing the course, you should be able to describe the major methods of customer data collection used by companies and understand how this data can inform business decisions, as well as the main tools used to predict customer behaviour and identify the appropriate uses for each tool.

You will also learn how to communicate key ideas about customer analytics and how the field informs business decisions.

This course also has flexible deadlines and is estimated to take four weeks of study (5-6
hours/week) to complete.

Analytics for Decision Making

For those keen on learning how to use data analytics for business, this course on EdX, offered by Babson, is ideal for gaining a strong grasp on the concepts.

After completion of the course, you should be able to understand the foundational theories that support modern data science and how to analyse various data types and quality to make smart business decisions.

According to the website, “We will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?

“This course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor’s degree contemplating graduate study in a business field.”

The course can be completed in four weeks, with an average of 4 to 6 hours of study a week.