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Dan Zhang
Welcome to the second course in the Data Analytics for Business specialization!
Read more
Welcome to the second course in the Data Analytics for Business specialization!
Read more
Welcome to the second course in the Data Analytics for Business specialization!
This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business.
You’ll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way. We will use a practical predictive modeling software, XLMiner, which is a popular Excel plug-in. This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning.
The expected prerequisites for this course include a prior working knowledge of Excel, introductory level algebra, and basic statistics.
Register for this course and see more details by visiting:
OpenCourser.com/course/ejcmbm/predictive
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What's inside
Syllabus
Exploratory Data Analysis and Visualizations
At the end of this module students will be able to: 1. Carry out exploratory data analysis to gain insights and prepare data for predictive modeling 2. Summarize and visualize datasets using appropriate tools 3. Identify modeling techniques for prediction of continuous and discrete outcomes. 4. Explore datasets using Excel 5. Explain and perform several common data preprocessing steps 6. Choose appropriate graphs to explore and display datasets
Read more
Syllabus
Exploratory Data Analysis and Visualizations
At the end of this module students will be able to: 1. Carry out exploratory data analysis to gain insights and prepare data for predictive modeling 2. Summarize and visualize datasets using appropriate tools 3. Identify modeling techniques for prediction of continuous and discrete outcomes. 4. Explore datasets using Excel 5. Explain and perform several common data preprocessing steps 6. Choose appropriate graphs to explore and display datasets
Read more
Predicting a Continuous Variable
This module introduces regression techniques to predict the value of continuous variables. Some fundamental concepts of predictive modeling are covered, including cross-validation, model selection, and overfitting. You will also learn how to build predictive models using the software tool XLMiner.
Predicting a Binary Outcome
This module introduces logistic regression models to predict the value of binary variables. Unlike continuous variables, a binary variable can only take two different values and predicting its value is commonly called classification. Several important concepts regarding classification are discussed, including cross validation and confusion matrix, cost sensitive classification, and ROC curves. You will also learn how to build classification models using the software tool XLMiner.
Trees and Other Predictive Models
This module introduces more advanced predictive models, including trees and neural networks. Both trees and neural networks can be used to predict continuous or binary variables. You will also learn how to build trees and neural networks using the software tool XLMiner.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores predictive modeling techniques, which are widely used in various functional areas within business organizations
Builds a solid foundation in predictive analytics, covering tools and techniques for building statistical or machine learning models
Taught by Dan Zhang, who provides expertise in predictive modeling
Uses XLMiner, a popular Excel plug-in, for hands-on practice in predictive modeling
Requires prior working knowledge of Excel, introductory level algebra, and basic statistics
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Reviews summary
Predictive modeling and analytics techniques
Learners say this course provides a good foundation for predictive modeling and analytics. Its lectures, assignments, and quizzes cover basic and advanced models, such as linear regression, boosting, and ensemble models. However, many note that the course heavily relies on XL Miner, a paid Excel add-on with limited accessibility. Some learners found the instructor's fast-paced delivery and accent challenging to understand. Overall, this course is recommended for those interested in gaining a broad understanding of predictive modeling techniques.
Engaging assignments that reinforce learning.
"I really enjoyed every aspect of the class it was well designed and the excises were both enjoyable and informative."
"This course gives a good background to building predictive models as well an easy to use tool for learning how to do it. "
Covers a wide range of predictive modeling topics.
"This course mainly aims at someone who knows about econometric regression and basics of ML algorithms."
"There waw a lot of information but it wasn't clear or how was related with the previous course."
"The knowledge and information are very useful."
"Covers a lot of essential topics around analytics and predictive modeling."
Knowledgeable but difficult to understand due to fast-paced delivery and accent.
"The instructor speaks to fast and it's hard to understand what he said too many times."
"I had to use the lesson subtitles and slide pack to study the material; then just used to play video lesson to be able to open the next lesson."
"The instructor's tone was difficult to Interpret. It was not fluent and pronunciations were uneasy."
Paid Excel add-on with a short trial period.
"You get stuck after week 2 in the assigment, because the software needed is not allowed in all coporate enviroments."
"The course is mainly a push to use a tool which is not free and an add-on to Excel."
"I found the instructor did a very poor job of explaining the concepts he was trying to get across. He read the material as fast as he could and it was obvious."
"Unfortunately I was not very happy with this course. The teacher's accent was very hard to understand most of the time."
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 Modeling and Analytics with these
activities:
Seek guidance from a mentor or expert
Show steps
Connect with a mentor or experienced professional to gain insights and support in your pursuit of predictive modeling.
Show steps
-
Identify potential mentors who have expertise in predictive modeling.
-
Reach out to your potential mentors and introduce yourself.
Organize and synthesize course materials
Show steps
Stay organized and enhance your understanding by compiling and synthesizing course materials into a cohesive format.
Show steps
-
Create a system for organizing notes, assignments, and quizzes.
-
Summarize and synthesize key concepts from each module.
Explore XLMiner's features
Show steps
Familiarize yourself with XLMiner's features to enhance your ability to build and evaluate predictive models effectively.
