Predictive analytics is a set of techniques and methodologies used to predict future events or outcomes based on data from previous periods. Throughout the process, practitioners utilize statistical algorithms, machine learning, data visualization, and pattern recognition, along with numerous other methods.
Currently, more and more businesses recruit data scientists or business analysts specializing in predictive analytics since they can help executives respond quickly and effectively to changing business operations and market conditions.
Hence, mastering predictive analytics is necessary if you want to pursue a lucrative career in data science or business intelligence.
Although you can learn predictive analytics through online courses, most of those available are not worth taking. You will need to spend time finding the right course to take, which can be tedious and time-consuming.
I decided to do the heavy lifting for you. This article will only feature the best predictive analytics courses based on my observation. You can then select the one that suits your preferences and start learning right away.
Affiliate Disclosure: This article from Victory Tale contains affiliate links. We will receive a small commission from the course providers if you purchase the course through those links.
Nevertheless, we always value integrity and prioritize our audience’s interests. You can rest assured that we will present each course truthfully.
Things You Should Know
Each predictive analytics course in this article will have different prerequisites. I will specify them in detail in each course section below. However, all require you to have basic knowledge in the following:
- Statistics for Data Science (Mostly, you will need descriptive statistics and inferential statistics. If you have taken Statistics in college, that will also be sufficient.)
- Algebra (High-school algebra or above)
Prior knowledge of data analytics and predictive modeling is not required.
Below are the criteria for the best predictive analytics courses.
- Taught by credible instructors with many years of experience in data analysis or business analytics
- High-quality course materials
- Most of the course content must be up-to-date
- Provide excellent value for money
- Receive mostly positive reviews from real students
- My personal experience with the course, instructor, and learning platform (if any) must be positive.
1. Predictive Analytics for Business
This Nanodegree program from Udacity aims toward students who want to apply predictive analytics to solve real-world business problems without delving too deeply into the technicals.
Therefore, this program is the best choice for managers, executives, and others with no programming background.
Created in collaboration with Alteryx, an analytics automation company, This program consists of six minor courses as follows:
1. Problem Solving with Advanced Analytics – This course will introduce you to predictive analysis. You will learn about a structured problem-solving framework and an analytical methodology that you can use to address and solve a business problem.
The second part of the course will focus on linear regression. You will build and manage linear regression models to solve real-world problems.
2. Data Wrangling – The second course will equip all the skills to handle data of different types. You will perceive how to clean, reformat, and blend data from various data sources to make them ready for future analysis.
3. Classification Models – The third course will explain the concepts of classification modeling and how it differs from numeric modeling. Subsequently, you will build binary and non-binary classification models and evaluate the results.
4. A/B Testing – A/B Testing is a crucial tool for effective decision-making. First of all, you will learn about the fundamentals, such as selecting the target and managing control units and variables.
The second part of the course will delve into randomized and matched pair design tests. You will design the tests and analyze the results.
5. Time Series Forecasting – The fifth course will explain how data analysts handle data that have trend, seasonal and cyclical behavior. Later, you will learn about ETS and ARIMA models using the Alteryx platform and visualize the results.
6. Segmentation and Clustering – This final course will guide you through segmentation and clustering processes. All of which will help you spot patterns in your data. You will learn the tips and techniques to perform the tasks properly.
Finally, you will use Tableau to create compelling visualizations to display your results.
Each minor course will provide you with case examples, quizzes, and assignments that you can study further and practice. However, the best part of this program is the projects. You will work on one real-world project for every single course.
For instance, you will predict expected sales and profits from a catalog launch for a home goods manufacturer in the first course and build predictive models to provide strategic recommendations to a grocery store chain.
Completing these projects will earn you valuable hands-on experience. You will then be confident in using predictive analytics to solve business problems.
Regarding the pace, Udacity recommends spending ten hours per week on the course, and you will complete it in three months.
However, this program is entirely self-paced. You can select your own pace at will. Just keep in mind that the more time you enroll in the course, the more expensive the tuition fees (see below).
Unlike most online courses, Udacity offers excellent student support, including the following:
Technical Mentor Support – Udacity mentors are available 24/7 for you to ask technical questions. You can use the chat interface on Student Hub to contact them. Most students will receive a reply within an hour, which is extremely fast compared to other platforms that may take days or even forever.
Project Reviews – Since you will be working on as many as six real-world projects, you can request experts to review your work and provide feedback at any time you want.
Also, there is no limit to requests, and in most cases, you will receive feedback in only 1-3 hours. Thus, you can freely create a feedback loop and learn from your mistakes.
Career Services – This student support is excellent for new grads. Udacity team will review your resume, LinkedIn profile, and Github portfolio (if available) to ensure that they are at professional standards.
