Predictive Analytics Certification

Top 3 Predictive Analytics Certification Programs

With the sharp increase in the number of businesses that are looking for ways to predict and analyze their data, demand for predictive analytics professionals has also increased rapidly. This guide lists some of the popular predictive analytics certification programs available for people who want to work with data in an analytical capacity.

Our Top 3 Picks

Udacity Logo
Predictive Analytics for Business Nanodegree
Best project-based courses
Introduction to Predictive Analytics
Best entry-level predictive analytics certification
Linear Regression Modeling in R
Best R and alternative program

1. Predictive Analytics for Business Nanodegree (Udacity)

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Take on the world with your analytical skills. In the 3-month online Predictive Analytics for Business Nanodegree program, you will gain the skills you need to get ahead in your business career. Learn how to use predictive analytics and business intelligence to solve real-world business problems.

Throughout the online program, you will learn how to use advanced analytics to predict sales, manage business risk, and make better decisions.  This program will also teach you how to wrangle your data for analysis, perform A/B testing, execute time series forecasting, and cluster data for more effective analysis.


  1. Problem-Solving with Advanced Analytics – In this course, you will learn how to solve complex analytical problems. It introduces you to both Simple Linear Regression and Multiple Linear Regression so you can use these methods to predict sales and provide suitable recommendations.
  2. Data Wrangling – Get your data workflows in order so you can make better decisions. Learn how to clean, reformat data, and identify outliers so it’s ready for analysis. The project of this course involves creating an analytical dataset and building a predictive model for site selection.
  3. Classification Models – In this course, you learn more about binary and non-binary classification models for business analysis. In the project, you will learn how to build a classification model and provide recommendations to the bank for loan default risk.
  4. A/B Testing – Get the information you need to make informed business decisions with this course. Create, execute, and analyze an A/B test (split test) so that you can find the best performance of various designs.
  5. Time Series Forecasting – You will learn the basics of time series forecasting, including how to create time series data as well as forecasting for trends and seasonality.
  6. Segmentation and Clustering – Learn how to make sense of your data with segmentation so you can find patterns and insights. We’ll explore how to group data for analysis and identify the most effective way to cluster data.

Skills Acquired

  • Linear Regression
  • A/B Testing
  • Data Wrangling
  • Time Series Forecasts
  • Segmentation
  • Clustering
  • ARIMA Models
  • ETS Models
  • Binary Classification
  • Prediction
  • Non-Binary Classification
  • PREREQUISITES: The prerequisites for the Predictive Analytics for Business Nanodgree from Udacity are knowledge of algebra, descriptive statistics, and Excel

2. Introduction to Predictive Analytics (DataCamp)

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Get started with predictive analytics today, in this Introduction to Predictive Analytics course. You will learn to use a variety of models and technologies to make predictions, and present them in a clear, concise way.

First, you will learn to use and present logistic regression models for making predictions. Next, you will look into model performance to business including cumulative gains curve and lift graph. Finally, you’ll take a more in-depth view of model interpretation.


  1. Building Logistic Regression Models – In this chapter, you will learn how to build a logistic regression model and how to use it to predict a binary target.
  2. Forward Stepwise Variable Selection for Logistic Regression – When you are trying to build a model, it is important to choose variables that will have a positive impact on the outcome. In this course, you will improve your model predictions by using forward stepwise variable selection. This method helps to identify and omit variables that are not associated with the desired outcome.
  3. Explaining Model Performance to Business – Get your stakeholders on board with the use of cumulative gains curves and lift graphs. In this course, you will develop graphs to communicate your findings to your stakeholders for model performance.
  4. Interpreting and Explaining Models – Learn how to interpret models. By understanding the relationships between the predictor variables and the target in a model, you will be able to better understand the intuition behind the model and make more informed decisions.

Skills Acquired

  • Logistic Regression
  • Coefficient Interpretation
  • Stepwise Variable Selection
  • Area Under the Curve (AUC)
  • Model Evaluation
  • Cumulative Gains Curve
  • Life Curve
  • Predictor Insight Graph
  • Model Interpretation
  • Prediction
  • INFORMATION: This course contains 50+ exercises with over 4 hours of material.

3. Linear Regression Modeling in R (Dataquest)


The main goal of the Linear Regression Modeling in R course from Dataquest is to teach you how to use linear regression in order to make predictions and inferences about data. In this course, you will learn how to use linear regression models to predict house prices.

Get the training you need to interpret linear regression models in R and so much more. This course will also teach you how to use linear regression models to analyze data, choose the right model, and make predictions.


  1. Introduction to Modeling – This course will introduce you to the basics of modeling and how to create models. You will learn how to create models from data, how to identify errors in your models, and how to fix them.
  2. Bivariate Relationships with Correlation and Scatterplots – In this lesson, you will explore how to use scatterplots to understand the relationship between two variables. You will also learn how to identify outliers and how to use correlation to improve your understanding of the data.
  3. Estimating the Coefficients and Fitting Linear Models – Get an overview of how to fit a bivariate linear regression model and how to visualize the fit. This course will help you estimate the coefficients, intercepts, and slopes of the models.
  4. Assessing the Accuracy of the Model – This course uses a variety of methods to assess the accuracy of the model course, including the t-statistic, p-value, and confidence intervals.
  5. Fitting Many Linear Models – Get a clear understanding of how to fit multiple linear models in order to understand the data better.
  6. Guided Project: Predicting Condominium Sale Prices – Use this guided project to understand how well the size of a condominium in New York City predicts the sale price. Use scatterplots and various other techniques to see how different variables affect the sale price of a condominium.

Skills Acquired

  • Linear Regression
  • R
  • Ordinary Least Squares (OLS)
  • Linear Algebra
  • Model Fit + Accuracy
  • Bivariate Relationships
  • Correlation
  • Scatterplots
  • Prediction
  • INFORMATION: This course contains 6 lessons with 6+ hours of material.

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