Top 5 Online Courses for Predictive Modeling

The first course on our list comes from the University of Washington, where the instructor, Bill Howe, is the director of research.

Practical Predictive Analytics: Models and Methods is available on Coursera, and the class focuses on designing statistical experiments with an end goal of learning to analyze the results of these experiments by using modern methods.

Students will discover common downfalls of interpreting mathematical arguments associated with large datasets.

At the end of the four-week study, with six to eight hours of study per week, the student will be able to make watertight statistical arguments using resampling methods, explain learning concepts and methods, and have a deep understanding of structural query, traversals, recursive queries, PageRank and community detection.

He or she can then apply practical machine learning methods and concepts to real-world problems.

The students will start with Practical Statistical Inference in Week 1, learning the basics of statistical inference and making simple programs to make statistical arguments. Week 1 also covers publication bias as well as reproducibility.

Week 2 is titled Supervised Learning, in which the student explores important methods, techniques, and algorithms involved in machine learning. As students build upon these predictive modeling methods, the student will form algorithms to perform a variety of tasks. This week’s lesson also includes learning pitfalls to avoid while structuring these algorithms.

Week 3 deals with optimization, and here, the video count decreases drastically from the first two weeks.

The student should have a certain level of comfort with the ideas and methods learned in the previous weeks and will learn to optimize a cost function using gradient descent, including improving performance with randomization and parallelization. The student will practice popular methods and will begin realizing fundamental similarities between them.

The theme of the last week is Unsupervised Learning, which guides students through unsupervised learning methods and provides students with opportunities to practice with more real-world problems.

Once again, the video count for this week drops significantly from the 20+ videos in the first two weeks down to four. The student will be using the knowledge gained in the first three weeks to complete the final week of the course.

Paying in full for the course, however, will allow you access to the entire course content, as well as an electronic certificate upon completion of the course, which can even be added directly to your LinkedIn page.

In Microsoft’s own course, Graeme Malcolm, Senior Content Developer for Microsoft Learning Experiences, will guide you through implementing predictive analytics for big data by using Apache Spark.

This course will allow you to work in Scala or Python to build models for machine learning with Spark ML, the machine learning library in Spark. Also, it is worth noting that you will need a subscription to Azure, Microsoft’s cloud computing service, in order to get the most out of the course.

The course itself is free. However, if you would like the verified certificate upon completion, there will be a $99 charge. Additionally, this certificate can be added to a CV, resume or directly to your LinkedIn Profile.

Moreover, the course lasts for six weeks with a workload of three to four hours per week. This is about the same amount of material in the University of Washington’s course but taken at a slightly slower pace. During the course, the student will learn to explore data and ready themselves for predictive modeling using Spark. They will also evaluate and optimize models, as well as build both supervised and unsupervised machine learning models.

Furthermore, the course syllabus begins with an introduction to data science and Spark. Students will use Spark to run Python or Scala code to work with data.

Finally, before signing up for this course, the student should have familiarity with Azure HDInsight, databases and SQL. Also needed are a little programming experience, and a willingness to take a self-paced approach to learn the material.

Our next course comes from Statistics.com, taught by Anthony Babinec and Dr. Galit Shmueli.

The purpose of this course is to introduce basic concepts of predictive modeling.

The course encompasses classification, two models that are vital in most business applications. The student will learn by visualizing historical data and examining relationships between different variables.

During the course, the student will be introduced to four different techniques that are used for machine learning, classification and regression trees, k-nearest neighbors, and Bayesian classifiers. After you are comfortable with these models, you will then learn to combine different models. This is in order to achieve more accurate results than those any individual model could produce on its own.

Additionally, Machine Learning Tools also covers dividing data into training data, validation data and test data with the use of partitioning.

Furthermore, the course is offered for three different software programs. They are: XLMiner, a data-mining add-in for Excel, R, and Python. These options give you an opportunity for quicker learning if you are already familiar with the language or software.

At the end of this course, the student will have the ability to visualize data in order to obtain a clearer understanding of the relationships within different data-sets. Furthermore, they will be able to provide an assessment basis for predictive modeling, implement models with four different algorithms, and understand how models can improve predictions.

Finally, the program is four weeks long. They also offer individual and certificate programs as well as a free one-week preview before you dive in.

Predictive Analytics is a seven-week course from the Indian Institute of Management, Bangalore. Accordingly, it has a course load of four to five hours of work per week. This course comes from Dinesh Kumar, Professor in Decision Sciences & Information Systems at IIMB. 

Moreover, it presents predictive modeling as a business strategy used by high-performing companies.

Additionally, students will work with models such as linear regression, logistic regression, ARIMA and decision trees in order to solve predictive analytics problems. The course is ideal for students who want to build upon their foundational knowledge of predictive analytics and will prepare you for a data analytics career.

Before enrolling in the course, you should have a firm grasp of advanced statistical concepts such as descriptive statistics, probability distribution, hypothesis testing and ANOVA. Familiarity with the software SPSS, SAS, or STATA is recommended before enrolling.

By the end of the course, you will understand how you can use predictive analytics tools to analyze actual problems that modern businesses have, use predictive analytics techniques to explore model outputs, and learn regression, logistic regression, and forecasting through MS Excel, SPSS, and SAS.

Equally important is that this advanced-level predictive modeling course is free. However, you will be required to pay $50 for a verified certificate for your LinkedIn profile. Additionally, the course is available in English as well as Hindi.

Lastly, we have a course from the California Institute of Technology.

This massive ten-week introductory course more than doubles the content of all of the other courses that made our list. Correspondingly, it expects 10-20 hours of work per week from the student.

The class' instructor is Yaser S. Abu-Mostafa, a professor of Electrical Engineering and Computer Science at Caltech. Learning from Data (Introductory Machine Learning) emphasizes beginner computer science and covers basic theory, applications, and algorithms. The demand for careers in the field of machine learning is growing exponentially, and Caltech can help you prepare for your first role as a data scientist or quantitative analyst.

The course consists of equal parts theory and hands-on practice. Accordingly, it aims to leave the student with a story that begins with the introduction of the concept of learning and ends with concepts that enable to student to fully understand and explain machine learning.

At the end of this course, the student will be able to identify basic principles of theory, algorithms, and applications of machine learning, analyze relationships between theory and practice in machine learning, and master the heuristic and mathematical aspects of machine learning with the ability to apply their applications to situations in the real world.

In a similar fashion to the other courses we have outlined, Learning from Data (Introductory Machine Learning) is available for free. You also have the option to obtain a verified certificate of completion for $49 once you have completed the material.