Predictive Analytics: Basic Modeling Techniques from edX

What is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs.

These skills also go under the names "machine learning" and "data science," the latter being a broader term than machine learning or predictive analytics but narrower than AI. This course is part of the Machine Learning Operations (MLOps) Program. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions. As you get further into the program, you will learn how to fit that model into a machine learning pipeline.

You will get hands-on experience with the top techniques in supervised learning: linear and logistic regression modeling, decision trees, neural networks, ensembles, and much more.

But most importantly, by the end of this course, you will know

What a predictive model can (and cannot) do, and how its data is structured
How to predict a numerical output, or a class (category)
How to measure the out-of-sample (future)performance of a model

What you'll learn

After completing this course, you will be able to:

Develop a variety of machine learning algorithms for both classification and regression, including linear and logistic regression, decisions trees and neural networks
Evaluate machine learning model performance with appropriate metrics
Combine multiple models into ensembles to improve performance
Explain the special contribution that deep learning has made to machine learning task

What's inside

Syllabus

Week 1 – Data Structures; Linear and Logistic Regression

Classification and Regression

Rectangular Data

Regression

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores foundational data science, machine learning, and artificial intelligence concepts

Taught by instructors who are recognized leaders in the tech industry

Provides hands-on experience with popular supervised learning techniques

Suited for individuals interested in developing skills in data science and machine learning

Requries a strong foundation in mathematics and statistics

Reviews summary

Foundational predictive analytics for professionals

According to students, this course offers a solid foundational understanding of predictive analytics and machine learning techniques, making complex topics like neural networks accessible. It is particularly well-suited for those seeking a high-level overview and conceptual clarity in the field. Learners appreciate the clear explanations and logical progression of topics, which help build confidence in evaluating model performance. While generally well-received, some express a desire for more in-depth practical application and coding guidance, noting that the hands-on activities might be more illustrative than intensely coding-focused. There are also mentions of the pace being fast in later modules and some content potentially being less modern, though more recent reviews tend to be highly positive, suggesting continuous improvement or varied learner expectations.

Generally relevant, but some found content less modern than expected.

"The material is very relevant for anyone looking to get into data science."

"Outdated content in some sections, especially regarding deep learning. The course felt more theoretical than practical."

"The content on neural networks was a good summary, even if some parts felt a bit academic."

Ideal for those new to machine learning and data science.

"Would recommend for beginners to ML. Overall, a solid foundation."

"This course laid a solid groundwork for me, especially as part of the MLOps program."

"I'm now much more confident in evaluating model performance. Highly recommend for aspiring data analysts."

Provides excellent clarity on core predictive analytics concepts.

"This course was a fantastic introduction to predictive analytics. The instructor made complex topics like neural networks accessible."

"I found the modules on linear and logistic regression very clear and well-structured. The quizzes helped reinforce learning."

"Excellent course for understanding the core concepts of predictive modeling. The explanation of ROC curves and ensemble methods was particularly clear."

Some found the pace fast; others desired more in-depth content.

"Sometimes the pace felt a bit fast, especially in the later weeks, but overall, a solid foundation."

"Good for a high-level overview, but not for in-depth skill building."

"The exercises were basic and didn't challenge me much; I expected more advanced practical applications."

Course leans more towards theoretical understanding than practical coding.

"I was disappointed with the lack of coding guidance. While it mentions 'hands-on experience,' it felt more like conceptual understanding than practical application."

"The theoretical explanations are good, but I wished there were more practical, real-world case studies beyond just illustrations."

"The 'how' part, specifically coding, is left mostly to the learner, which could be a challenge for some."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Predictive Analytics: Basic Modeling Techniques with these activities:

Find a mentor who can provide guidance on machine learning

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Finding a mentor can provide you with valuable guidance and support as you learn about machine learning.

Browse courses on Machine Learning

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Reach out to friends, family, or colleagues to see if they know any machine learning experts.
Attend machine learning meetups and conferences.
Contact professors or researchers at universities.

Review the basics of artificial intelligence

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Have at least some exposure to the key concepts of artificial intelligence so that you can better understand the material presented in this course.

Browse courses on Artificial Intelligence

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Read a beginner-friendly introduction to AI
Watch a few videos on YouTube or Coursera about the basics of ML
Complete a few coding exercises on a platform like Hackerrank

Review key concepts in statistics

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Review the basics of statistics will help you better understand and apply the techniques you will learn in this course.

Browse courses on Probability and Statistics

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Read through your old statistics notes or textbook.
Take a practice quiz or test to assess your understanding.
Watch online videos or tutorials on key statistical concepts.

Eight other activities

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Read 'An Introduction to Statistical Learning'

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Reading 'An Introduction to Statistical Learning' will provide you with a comprehensive overview of statistical learning techniques and their applications.

View An Introduction to Statistical Learning: with... on Amazon

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Read through the book and take notes.
Work through the exercises at the end of each chapter.
Apply the techniques you learn to real-world problems.

Follow a tutorial on decision trees

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Understand the concepts, algorithms, and applications of decision trees.

Browse courses on Decision Trees

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Find a tutorial on decision trees for beginners
Go through the tutorial step-by-step
Implement a decision tree algorithm in a programming language

Practice linear regression

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Gain a deeper understanding of linear regression and become familiar with its implementation.

