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Bill Howe
Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.
Read more
Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.
Read more
Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.
Learning Goals: After completing this course, you will be able to:
1. Design effective experiments and analyze the results
2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation
3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants)
4. Explain and apply a set of unsupervised learning concepts and methods
5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection
Register for this course and see more details by visiting:
OpenCourser.com/course/mb6cd6/practical
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What's inside
Syllabus
Practical Statistical Inference
Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.
Read more
Syllabus
Practical Statistical Inference
Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.
Read more
Supervised Learning
Follow a tour through the important methods, algorithms, and techniques in machine learning. You will learn how these methods build upon each other and can be combined into practical algorithms that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.
Optimization
You will learn how to optimize a cost function using gradient descent, including popular variants that use randomization and parallelization to improve performance. You will gain an intuition for popular methods used in practice and see how similar they are fundamentally.
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes foundational concepts and up-to-date techniques in statistical experiment design and analysis
Prepares learners to critically evaluate statistical arguments, especially those associated with big data
Instructs modern methods in statistical inference, resampling, machine learning, optimization, and unsupervised learning
Includes real-world applications and practical problem-solving
Provides exposure to the common idioms of large-scale graph analytics, including structural query, traversals, and recursive queries
Addresses prevalent concerns in the scientific community, such as publication bias and reproducibility
Save this course
Save Practical Predictive Analytics: Models and Methods to your list so you can find it easily later:
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Reviews summary
Predictive analytics focus
Learners say this course takes a practical approach to predictive analytics, focusing on hands-on practice. Some students found the course outdated or unchallenging.
Focuses on applied learning.
"Hands on practices are very good."
"learning predictive model was a challenge."
May not cover latest methods.
"This course is quite outdated."
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 Practical Predictive Analytics: Models and Methods with these
activities:
Review Statistical Inference
Show steps
Review the fundamentals of statistical inference to strengthen your understanding of the principles covered in the course.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
-
Read the first three chapters of the book.
-
Complete the practice exercises at the end of each chapter.
Review the book 'Introduction to Statistical Learning'
Show steps
Reinforce the concepts covered in the course with a comprehensive overview of statistical learning.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read the book.
-
Take notes and highlight important passages.
-
Review the material regularly.
Join a study group to discuss course concepts
Show steps
Engage with other students to enhance comprehension and retention of course material.
Browse courses on
Data Science
Show steps
-
Find a study group or form one with classmates.
-
Meet regularly to discuss course concepts.
-
Work together on assignments and projects.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow tutorials on statistical inference
Show steps
Gain practical experience in using statistical inference techniques to analyze data.
Browse courses on
Statistical Inference
Show steps
-
Find tutorials on statistical inference.
-
Follow the tutorials and complete the exercises.
-
Apply the techniques to real-world data.
Build a statistical experiment design
Show steps
Build a foundation for understanding the principles of statistical experiment design and data analysis.
Show steps
-
Choose a research question and define the hypothesis.
-
Determine the type of statistical experiment to be conducted.
-
Select the appropriate statistical tests to be used.
-
Collect the data.
-
Analyze the data and interpret the results.
Attend a workshop on machine learning
Show steps
Expand knowledge and skills in machine learning through practical, hands-on experience in a workshop setting.
Browse courses on
Machine Learning
Show steps
-
Find a workshop on machine learning that aligns with interests and skill level.
-
Register for the workshop.
-
Attend the workshop and actively participate in activities.
Solve practice problems on classification methods
Show steps
Develop proficiency in applying classification methods to solve real-world problems.
Browse courses on
Classification Methods
Show steps
-
Find practice problems on classification methods.
-
Solve the problems using the techniques learned in the course.
-
Review the solutions and identify areas for improvement.
Create a presentation on unsupervised learning concepts
Show steps
Demonstrate an understanding of unsupervised learning concepts and their applications.
Browse courses on
Unsupervised Learning
Show steps
-
Choose a topic for the presentation.
-
Research the topic and gather information.
-
Create a presentation that is clear, concise, and engaging.
-
Present the presentation to an audience.
Create a blog post on a statistical topic
Show steps
Demonstrate understanding of course concepts by creating original content that explores a statistical topic.