This will increase your chances of receiving interview invitations from top companies and firms.
This program costs $399 monthly. However, you can opt for a 3-month bundle and receive a 15% discount, lowering tuition fees to $339 per month.
Additionally, you can create an account with Udacity to access personalized discounts (like I did below).
These discounts can be as high as 75%. You can even use it to buy the bundle. Hence, it is possible to enroll in this top-notch program by paying less than $100 per month.
Pros & Cons
- Learn from the team of data analysts, business analysts, and solution engineers at Alteryx. Inc.
- Well-structured, comprehensive curriculum, covering all fundamental predictive analytics tools
- In-depth, informative lessons
- Offer as many as six real-world projects for students to complete to obtain hands-on experience
- Timely mentor support + Unlimited project requests
- Career services are a big plus for new grads.
- Udacity projects are known to be highly challenging, especially for absolute beginners.
- Not the best option if you have platform preferences (to be specific, if you don’t want to use Alteryx)
2. Predictive Analytics using Python
The second course will be far more technical than the first. This program by the University of Edinburgh will guide you through the entire process of building fully-functional predictive analytics models using Python.
Upon program completion, you will be confident in applying them to real-world scenarios like a data scientist.
There are five courses in this program as follows:
1. Introduction to Predictive Analytics using Python – You will learn the fundamentals of the predictive analytics process in this course. You will then gather data and clean them to ensure that they are ready for use. Later, you will build linear and logistic regression models based on that data.
2. Successfully Evaluating Predictive Modeling – The second course will discuss the measures and procedures you need to implement to evaluate your models. You will learn how to optimize your model for improved performance.
You will study the comprehensive case study in the second part of the course. You will build predictive models and benchmark them to predict customer churn trends for a telecommunications provider.
3. Statistical Predictive Modeling and Applications – The third course will drill deep into three predictive modeling techniques: Linear/Logistic regression and Naive Bayes classifiers. Apart from the theories, you will perceive how to apply them to predict outcomes for real-world business cases
4. Predictive Analytics using Machine Learning – The fourth course will explain how to adopt machine learning and deep learning for your predictive models. First, you will learn crucial concepts such as tree-based techniques, support vector machines (SVMs), and neural networks.
Subsequently, you will apply them to assigned real-world business scenarios. For example, you will build two models. The first one can forecast the consequences of the marketing campaign on customer behavior, while another will predict flight delays and cancelation trends for an airliner.
5. Final Project – The final project is where you will put the knowledge you have learned to the test. You will build predictive models to generate actionable insights and provide reliable solutions to multiple issues a virtual company faces.
All will be set in a real-world business setting. Hence, you will obtain significant practical experience upon completion.
Regarding the pace, the university recommends 8-10 hours per week for eight months. However, as an instructor-led program, you need to strictly follow the course schedule, which may not be optimal for some students.
You can audit all four courses for free, but you will need to pay $450 in tuition fees if you want to complete a final project.
To get the best learning experience, I suggest enrolling in the verified track, which costs $1350 one-time. This track will grant full access to all graded assignments, a final project, and a digital certificate upon program completion.
Note: As of January 2022, this program is not available. If you are interested, please visit the course page through the link below to double-check whether the university has announced new opening dates.
Pros & Cons
- Learn from a team of seven predictive analytics lecturers and professors at the University of Edinburgh
- Well-structured curriculum
- In-depth, informative lessons
- Four real-world projects to complete (one of them is large-scale)
- Free auditing (courses only)
- Costlier than other alternatives (including Udacity)
- Not always available. This program may be archived during some parts of the year.
- Instructor-Led: You will not be able to create your study schedule.
3. Datacamp’s Predictive Analytics Courses
Datacamp is a promising option to learn predictive analytics for beginners. The platform uses an interactive learning approach, making the lessons beginner-friendly and engaging. Therefore, if you are bored of traditional online courses, you may want to give Datacamp a try.
Datacamp offers more than 300 data science courses on its platform, including three on predictive analytics as follows:
- Introduction to Predictive Analytics in Python – The first course will explain the concepts of logistic regression models and how to use them to make predictions in a real business case.
- Intermediate Predictive Analytics in Python – The second course will discuss organizing and preparing data for predictive analytics.
- Predictive Analytics using Networked Data in R – You will use network learning and standard data mining techniques to predict the labels of nodes in networks.
Note: You will need to know Python and R before taking these courses. However, once you subscribe to Datacamp, you will gain access to all of its Python and R courses that you can take to fulfill the prerequisites.
Below is what you should expect from the Datacamp course. You will read the detailed instructions (on the left side) and complete the assignments by coding along directly on the web-based platform (on the right).