Browse courses on Linear Regression

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Go through the course material on linear regression
Complete the practice exercises at the end of each section
Work on a small project involving linear regression

Solve practice problems

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Solving practice problems will help you apply the techniques you learn in this course and improve your problem-solving skills.

Show steps

Find practice problems online or in textbooks.
Solve the problems on your own.
Check your answers and identify areas where you need more practice.

Contribute to an open-source machine learning project

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Contributing to an open-source machine learning project can help you learn about the latest techniques and best practices.

Browse courses on Machine Learning

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Find an open-source machine learning project that you are interested in.
Identify an area where you can contribute.
Make a contribution to the project.
Get feedback from the project maintainers.

Create a visual representation of a statistical concept

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Creating a visual representation of a statistical concept will help you understand the concept more deeply and communicate it more effectively to others.

Browse courses on Data Visualization

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Choose a statistical concept to visualize.
Select a visual format (e.g., graph, chart, diagram).
Create the visual representation using a tool or software.
Write a brief explanation of the visual representation.

Develop a machine learning model to predict a real-world outcome

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Developing a machine learning model to predict a real-world outcome will give you hands-on experience with the techniques you learn in this course and help you apply them to a real-world problem.

Browse courses on Machine Learning

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Identify a real-world problem that you want to solve.
Gather data that is relevant to the problem.
Clean and prepare the data for modeling.
Develop and train a machine learning model.
Evaluate the performance of the model.

Participate in a machine learning competition

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Participating in a machine learning competition can help you test your skills and learn from others.

Browse courses on Machine Learning

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Find a machine learning competition that you are interested in.
Join a team or work on your own.
Develop a machine learning model and submit it to the competition.
Analyze the results of the competition and identify areas for improvement.

Career center

Learners who complete Predictive Analytics: Basic Modeling Techniques will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use data to solve business problems. They develop and implement predictive models to help businesses make better decisions. This course will help you build the skills you need to become a Data Scientist. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. You will also learn how to evaluate the performance of your models and communicate your findings to stakeholders.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for developing and deploying machine learning models. They work with Data Scientists to identify the right problems to solve and then develop and implement the models that will solve those problems. This course will help you build the skills you need to become a Machine Learning Engineer. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. You will also learn how to evaluate the performance of your models and deploy them to production.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians use data to solve problems. They work in a variety of industries, including healthcare, finance, and education. This course will help you build a solid foundation in predictive analytics, which is a key skill for Statisticians. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They develop and implement trading strategies and make investment decisions. This course will help you build a solid foundation in predictive analytics, which is a key skill for Quantitative Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Quantitative Analyst

Financial Analyst

Financial Analysts use data to make investment decisions. They work with businesses and investors to identify and evaluate investment opportunities. This course will help you build a solid foundation in predictive analytics, which is a key skill for Financial Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Financial Analyst

Insurance Analyst

Insurance Analysts use data to assess risk and make insurance decisions. They work with insurance companies to develop and price insurance policies. This course will help you build a solid foundation in predictive analytics, which is a key skill for Insurance Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Insurance Analyst

Healthcare Analyst

Healthcare Analysts use data to improve the quality and efficiency of healthcare delivery. They work with hospitals and other healthcare providers to identify and solve problems in areas such as patient care, cost reduction, and process improvement. This course will help you build a solid foundation in predictive analytics, which is a key skill for Healthcare Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Healthcare Analyst

Risk Analyst

Risk Analysts use data to identify and assess risks. They work with businesses and governments to develop and implement risk management strategies. This course will help you build a solid foundation in predictive analytics, which is a key skill for Risk Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Risk Analyst

Fraud Analyst

Fraud Analysts use data to identify and investigate fraudulent activity. They work with businesses and governments to develop and implement fraud prevention strategies. This course will help you build a solid foundation in predictive analytics, which is a key skill for Fraud Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Fraud Analyst

Data Engineer

Data Engineers are responsible for building and maintaining the data infrastructure that supports data analysis. They work with Data Scientists and other data professionals to ensure that data is available, reliable, and secure. This course will help you build a solid foundation in predictive analytics, which is a key skill for Data Engineers. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Data Engineer

Data Analyst

A Data Analyst is responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course will help you build a solid foundation in predictive analytics, which is a key skill for Data Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Data Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. They work with businesses to identify and solve problems in areas such as supply chain management, logistics, and scheduling. This course will help you build a solid foundation in predictive analytics, which is a key skill for Operations Research Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Operations Research Analyst

Market Research Analyst

Market Research Analysts collect and analyze data to help businesses understand their customers and make better decisions. This course will help you build a solid foundation in predictive analytics, which is a key skill for Market Research Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Market Research Analyst

Business Analyst

Business Analysts use data to help businesses make better decisions. They work with businesses to identify and solve problems in areas such as cost reduction, process improvement, and customer satisfaction. This course will help you build a solid foundation in predictive analytics, which is a key skill for Business Analysts. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. They work with a variety of technologies, including data analytics tools. This course will help you build a solid foundation in predictive analytics, which is a key skill for Software Engineers who work with data. You will learn how to use different techniques to predict future outcomes, such as linear regression, logistic regression, and decision trees. This course will also help you develop the skills you need to communicate your findings to stakeholders.

See salaries and explore the career path for Software Engineer