Show steps
-
Watch tutorials on XLMiner's interface and functionalities.
-
Practice using XLMiner's data exploration and visualization tools.
-
Experiment with different predictive modeling algorithms in XLMiner.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice data cleaning and preparation
Show steps
Practice data cleaning and preparation to gain hands-on experience and reinforce your understanding of these essential skills.
Browse courses on
Data Cleaning
Show steps
-
Download a dataset from a reputable source.
-
Identify and remove any duplicate or incomplete rows.
-
Check for missing values and apply appropriate imputation techniques.
-
Transform and normalize the data as necessary.
Discuss and share insights on predictive modeling
Show steps
Engage with peers to discuss and share your insights on predictive modeling techniques, challenges, and best practices.
Show steps
-
Join or create a study group with other students in the course.
-
Discuss different types of predictive modeling techniques.
-
Share and critique each other's predictive models.
Learn about cross-validation and model selection
Show steps
Understand the importance of cross-validation and model selection to improve the reliability and generalization of your predictive models.
Browse courses on
Cross-Validation
Show steps
-
Watch tutorials on cross-validation and model selection techniques.
-
Practice using cross-validation and model selection methods in XLMiner or another software tool.
Build a predictive model for a real-world dataset
Show steps
Apply your knowledge to a real-world problem by building a predictive model using XLMiner or another software tool.
Browse courses on
Data Analysis
Show steps
-
Identify a problem or question that can be addressed with predictive modeling.
-
Gather and prepare a relevant dataset.
-
Select and train a suitable predictive model.
-
Evaluate the model's performance using appropriate metrics.
Seek guidance from a mentor or expert
Show steps
Connect with a mentor or experienced professional to gain insights and support in your pursuit of predictive modeling.
Show steps
Show steps
Show steps
- Identify potential mentors who have expertise in predictive modeling.
- Reach out to your potential mentors and introduce yourself.
Organize and synthesize course materials
Show steps
Stay organized and enhance your understanding by compiling and synthesizing course materials into a cohesive format.
Show steps
Show steps
Show steps
- Create a system for organizing notes, assignments, and quizzes.
- Summarize and synthesize key concepts from each module.
Explore XLMiner's features
Show steps
Familiarize yourself with XLMiner's features to enhance your ability to build and evaluate predictive models effectively.
Show steps
Show steps
Show steps
- Watch tutorials on XLMiner's interface and functionalities.
- Practice using XLMiner's data exploration and visualization tools.
- Experiment with different predictive modeling algorithms in XLMiner.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice data cleaning and preparation
Show steps
Practice data cleaning and preparation to gain hands-on experience and reinforce your understanding of these essential skills.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Download a dataset from a reputable source.
- Identify and remove any duplicate or incomplete rows.
- Check for missing values and apply appropriate imputation techniques.
- Transform and normalize the data as necessary.
Discuss and share insights on predictive modeling
Show steps
Engage with peers to discuss and share your insights on predictive modeling techniques, challenges, and best practices.
Show steps
Show steps
Show steps
- Join or create a study group with other students in the course.
- Discuss different types of predictive modeling techniques.
- Share and critique each other's predictive models.
Learn about cross-validation and model selection
Show steps
Understand the importance of cross-validation and model selection to improve the reliability and generalization of your predictive models.
Browse courses on
Cross-Validation
Show steps
Show steps
Show steps
- Watch tutorials on cross-validation and model selection techniques.
- Practice using cross-validation and model selection methods in XLMiner or another software tool.
Build a predictive model for a real-world dataset
Show steps
Apply your knowledge to a real-world problem by building a predictive model using XLMiner or another software tool.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a problem or question that can be addressed with predictive modeling.
- Gather and prepare a relevant dataset.
- Select and train a suitable predictive model.
- Evaluate the model's performance using appropriate metrics.
Career center
Learners who complete Predictive Modeling and Analytics will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use data analysis to improve business outcomes. They analyze data, identify trends, and use their findings to make recommendations to businesses. Predictive analytics is a major component of data analysis that helps data analysts identify key business trends and make more accurate predictions about future events. This course is a foundational step towards becoming a data analyst as it introduces the most common predictive modeling techniques and prepares learners to build models in the popular industry software XLMiner.
Market Research Analyst
Market Research Analyst
Market Research Analysts perform research to understand and analyze consumer behavior and market trends. Predictive analytics is a powerful tool for market research analysts as it can help them predict consumer behavior and develop more effective marketing campaigns. This course can help market research analysts build a foundation of knowledge in predictive analytics and prepare them to use XLMiner, an industry-standard software.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions and provide financial advice. Predictive analytics is used by financial analysts to predict the future performance of stocks, bonds, and other financial instruments. This course can help financial analysts build a strong foundation in predictive analytics and prepare them to use XLMiner to build more accurate financial models.