Browse courses on
Data Science
Show steps
-
Choose a statistical topic to write about.
-
Research the topic and gather information.
-
Write a clear and concise blog post.
-
Publish the blog post.
Review Statistical Inference
Show steps
Review the fundamentals of statistical inference to strengthen your understanding of the principles covered in the course.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Complete the practice exercises at the end of each chapter.
Review the book 'Introduction to Statistical Learning'
Show steps
Reinforce the concepts covered in the course with a comprehensive overview of statistical learning.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read the book.
- Take notes and highlight important passages.
- Review the material regularly.
Join a study group to discuss course concepts
Show steps
Engage with other students to enhance comprehension and retention of course material.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a study group or form one with classmates.
- Meet regularly to discuss course concepts.
- Work together on assignments and projects.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow tutorials on statistical inference
Show steps
Gain practical experience in using statistical inference techniques to analyze data.
Browse courses on
Statistical Inference
Show steps
Show steps
Show steps
- Find tutorials on statistical inference.
- Follow the tutorials and complete the exercises.
- Apply the techniques to real-world data.
Build a statistical experiment design
Show steps
Build a foundation for understanding the principles of statistical experiment design and data analysis.
Show steps
Show steps
Show steps
- Choose a research question and define the hypothesis.
- Determine the type of statistical experiment to be conducted.
- Select the appropriate statistical tests to be used.
- Collect the data.
- Analyze the data and interpret the results.
Attend a workshop on machine learning
Show steps
Expand knowledge and skills in machine learning through practical, hands-on experience in a workshop setting.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a workshop on machine learning that aligns with interests and skill level.
- Register for the workshop.
- Attend the workshop and actively participate in activities.
Solve practice problems on classification methods
Show steps
Develop proficiency in applying classification methods to solve real-world problems.
Browse courses on
Classification Methods
Show steps
Show steps
Show steps
- Find practice problems on classification methods.
- Solve the problems using the techniques learned in the course.
- Review the solutions and identify areas for improvement.
Create a presentation on unsupervised learning concepts
Show steps
Demonstrate an understanding of unsupervised learning concepts and their applications.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Choose a topic for the presentation.
- Research the topic and gather information.
- Create a presentation that is clear, concise, and engaging.
- Present the presentation to an audience.
Create a blog post on a statistical topic
Show steps
Demonstrate understanding of course concepts by creating original content that explores a statistical topic.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Choose a statistical topic to write about.
- Research the topic and gather information.
- Write a clear and concise blog post.
- Publish the blog post.
Career center
Learners who complete Practical Predictive Analytics: Models and Methods will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you will use your knowledge of statistics, modeling, and machine learning to solve real-world problems. The Practical Predictive Analytics course provides a comprehensive overview of these topics, which will help you develop the skills needed for a successful career as a Data Scientist. In particular, the course's focus on supervised and unsupervised learning, as well as optimization, will give you the practical experience you need to excel in this field.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will design, develop, and implement machine learning models. The Practical Predictive Analytics course provides a solid foundation in machine learning, including supervised and unsupervised learning, optimization, and graph analytics. This knowledge will be invaluable as you build your career in machine learning engineering. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed in this rapidly growing field.
Data Analyst
Data Analyst
As a Data Analyst, your daily tasks include collecting, cleaning, analyzing, interpreting, and visualizing data. To perform these tasks, you will need to be familiar with statistics, modeling, and machine learning. The Practical Predictive Analytics course covers all of these topics in detail, making it a valuable learning experience for aspiring Data Analysts. Specifically, the course's emphasis on statistical inference and machine learning can help you build a strong foundation for a career in data analysis.
Business Analyst
Business Analyst
As a Business Analyst, you will use data to identify and solve business problems. The Practical Predictive Analytics course can help you build the skills needed for this role, as it provides an introduction to statistical inference, machine learning, and optimization. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as a Business Analyst.
Statistician
Statistician
As a Statistician, you will collect, analyze, and interpret data to provide insights to businesses and organizations. The Practical Predictive Analytics course provides a comprehensive overview of statistical inference, machine learning, and optimization, which are essential skills for success in this field. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as a Statistician.