I find that this method is effective in keeping me concentrated. I am also less prone to being bored.
However, the drawback of Datacamp is that its courses only cover the fundamentals. They will not drill deep into advanced concepts or guide you to build real-world projects as other alternatives. Hence, you will need to buy another course elsewhere to learn more.
Datacamp offers two pricing plans as follows (billed annually):
- Standard – $12.42 per month
- Premium – $33.25 per month
The Standard plan would provide access to most of its 350+ courses on the platform, while the Premium plan would add 80 projects and additional courses on Tableau, Power BI, and Oracle SQL on top of it.
If you want to learn predictive analytics or business analysis in general, the Standard plan would be more than adequate.
However, Datacamp occasionally offers steep discounts (75% off) to the Premium plan (typically once every quarter), lowering subscription fees to $8 per month or even lower. I hence suggest subscribing to the Premium plan when they are on sale.
Pros & Cons
- Excellent platform that utilizes an interactive learning approach
- Easy-to-follow curriculum
- Beginner-friendly lessons
- Students will be able to start coding right away without installing the IDE.
- Learn predictive analytics anytime, anywhere through a well-built mobile app
- With a subscription, you can also take 300 other data science courses.
- The lessons are not in-depth. You will need another course to learn advanced tools and techniques.
4. Introduction to Predictive Modeling
This Coursera course from the University of Minnesota will lead you through concepts, processes, techniques, and real-world predictive modeling applications. If you are looking for a tutorial to test the waters, this course will serve that purpose.
Below is a summary of topics that you will learn from this course.
- Simple Linear Regression – Model structure, Ordinary Least Squares, and how to use Excel tools to create that model and make predictions
- Multiple Linear Regression – How to fit a multiple linear regression model using Excel tools, Overfitting/Underfitting problems, and best practices for selecting a suitable model
- Data Preparation – Steps to prepare a dataset for predictive modeling with Excel tools, Types of Variables, Data Cleaning, Multicollinearity, and many more
- Time Series Forecasting – Nature of Time-Series Data, Time Series Forecasting Techniques, and other techniques that can boost accuracy
In essence, this course focuses on building and evaluating predictive models using Excel. This will suit absolute beginners who want to grasp the concepts but are not tech-savvy enough to learn programming.
Most students complete this course in 12 hours. Regarding pricing, you can audit this course for free or subscribe to the entire specialization for $49 per month to get full access to all four courses on analytics for decision making.
Pros & Cons
- Well-structured curriculum with the logical teaching sequence
- Straightforward explanations with numerous examples
- Free auditing
- Some students reported a massive leap in assignment difficulty in Week 4.
- Difficult-to-read transcripts
5. Predictive Modeling and Machine Learning with MATLAB
Designed for MATLAB users, this course by MathWorks will guide students on how to use MATLAB to build, train, and optimize predictive models to analyze data.
Note: You should be familiar with MATLAB. Otherwise, you should take all prior courses in the specialization before taking this course.
Below is a summary of topics you will learn in this 22-hour course.
- Create and evaluate regression models with MATLAB
- Supervised Learning Workflow
- Create and assess classification models
- Ensemble Models
- Class Imbalance Handling
- and many more
Besides the concepts, you will learn through a practical study case. Hence, you will perceive how to use MATLAB tools to build a fully-functional predictive model in real life.
Auditing the course is free. However, I recommend subscribing to the entire specialization ($49 per month) to learn everything from the start, especially if you are not a proficient MATLAB user.
Pros & Cons
- Learn from a team of experts at MathWorks, a company that creates MATLAB
- Easy-to-follow curriculum
- High-quality video lessons with excellent explanations of concepts and case studies
- Compelling reading lists
- Free auditing
- The instructions are fast-paced, which may not be optimal for some students.
- The transcripts are not organized. Thus, they are tough to read.
Since I have recommended several Coursera courses in this article, you may be interested in more than one course. If that is the case, I recommend subscribing to Coursera Plus instead.
This is because by paying $399 per year ($33.25 per month on average), you can get full access (not just audit) to more than 3000 courses and specializations on the platform.
With this subscription, you do not need to pay fees for each specialization, saving more than hundreds or even thousands of dollars in a process.
Below are other predictive analytics courses that you may want to consider. However, I did not include them in the list for specific reasons explained below.
Python Data Products for Predictive Analytics Specialization – Designed for students with Python experience, this Coursera specialization from UC San Diego explains all the steps to build accurate predictive models with Python and machine learning.
However, students reported that some lessons in the last course were not well-taught, while many had issues with the peer review system.
The following is a list of articles that will be helpful if you want to take other courses to strengthen your skills once you master predictive analytics.