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

Partitioning and Overfitting

Illustration - Linear Regression (for verified users)

Knowledge Check 1.1

Logistic Regression

Illustration - Logistic Regression (for verified users)

Understand and Prepare Data

Visualization

CRISP-DM framework

P-Values

Knowledge Check 1.2

Discussion Prompt #1 (for verified students, graded)

Quiz #1 (for verified students, graded)

Exercise #1 - Linear Regression (for verified students, graded)

Exercise #2 - Logistic Regression (for verified students, graded)

Summary

Week 2 - Assessing Models; Decision Trees

Assessing Model Performance: Metrics

ROC Curve and Gains Chart

Decision Trees

Illustration - Classification Tree (for verified users)

Knowledge Check 2

Quiz #2 (for verified students, graded)

Exercise #3 - Regression Tree (for verified students, graded)

Exercise #4 - Classification Tree (for verified students, graded)

Week 3 – Ensembles

Cross validation

Module 3 Reading

Ensembles

Illustration - Ensemble Methods (for verified users)

Knowledge Check 3

Discussion Prompt #2 (for verified students, graded)

Quiz #3 (for verified students, graded)

Exercise #5 - Ensemble Methods (for verified students, graded)

Week 4 - Neural Networks

Neural Nets

Illustration - Neural Nets (for verified users)

Deep Learning

Reading

Knowledge Check 4

Quiz #4 (for verified students, graded)

Exercise #6 - Neural Nets (for verified students, graded)

Good to know

Know what's good

, what to watch for

, 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

Confusing quiz structure

According to students, basic course content is hampered by a confusing quiz structure and a relatively high passing grade. Students do not feel that the course is worth the money and recommend taking the course without paying.

The quiz structure is confusing.

"confusing quiz structure"

The passing grade is relatively high.

"relatively high passing grade required"

The course is not worth the money.

"not worth the money to be honest"

"should just take the course without paying"

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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