Predictive Analytics from edX

Welcome to Thayer School of Engineering at Dartmouth’s Predictive Analytics for Digital Transformation. This course equips you with the tools and knowledge to turn raw data into actionable insights, helping you lead data-driven innovation in your field. Whether you aim to enhance organizational efficiency, improve customer experiences, or drive transformative solutions, this course provides a solid foundation in predictive analytics techniques.

You’ll start with essential linear and logistic regression methods and progress to advanced modeling techniques that solve real-world business challenges. Using Python and cloud-based tools, you’ll gain hands-on experience building, training, and evaluating predictive models. The curriculum covers diagnosing common issues such as overfitting and underfitting, selecting meaningful features, working with skewed datasets, and employing cross-validation methods to ensure robust and generalizable models.

This course blends theoretical concepts with practical applications. You’ll explore predictive analytics' role in digital transformation initiatives through case-based projects, reflection exercises, and guided activities. You’ll also develop critical skills in identifying opportunities to integrate analytics into decision-making processes, ensuring your insights drive measurable outcomes.

This course, led by Professors Vikrant Vaze and Reed Harder, provides a supportive yet challenging environment for learners at all levels. Whether a seasoned professional or new, you’ll learn to think critically, code effectively, and apply your skills to meaningful, data-centric problems. By the end of the course, you’ll have the expertise to lead predictive analytics projects and contribute to digital transformation efforts in any industry.

What's inside

Learning objectives

● build predictive models using python : gain hands-on experience with scikit-learn to develop and refine regression and classification models, applying them to real-world scenarios.
● diagnose and improve model performance : identify issues like overfitting and underfitting, apply cross-validation, and select optimal features to ensure robust, generalizable results.

● leverage advanced techniques : explore neural networks, regularization, and cloud-based tools to scale and optimize predictive analytics for complex business challenges.
● integrate analytics into decision-making : translate data-driven insights into actionable strategies to drive innovation and efficiency in digital transformation initiatives.

● build predictive models using python : gain hands-on experience with scikit-learn to develop and refine regression and classification models, applying them to real-world scenarios.
● diagnose and improve model performance : identify issues like overfitting and underfitting, apply cross-validation, and select optimal features to ensure robust, generalizable results.
● leverage advanced techniques : explore neural networks, regularization, and cloud-based tools to scale and optimize predictive analytics for complex business challenges.
● integrate analytics into decision-making : translate data-driven insights into actionable strategies to drive innovation and efficiency in digital transformation initiatives.

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Uses Python and cloud-based tools, which are standard in the field of data science and predictive modeling

Develops skills in regression, classification, and neural networks, which are core techniques in predictive analytics

Taught by Professors Vaze and Harder, who provide a supportive environment for learners at all levels of experience

Explores techniques to diagnose overfitting and underfitting, which are common challenges in building predictive models

Requires familiarity with Python, which may require learners to acquire basic programming skills beforehand

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 with these activities:

Review Statistical Foundations

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Strengthen your understanding of fundamental statistical concepts to better grasp the underlying principles of predictive modeling.

Browse courses on Linear Regression

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Review key statistical concepts.
Work through practice problems.
Identify areas of weakness.

Read 'Python Data Science Handbook'

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Enhance your Python skills with a comprehensive guide to data science tools.

View Python Data Science Handbook: Essential Tools... on Amazon

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Review chapters on Pandas and Scikit-learn.
Practice data manipulation techniques.
Experiment with different visualization methods.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Supplement your learning with a comprehensive guide to machine learning tools and techniques.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read relevant chapters on regression.
Experiment with the code examples.
Apply the concepts to new datasets.

Four other activities

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Practice Regression Problems on Kaggle

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Reinforce your understanding of regression techniques by working through practical problems on Kaggle.

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Find regression datasets on Kaggle.
Implement linear and logistic regression models.
Evaluate model performance.
Experiment with feature engineering.

Follow Scikit-learn Tutorials

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Improve your proficiency with Scikit-learn by following guided tutorials on the official documentation.

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Visit the Scikit-learn website.
Choose a tutorial on regression.
Follow the tutorial step-by-step.
Adapt the code to new datasets.

