Big Data and Predictive Analytics Short Course

Course Structure

Module 1: Introduction to Big Data and Predictive Analytics

In this module, you’ll be introduced to the foundational concepts of Big Data and Predictive Analytics. Big Data refers to massive datasets that are too complex or large to be handled by traditional data storage methods or processed with conventional data analytics tools. These datasets may include raw data from various sources, such as social media, sensors, and transactional systems. Predictive Analytics, on the other hand, involves using advanced statistical models, machine learning techniques, and data mining to analyze historical data and predict future outcomes.

You’ll learn about the difference between Big Data and Predictive Analytics and understand how Big Data analytics involves managing vast amounts of data storage and processing. At the same time, Predictive Analytics uses that data to forecast future events or trends. Predictive Analytics aims to identify patterns and relationships within historical data through data mining techniques to generate predictive models. This process is vital in various industries, where predicting future outcomes, such as customer behavior, market trends, or supply chain demands, can provide a competitive edge.

By the end of this module, you’ll clearly understand how Big Data and Predictive Analytics work together to drive data-driven decision-making.

Module 2: Predictive Modeling Techniques

In this module, we’ll explore critical predictive modeling techniques that help forecast future events based on historical data. Predictive analytics models use statistical models and machine learning algorithms to analyze data and predict future outcomes.

You’ll gain insight into how data analytics and statistical analysis are essential for building predictive models that can be applied across various industries. One of the core techniques covered will be classification trees, which are used for predicting categorical outputs (e.g., whether a customer will buy a product or not).

We will also discuss regression models, including logistic regression for binary outputs (e.g., yes/no) and traditional regression for continuous outcomes (e.g., predicting revenue or sales growth). These models help estimate relationships between variables and predict future events based on historical data.

By the end of this module, you’ll have a solid understanding of the different predictive modeling techniques and how to apply them in analyzing large datasets to forecast future trends and behaviors.

Module 3: Classification Trees for Categorical Outputs

Classification trees are a powerful predictive analytics model for categorical outcomes, such as “yes/no” decisions or product classifications. In this module, we’ll explore how classification trees work, focusing on their ability to analyze raw data and divide it into distinct categories based on specific decision rules.

You’ll learn how classification trees differ from other models in terms of structure and how they handle categorical outputs. While different models like linear regression might predict continuous outcomes, classification trees work by splitting the data into subsets based on the values of input features. This makes them highly effective in applications that classify data into specific categories.

Throughout this module, we’ll also discuss how classification trees are used in Big Data analytics, where large amounts of raw data must be processed to make informed predictions. You’ll practice building classification trees and applying them to real-world datasets.

Module 4: Regression Models for Continuous and Binary Outputs

In this module, you’ll explore regression models and their applications in Predictive Analytics. Regression models are fundamental tools for predicting outcomes based on one or more predictor variables.

You’ll begin with traditional regression models, typically used to predict continuous outputs, such as sales figures, customer lifetime value, or revenue growth. We’ll cover the statistical analysis behind these models, including assumptions and diagnostics.

Next, we’ll discuss logistic regression, which is used for binary outputs—those that can take only two possible outcomes, like “yes” or “no,” “true” or “false.” Logistic regression plays a crucial role in scenarios where the goal is to classify events, such as whether a customer will purchase a product or whether a patient will develop a disease.

The module will also cover the differences in diagnostic methods between logistic and traditional regression, explaining how to assess the quality of predictive models and their ability to forecast future events.

Module 5: Regression Analysis Diagnostics

In this module, you will learn the vital skills of regression analysis diagnostics. Regression models are valuable but must be thoroughly assessed to ensure their predictive accuracy.

You’ll explore the diagnostic tests used to evaluate regression models, including residual analysis, multicollinearity checks, and hypothesis testing. For traditional regression, diagnostics include examining the distribution of residuals and testing for homoscedasticity and normality.

The diagnostic process for logistic regression differs, with an emphasis on measures like the odds ratio and confusion matrices. You’ll also learn about model performance metrics like accuracy, precision, and recall, which are essential for understanding how well the model predicts binary outcomes.

This module will help you interpret regression outputs and make necessary adjustments to improve your predictive models, ensuring they can accurately forecast future outcomes.

Module 6: Graphical Techniques for Enhancing Predictive Analytics

Graphical techniques are powerful tools for enhancing predictive analytics. Visualizing data and model results can help you better understand the relationships between variables and refine your predictive models.

In this module, we’ll cover various graphical techniques, such as scatter plots, residual plots, and ROC curves, that allow you to assess the performance of your predictive models visually. These tools provide insights into how well the model fits the data and help identify areas where the model can be improved.

Using data visualization methods, you can detect patterns, outliers, and trends within the data that might not be obvious through statistical analysis alone. Graphical techniques are precious when working with Big Data, enabling you to interpret and communicate the insights drawn from complex datasets.

Module 7: Case Studies and Real-World Applications

In this module, we’ll look at how predictive analytics has been applied in real-world situations, particularly in finance, healthcare, and retail industries. You’ll explore case studies demonstrating how companies have used predictive modeling to make informed decisions, reduce costs, and increase revenue.

We’ll examine how data mining techniques and big data analytics are used to analyze massive datasets, identify patterns, and predict future trends. Predictive analytics is transforming industries, whether it’s predicting customer churn in telecoms, forecasting demand in supply chain management, or identifying potential health risks in medical research.

By the end of this module, you’ll have a deeper understanding of how predictive analytics solutions are applied to Big Data to forecast future outcomes and make data-driven decisions that improve business performance.

Module 8: Hands-On Practice with Big Data

In this final module, you’ll apply the techniques you’ve learned throughout the course to real-world Big Data sets. You’ll work with raw and historical data to build predictive models using classification trees and regression models. Through hands-on exercises, you’ll practice predictive analysis and forecast future outcomes based on actual datasets.

This module will give you practical experience working with big data analytics tools and methodologies. You’ll analyze complex datasets, perform data mining, and apply predictive modeling techniques to solve business problems and predict future events.

By the end of this module, you’ll have developed the skills to handle Big Data and apply predictive analytics techniques to forecast future events in various business scenarios.