Business Analyst
Business Analyst
Business Analysts use data analysis to identify inefficiencies and improve organizational performance. Predictive analytics is becoming increasingly important for business analysts as it can help them predict future business outcomes and make better decisions. This course can help business analysts build a solid foundation in predictive analytics and gain proficiency in using XLMiner.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to solve complex business problems. They rely heavily on predictive analytics to build predictive models that can help organizations identify trends and make more informed decisions. This course can help data scientists build a strong foundation in predictive analytics and gain valuable experience using XLMiner.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. Machine learning models are used to predict future outcomes and make intelligent decisions. Predictive analytics is a core component of machine learning, and this course can help machine learning engineers develop a strong foundation in predictive analytics and gain proficiency using XLMiner, a popular industry software.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risks for organizations. They rely on predictive analytics to identify and quantify risks and develop mitigation strategies. This course can help risk analysts build a solid foundation in predictive analytics and gain proficiency in using XLMiner.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. They rely on predictive analytics to forecast demand, optimize supply chains, and improve decision-making. This course can help operations research analysts build a strong foundation in predictive analytics and gain valuable experience using XLMiner.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze and predict financial data. They rely heavily on predictive analytics to develop trading strategies and make investment decisions. This course can help quantitative analysts build a foundation in predictive analytics and prepare them to use XLMiner, a popular financial modeling software.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to create reports and dashboards that help businesses understand their performance and make better decisions. They often rely on predictive analytics to identify trends and forecast future performance. This course can help business intelligence analysts build a solid foundation in predictive analytics and gain proficiency using XLMiner.
Statistical Analyst
Statistical Analyst
Statistical Analysts use statistical methods to collect, analyze, and interpret data. They rely heavily on predictive analytics to build predictive models and make informed decisions. This course can help statistical analysts build a solid foundation in predictive analytics and gain proficiency using XLMiner.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. They often work with data analysts and data scientists to ensure that data is available and accessible for analysis. This course can help data engineers gain a foundational understanding of predictive analytics and prepare them to work with predictive models.
Predictive Modeler
Predictive Modeler
Predictive Modelers use statistical and machine learning techniques to build predictive models. They use these models to predict future outcomes and make informed decisions. This course can help predictive modelers build a strong foundation in predictive analytics and gain valuable experience using XLMiner.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They ensure that data is available and accessible for analysis and reporting. This course can help database administrators gain a foundational understanding of predictive analytics and prepare them to work with predictive models.
Data Warehouse Architect
Data Warehouse Architect
Data Warehouse Architects design and build data warehouses. Data warehouses are used to store and manage large amounts of data for analysis and reporting. This course can help data warehouse architects gain a foundational understanding of predictive analytics and prepare them to work with predictive models.
For more career information including salaries, visit:
OpenCourser.com/course/ejcmbm/predictive
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Predictive Modeling and Analytics .
Provides a comprehensive introduction to statistical learning methods.
It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection.
More advanced treatment of statistical learning methods.
It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection.
Save
Provides a comprehensive introduction to deep learning.
It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive introduction to reinforcement learning.
It covers a wide range of topics, including Markov decision processes, value functions, and policy gradients.
Provides a practical guide to using R to build and deploy machine learning models.
It covers a wide range of topics, including data preparation, feature engineering, model selection, and evaluation.
Provides a practical guide to applying machine learning to business problems.
It covers a wide range of topics, including data preparation, feature engineering, model selection, and evaluation.
Save
Provides a comprehensive introduction to data mining.
It covers a wide range of topics, including data preprocessing, data clustering, and data classification.
Provides a practical guide to applying big data analytics to business problems.
It covers a wide range of topics, including data collection, data storage, data processing, and data visualization.
Save
Provides a practical introduction to data visualization.
It covers a wide range of topics, including data visualization techniques, data visualization tools, and data visualization best practices.
Provides a practical guide to using Tableau to create data visualizations.
It covers a wide range of topics, including data preparation, data visualization techniques, and data visualization best practices.
Provides a non-technical overview of data analytics and its applications in business.
It covers a wide range of topics, including data collection, data cleaning, data analysis, and data visualization.
Provides a non-technical overview of data science and its applications in business.
It covers a wide range of topics, including data collection, data cleaning, data analysis, and data visualization.
Provides a practical guide to using Excel to perform data analysis and business modeling.
It covers a wide range of topics, including data preparation, data analysis techniques, and data visualization.
Provides a practical guide to using Excel to perform statistical analysis.
It covers a wide range of topics, including data preparation, data analysis techniques, and data visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ejcmbm/predictive
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Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Dan Zhang
Language
English
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This course belongs to a
collection
called
Advanced Business Analytics. It's comprised of five courses:
- Communicating Business Analytics Results
- Advanced Business Analytics Capstone
- Introduction to Data Analytics for Business
- Predictive Modeling and Analytics (viewing)
- Business Analytics for Decision Making
Good to know
Explores predictive modeling techniques, which are widely used in various functional areas within business organizations
Builds a solid foundation in predictive analytics, covering tools and techniques for building statistical or machine learning models
Taught by Dan Zhang, who provides expertise in predictive modeling
Uses XLMiner, a popular Excel plug-in, for hands-on practice in predictive modeling
Requires prior working knowledge of Excel, introductory level algebra, and basic statistics
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