Create a Model Evaluation Report

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Solidify your understanding of model evaluation by creating a comprehensive report on the performance of a predictive model.

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Choose a predictive model.
Evaluate the model using metrics.
Document your findings.
Visualize the results.

Predict Customer Churn

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Apply your predictive analytics skills to a real-world problem by building a model to predict customer churn.

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Gather customer data from a source.
Clean and preprocess the data.
Build and train a predictive model.
Evaluate model performance.
Present your findings.

Career center

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

Data Scientist

The role of a data scientist is to analyze complex data sets and develop predictive models to inform business decisions. This course equips learners to excel as a data scientist by providing a strong foundation in predictive analytics techniques. You will learn to build and refine regression and classification models using Python and Scikit-learn, directly applicable to the tasks a data scientist undertakes daily. By diagnosing and improving model performance, including addressing issues like overfitting and underfitting, you'll ensure your models are robust and generalizable. Furthermore, the course covers advanced techniques such as neural networks and regularization, essential for tackling complex real-world business challenges that data scientists often encounter.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

The work of a machine learning engineer involves designing, building, and deploying machine learning models and systems. This course is particularly beneficial for aspiring machine learning engineers. You'll gain hands-on experience with Scikit-learn to develop and refine regression and classification models, directly applicable to building machine learning solutions. The course emphasizes diagnosing and improving model performance, including addressing issues like overfitting, and leveraging advanced techniques like neural networks, all critical skills for ensuring machine learning models are effective and scalable. As a machine learning engineer, the skills taught allow one to contribute to digital transformation efforts.

See salaries and explore the career path for Machine Learning Engineer

Healthcare Data Analyst

Healthcare data analysts analyze healthcare data to improve patient outcomes and operational efficiency. This course helps in the ability to leverage predictive analytics to forecast patient needs and optimize healthcare delivery. You'll gain hands-on experience with Scikit-learn to develop and refine regression and classification models, applying them to real-world healthcare scenarios. The course emphasizes diagnosing and improving model performance, ensuring your analyses are robust and generalizable, leading to more effective healthcare interventions.

See salaries and explore the career path for Healthcare Data Analyst

Business Intelligence Analyst

As a business intelligence analyst, your primary goal is to translate data into actionable insights that drive strategic decision-making. This course helps enhance your ability to perform predictive analysis, a crucial skill for a business intelligence analyst. You'll gain expertise in building predictive models and using Python and cloud-based tools to analyze data and create forecasts. The course focuses on identifying opportunities to integrate analytics into decision-making processes, ensuring your insights drive measurable outcomes, aligning perfectly with the responsibilities of a business intelligence analyst. The emphasis on diagnosing and improving model performance ensures your analyses are reliable and accurate.

See salaries and explore the career path for Business Intelligence Analyst

Supply Chain Analyst

Supply chain analysts are responsible for optimizing the flow of goods and services from suppliers to customers. This course helps build a strong analytical skillset, which is crucial for forecasting demand, managing inventory, and improving logistics. You'll gain experience with Python and cloud-based tools to build and evaluate predictive models, allowing you to identify and address supply chain inefficiencies. The course focuses on integrating analytics into decision-making processes, ensuring your insights drive measurable improvements in supply chain performance. Predictive analytics is central to this role.

See salaries and explore the career path for Supply Chain Analyst

Operations Research Analyst

Operations research analysts use mathematical and analytical methods to help organizations make better decisions. This course helps build a foundation in predictive analytics, which is essential for optimizing operational processes. You'll learn to develop and refine regression and classification models, applying them to real-world scenarios to improve efficiency and reduce costs. The course emphasizes diagnosing and improving model performance, ensuring your analyses are robust and reliable, a critical aspect of an operations research analyst's work.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

A risk analyst assesses and manages potential risks for organizations, and this course helps to analyze and predict potential risks using data-driven insights. You'll learn to build and evaluate predictive models to identify and mitigate risks across various domains. The course covers diagnosing and improving model performance, ensuring your risk assessments are accurate and reliable. By translating data-driven insights into actionable strategies, you'll be equipped to proactively manage risks and protect organizational assets, a key responsibility of a risk analyst.

