Whether you’re a student interested in the field or an employee looking to upskill yourself, taking online courses in predictive analytics is a great way to get your foot in the door of an emerging field.
Thousands of workers and students have now been sent home amidst the COVID-19 outbreak. People are finding themselves confined at home with plenty of hours to fill, so why not use them productively to learn something new?
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.
In e-commerce, 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 enroled in a degree programme or have graduated from a subject that isn’t data science-related, it’s not too late to acquire this highly sought-after skill by taking predictive analytics courses online.
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 pursuing as a career.
Here are three popular online courses by reputable online education providers in predictive analytics that you can take while we weather the COVID-19 storm.
Through this Coursera programme, you can choose courses in predictive analytics to start from or go straight ahead with the full course which includes other subjects such as data science, machine learning, statistics, data analysis, etc.
The Data Science at Scale Specialisation, 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 internalise 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 (six to eight hours per week).
The Business Analytics Specialization offered by WhartonOnline via Coursera 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 (five to six hours per week) to complete.
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 completing 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.
Are traditional statistical methods still relevant in modern analytics applications? What are the common fallacies and misconceptions when approaching quantitative problems? How can analytics help us gauge customer wants and needs better? These are the questions the course will seek to answer.
The course can be completed in four weeks, with an average of four to six hours of study a week.