Forecast the Future: How to Learn Predictive Analytics

We all wish we could see what the future holds. In fields like risk management and finance, knowing the future would not only set your mind at ease. While you can’t know the future with absolute certainty, there are ways to make accurate projections. You can do this by learning predictive analytics.

What Is Predictive Analytics?

Predictive analytics is the harnessing of data to predict a specific outcome. Using statistical and predictive models, experts can, in most cases, predict a business outcome more accurately. 

For example, those using predictive modeling can determine if a product at Walmart will perform well over the winter. A data scientist can utilize predictive analysis models to inform corporations and companies about credit risk and future sales. However, that is just the tip of the iceberg. Let’s take a deeper look into what data analysis and predictive analytics can do. 

It all falls under the giant umbrella of data science. Predictive analysis is an essential subfield in data science and one of the main reasons data science is so crucial to our ever-evolving world.

If you are familiar with harnessing data for robust predictions, you may see similarities between data mining and data science. However, there are a few key differences that set the two practices apart. 

What Is Predictive Analytics Used For?

While its most significant uses are in the business sector, you can find uses for predictive analytics in nearly all areas where there is potential financial risk and reward. Let’s explore some of its primary functions. 

• Making predictions about the future. Instead of a crystal ball, data scientists and other experts use machine learning, neural networks, and the practice of analyzing data to predict possible positive and negative outcomes to business decisions. 

Will investing in a startup prove to be a prudent move, or will it spell disaster? Exploring data can change the course of success for tons of businesses. This is where the crucial skills of a data scientist come in.

• Finding trends. Predictive analysis falls under the umbrella of data analytics, as it can more accurately spot trends and potential risks. In financial services, knowing what lies on the horizon can mean the difference between profit and loss. 

Predictive analysis can help us determine what trends in customer behavior become apparent through extensive data sets, decision trees, and other data collected about a particular subject.

• Credit scoring. More specifically, predictive analysis is a significant aspect of credit scoring. Is an individual or company a credit risk? By using predictive analytics, financial professionals can better predict the future behaviors of individuals and their likelihood of paying off debts or applying for loans.

• Finding relationships between two variables. Those familiar with statistics and data collection will immediately know the significance of different variables and see how they interact with one another. Predictive analytics seeks to spot a relation between the explanatory and the predicted variable. Analyzing big data through various means like artificial intelligence can mean the difference between robust predictions and weak ones.

Types of Predictive Analytics

Predictive analytics may seem rather specific, but there are actually a few subcategorizations.

Predictive Modeling

This is the bread and butter of predictive analytics. Experts use predictive modeling to ascertain the possible performance of a product in the future. Predictive modeling gets valuable assistance from advances in artificial intelligence. 

With the help of things like machine learning and prescriptive analytics, data scientists can use past performances to better predict how a unit will perform in the future. Predictive modeling focuses mostly on singular customers and products to see what future trends might appear.

With predictive modeling, the most important data is collected through actual transactions of the unit. This is where machine learning comes in and where artificial intelligence can parse specific interactions. 

Descriptive Modeling

Descriptive modeling, on the other hand, groups both products and customers into more extensive categories to analyze data. Where predictive analytics may try to elucidate the relationship between the purchase of a winter coat and customer, descriptive modeling attempts to find multiple correlations between large quantities of units, products, and consumers. 

Descriptive modeling can determine what products someone in a particular cohort will buy or what brands do better with specific genders.

Decision Models

Decision models are templates that companies and professionals can use multiple times to achieve success. By intensively studying collected data, data scientists can construct a decision model that, with past evidence and luck, can reliably forecast the future multiple times. This usually requires a large amount of data collected over a long period of time. 

Once data scientists and data analysts find enough concrete relationships between multiple explanatory variables, they can more reliably construct robust decision models for future success. Ideally, decision models are solid enough to implement for various financial quarters and years.

Learning Predictive Analytics

While predictive analysis may seem a bit complicated, even for the statistically gifted, nearly anyone can learn the valuable practices and data modeling needed to help in critical business decisions. 

Let’s see how long it takes individuals to learn predictive analytics and how to go about learning it. We also show you some invaluable resources to help you on your journey. 

How Long Does It Take to Learn Predictive Analytics?

A few months to a few years is the short answer. Unfortunately, predictive analytics isn’t something you can pick up overnight. Even when you do learn enough about predictive analysis, implementing different analytical techniques at a job can take months. 

You need to either have a background in statistics and data science or dedicate yourself to learning new things. However, let’s assume that you are in the latter camp. How do you learn statistics and predictive analysis from scratch?

How to Learn Predictive Analytics: Step-by-Step

Here we will outline specific steps that you can take to make learning predictive analytics a little smoother.

1. Familiarize yourself with statistical concepts. If you want to have any hope of successfully learning predictive analytics, you need to know many statistical concepts and analytical techniques. This means you should be familiar with the different types of variables, linear regressions, graphs, standard deviation, and clusters. Learning these terms from scratch can take a while, especially for those with no affinity for data science or even economics. 
2. Take predictive analytics courses. Like many other subjects, there is a bevy of both offline and online courses. If you want in-person predictive analytics courses, your best bet is something in a computer science or data science degree program to educate you on multiple aspects of analytical techniques. 
3. Complete predictive analytics projects. Different projects and exercises can help you brush up on skills and strengthen your grasp of analytical techniques. Examine predictive models from places like businesses, financial services, and even child protection services to see how to apply principles you have learned. This can also help to build a portfolio. 
4. Search for a predictive analytics or data scientist job. Applying for a data scientist job may not be that simple. However, tons of businesses, like financial institutions and other corporations, absolutely need the expertise of talented and experienced predictive analyzers. Many of their business models and financial decisions rest on the risk and reward potential from predictive analytics. 