See salaries and explore the career path for Risk Analyst

Digital Transformation Consultant

A digital transformation consultant advises organizations on how to leverage technology to improve their business processes and outcomes. This course helps build a foundation in predictive analytics, a critical component of many digital transformation initiatives. You'll explore predictive analytics' role in digital transformation through case-based projects and guided activities, allowing you to develop strategies to integrate analytics into decision-making processes. By the end of the course, you’ll have the expertise to lead predictive analytics projects and contribute to digital transformation efforts in any industry, making you a more valuable digital transformation consultant.

See salaries and explore the career path for Digital Transformation Consultant

Marketing Analyst

The job of a marketing analyst is to analyze marketing data to improve campaign performance and customer engagement. This course helps you to improve your marketing strategies with predictive analytics. You'll learn to build predictive models using Python and cloud-based tools, allowing you to forecast customer behavior and campaign outcomes. The course emphasizes translating data-driven insights into actionable strategies, ensuring your marketing efforts are targeted and effective. The skills taught are applicable to a variety of other industries as well.

See salaries and explore the career path for Marketing Analyst

Financial Analyst

Financial analysts provide guidance to businesses and individuals making investment decisions. This course helps a financial analyst to improve the ability to forecast financial trends and assess risk using predictive analytics. You'll gain experience with linear and logistic regression methods, as well as advanced modeling techniques, enabling you to solve complex financial challenges. The focus on building, training, and evaluating predictive models is directly applicable to financial forecasting and risk management, adding value to a financial analyst's skill set.

See salaries and explore the career path for Financial Analyst

Data Analyst

Data analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns that can inform business decisions. This course may be useful for a data analyst looking to enhance their predictive modeling skills. The course introduces essential linear and logistic regression methods and progresses to advanced modeling techniques, enabling you to solve real-world business challenges. By learning to diagnose and improve model performance, you'll enhance the accuracy and reliability of your analyses, making you a more effective data analyst. The skills taught help with the integration of analytics into decision-making.

See salaries and explore the career path for Data Analyst

Business Analyst

Business analysts identify business needs and determine solutions to business problems. This course may be useful to a business analyst by enhancing your analytical skills and providing a deeper understanding of predictive modeling. You'll learn to use data-driven insights to drive innovation and efficiency, a key aspect of a business analyst's role. The course covers diagnosing common issues such as overfitting and underfitting, ensuring your models are robust and generalizable, leading to more reliable recommendations. Predictive analytics plays a large part in the determination of solutions.

See salaries and explore the career path for Business Analyst

Actuary

Actuaries assess and manage financial risks, often in the insurance and finance industries. This course may enhance an actuary's existing skill set with advanced predictive modeling techniques. You'll learn to build and evaluate predictive models using Python and cloud-based tools, allowing you to forecast financial outcomes and assess risk more accurately. The emphasis on diagnosing and improving model performance helps develop more robust and reliable models, crucial for making sound financial decisions within an actuary setting. An advanced degree is typically expected for a role as an actuary.

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Statistician

Statisticians collect, analyze, and interpret data to identify trends and relationships. This course may contribute to the ability to develop advanced predictive models and apply statistical techniques to solve real-world problems. You'll gain experience with linear and logistic regression methods, as well as advanced modeling techniques, enabling you to tackle complex statistical challenges. The focus on diagnosing and improving model performance ensures your analyses are robust and reliable, an important aspect of statistical work. A master's degree or PhD is often required to work as a statistician.

See salaries and explore the career path for Statistician

Economist

Economists study the production, distribution, and consumption of goods and services. This course may enhance an economist's ability to forecast economic trends and analyze market behavior using predictive analytics. You'll gain experience with linear and logistic regression methods, as well as advanced modeling techniques, enabling you to tackle complex economic challenges. The focus on building, training, and evaluating predictive models is directly applicable to economic forecasting and policy analysis. Many economist roles typically require a master's degree or PhD.

See salaries and explore the career path for Economist

Predictive Analytics

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