The editors at Solutions Review have compiled this list of the best predictive analytics courses and online training to consider for 2020.
The core focus of predictive modeling is to use explanatory variables from past occurrences and exploit them to predict the previously unknown future. The accuracy and usability of predictive analytics is wholly dependent on how granular the analysis has been run and the type of assumptions that are being made. Forward-thinking organizations will utilize predictive models for a variety of business functions. Some of the most common ways this type of analysis is being used is to detect fraud, optimize marketing campaigns, improve operations, and to manage risk.
With this in mind, we’ve compiled this list of the best predictive analytics courses and online training to consider if you’re looking to grow your data analytics skills for work or play. This is not an exhaustive list, but one that features the best predictive analytics courses and training from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well. Click Go to training to learn more and register.
Description: This course provides you with the skills to build a predictive model from the ground up, using Python. You will learn the full lifecycle of building the model. First, you’ll understand the data discovery process and discover how to make connections between the predicting and predicted variables. You will also learn about key data transformation and preparation issues, which form the backdrop to an introduction in Python for data analytics.
Related paths/tracks: Predictive Analytics using Machine Learning
Description: Learn to apply predictive analytics and business intelligence to solve real-world business problems. Students who enroll should be familiar with algebra and descriptive statistics and have experience working with data in Excel. Working knowledge of SQL and Tableau is a plus, but not required.
Description: In this course, you will learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions and how to present it and its performance to business stakeholders. The course is instructed by Nele Verbiest, a senior data scientist at Python Predictions. At Python Predictions, she developed several predictive models and recommendation systems in the fields of banking, retail and utilities.
Platform: LinkedIn Learning
Description: This course provides that perspective through the lens of a veteran practitioner who has completed dozens of real-world projects. Keith McCormick is an independent data miner and author who specializes in predictive models and segmentation analysis, including classification trees, cluster analysis, and association rules.
Related paths/tracks: Predictive Analytics Essential Training for Executives, Python: Working with Predictive Analytics, Business Analytics Foundations: Predictive, Prescriptive, and Experimental Analytics
Description: This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business.
Description: In this course, you will learn foundational knowledge of solving real-world data science problems. First, you will explore the basics of implementing supervised learning problems including linear regression and neural networks. Next, you will discover how recommendation systems can be implemented using TensorFlow. Finally, you will learn how to understand and implement reinforcement learning systems.
Description: You will learn about different ways in how you can handle date and time data in R. Things like time zones, leap years or different formats make calculations with dates and time especially tricky for the programmer. You will learn about POSIXt classes in R Base, the chron package, and especially the lubridate package. You will learn how to visualize, clean, and prepare your data. After that, you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity, and unit root tests.
Related paths/tracks: Logistic Regression (Predictive Modeling) workshop using R, Understanding Regression Techniques, Logistic Regression using SAS – Indepth Predictive Modeling, R Programming: Advanced Analytics In R For Data Science
Description: This course will address this issue and will help you understand what exactly machine learning and predictive analytics are, what are its limits and its potential risks, and why it may benefit your organization. Using real-world case studies and many other examples of current and potential future industry usage, this course will help you better understand why many corporations are adopting or should be adopting machine learning to better enable their future.
Related paths/tracks: Scaling Advanced Analytics
Description: This advanced programming course will teach you how to analyze Python data with NumPy and pandas. As machine learning becomes more prevalent, Python has emerged as a scientific language. Within Python, NumPy and pandas are essential for any scientific computation. Understanding how these elements work together is critical for the aspiring data scientist.
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