The Best Predictive Analytics Courses and Training

Joining courses and taking advantage of various training can increase your chances of understanding the nuance behind predictive modeling and landing an impressive job. Let’s explore some educational outlets to enhance your knowledge. 

Best Online Predictive Analytics Courses

Since everything is online, there is no better place to learn things like Excel, Stata, SPSS, and other statistical resources than the web. Here are some of the stand out classes you can access right now. 

Rutgers Data Science Bootcamp

• Provider: Rutgers University
• Time: 24 weeks
• Prerequisites: None

Data science bootcamps are a fantastic way to learn the ins and outs of data science and other subjects in a short period of time. This data science bootcamp, offered by Rutgers University, has participants learn everything they need to become predictive analysis experts. 

The course covers programming languages, Excel, learning SQL, machine learning, and artificial intelligence. It is perfect for both absolute beginners and those with some experience in the field. Participants will learn essential practices and predictive analytics principles through projects using real data sets.

Learning Path: Python: Predictive Analysis with Python

• Provider: Udemy 
• Time: 9 hours
• Prerequisites: None
• Cost: $95

This Udemy course dealing with Pandas and Python will help you in your predictive analytics education. Not only will you learn valuable data analysis techniques, but you will also familiarize yourself with the many uses of Python and how it can help in mining data for analysis.

Along with utilizing Pandas frameworks and Python to parse through data sets, students will understand how predictive analysis fits into financial services. They will be able to determine who is a credit risk or how to best allocate funds.

"Career Karma entered my life when I needed it most and quickly helped me match with a bootcamp. Two months after graduating, I found my dream job that aligned with my values and goals in life!"

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Find Your Bootcamp Match

There are four sections and 86 lectures that will fully introduce learners to the ways of predictive analytics. The course takes you through many different methods for extrapolating data and using it for predictive analysis by using Python, Pandas, SQL, models, and algorithms. 

Best Free Predictive Analytics Courses

What if you are interested in predictive analytics, but aren’t prepared to take the plunge, so to speak? Doling out a large amount of money for a course can be scary, especially if it is something you aren’t at all familiar with. Luckily, there are quite a few excellent free predictive analytics courses from reputable institutions.

Predictive Analytics using Machine Learning

• Provider: edX, the University of Edinburgh 
• Time: 6 weeks
• Prerequisites: None
• Cost: Free

From the University of Edinburgh in Scotland comes this predictive analytics crash course. This class focuses primarily on the absolutely essential relationship between machine learning, artificial intelligence, and predictive analytics. 

Machine learning refers to the process that enables machines to work on their own and assist data scientists. This predictive analytics course introduces you to machine learning and data science. 

Through six weeks of units on neural networks, big data, and decision trees, among other topics, students will understand the importance of machine learning for predictive analysis. This is a more advanced data science class. While there are no prerequisites, you should have a basic grasp of data science concepts. 

Machine Learning for Data Science and Analytics

• Provider: edX, Columbia University
• Time: Five weeks
• Prerequisites: None
• Cost: Free

From the illustrious Columbia University in New York City comes this fantastic free course in predictive analytics and data science. 

Students will learn all about machine learning, and its impact on data science and the world at large. The course explores the profound implications of this technology and how it can be employed to make predictions. The class uses real-world examples to better illustrate the topics explored.

This is a great introductory course for anyone not already familiar with analytical techniques and data science. It is entirely self-paced, so no need to stress about deadlines and time management. Although not entirely necessary, knowing the basics of Python is highly recommended. 

Best Online Predictive Analytics Resources

If classes aren’t your thing or if you want to enhance the knowledge gleaned from predictive analytic courses, there are great resources online, including forums, websites, and blogs. Let’s see some of them.

LinkedIn Learning Analytics

As well as connecting people through networks, LinkedIn provides many training and resources in various fields. LinkedIn Learning, in particular, is a reliable training resource for those looking to expand their professional skills to include predictive analytics. 

While this is technically a course, it is entirely open and free, available at any time. That means no need for enrollment. Simply follow the link and start watching lectures. This is a great, casual way to learn some of the basics of both data and predictive analytics. Taught by developer and educator Robin Hunt, this online predictive analytics resource can introduce even the most unfamiliar beginner to their future career choice. 

The Works of Eric Siegel: Predictive Analytics Expert

Can one person alone be a useful resource in predictive analytics? In Eric Siegel’s case, the answer is a resounding ‘yes’. Eric Siegel is a predictive analytics expert who has written and spoken extensively on the subject for years. 

His book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, Or Die, is one of the definitive texts on predictive analytics. He offers his invaluable insights to both beginners and experts in the field. 

Here are a few informative speeches and videos:

• Eric Siegel – Predictive Analytics – Keynote 2016
• Machine learning in 20 seconds – from Coursera’s “Machine Learning for Everyone”
• Eric Siegel answers eight questions about predictive analytics

Even if you don’t pick up his book, his videos offer a surprising amount of background and tips on predictive analytics and data science in general. His speeches and videos make complicated subjects much more palatable, making it easier to learn more advanced topics. 

Should You Study Predictive Analytics?

If you are someone who likes to predict business trends and extrapolate data from various companies and individuals, then learning predictive analysis can be a boon to you and your career. 

Although data science comprises many subfields and subsets, predictive analytics is arguably one of the most important for success. Financial services, corporations, and even individuals all benefit from learning the basics of data and predictive analytics. Knowing what is a good business decision, who is a credit risk, and what past data can tell you about the future is invaluable knowledge that is always in high demand. 

Do you see yourself as someone who can master predictive modeling? Are you confident enough in your ability to create robust decision models? Either way, knowing how to perform effective predictive analytics can mean the difference between profit and loss.