Data Architect
Data Architect
As a Data Architect, you will design and implement data architectures for organizations. Knowledge of statistics, modeling, and machine learning is essential for success in this role. The Practical Predictive Analytics course provides a comprehensive overview of these topics. The course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as a Data Architect.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will use data to analyze financial markets and make investment recommendations. The Practical Predictive Analytics course provides a solid foundation in statistical inference, machine learning, and optimization, which are essential skills for success in this field. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as a Financial Analyst.
Risk Analyst
Risk Analyst
As a Risk Analyst, you will use data to identify and assess risks to businesses and organizations. The Practical Predictive Analytics course provides a solid foundation in statistical inference, machine learning, and optimization, which are essential skills for success in this field. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as a Risk Analyst.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will use mathematical and statistical models to analyze financial data. The Practical Predictive Analytics course can help you build the skills needed for this role, as it provides an introduction to statistical inference, machine learning, and optimization. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as a Quantitative Analyst.
Operations Research Analyst
Operations Research Analyst
As an Operations Research Analyst, you will use mathematical and statistical models to improve the efficiency and productivity of organizations. The Practical Predictive Analytics course provides a solid foundation in statistical inference, machine learning, and optimization, which are essential skills for success in this field. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as an Operations Research Analyst.
Research Analyst
Research Analyst
As a Research Analyst, you will collect, analyze, and interpret data to provide insights to businesses and organizations. The Practical Predictive Analytics course can help you build the skills needed for this role, as it covers topics such as statistical inference, machine learning, and optimization. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed in research analysis.
Software Engineer
Software Engineer
As a Software Engineer, you will design, develop, and implement software applications. The Practical Predictive Analytics course can help you build the skills needed for this role, as it provides an introduction to statistical inference, machine learning, and optimization. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as a Software Engineer.
Data Engineer
Data Engineer
As a Data Engineer, your responsibilities will include building and maintaining data pipelines, as well as developing and implementing data processing algorithms. The Practical Predictive Analytics course can help you build the skills needed for this role, as it provides an introduction to statistical inference, machine learning, and optimization. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as a Data Engineer.
Computer Scientist
Computer Scientist
As a Computer Scientist, you will research and develop new computer technologies. The Practical Predictive Analytics course can help you build the skills needed for this role, as it provides an introduction to statistical inference, machine learning, and optimization. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as a Computer Scientist.
Actuary
Actuary
As an Actuary, you will use mathematical and statistical models to assess risk and uncertainty. The Practical Predictive Analytics course provides a solid foundation in statistical inference, machine learning, and optimization, which are essential skills for success in this field. In particular, the course's emphasis on practical methods and algorithms will help you develop the skills needed to succeed as an Actuary.
For more career information including salaries, visit:
OpenCourser.com/course/mb6cd6/practical
Reading list
We've selected 26 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
Practical Predictive Analytics: Models and Methods.
Save
Comprehensive guide to deep learning, and it covers a wide range of topics, from the basics to the latest advances.
Comprehensive text on machine learning and pattern recognition. It provides a comprehensive overview of machine learning algorithms and techniques, including supervised learning, unsupervised learning, and reinforcement learning.
Classic text on statistical learning, and it provides a comprehensive overview of the field.
Provides a comprehensive introduction to machine learning from a probabilistic perspective.
Provides a comprehensive introduction to information theory, inference, and learning algorithms.
Provides a comprehensive overview of statistical learning methods, including linear regression, logistic regression, decision trees, support vector machines, and clustering. It also covers advanced topics such as Bayesian methods and ensemble methods.
Save
Classic text on machine learning. It provides a comprehensive overview of machine learning concepts and techniques, including supervised learning, unsupervised learning, and reinforcement learning.
Teaches readers to build machine learning systems using popular Python libraries. It provides a comprehensive overview of machine learning concepts and techniques, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to computer networks.
Provides a comprehensive introduction to cryptography and network security.
Save
Provides a comprehensive introduction to cloud computing.
Provides a solid foundation in probability and statistics, which is essential for understanding the theoretical underpinnings of machine learning.
Provides a comprehensive introduction to linear algebra, which fundamental tool for machine learning.
Provides a comprehensive introduction to convex optimization, which fundamental tool for machine learning.
Provides a comprehensive introduction to artificial intelligence.
Provides a comprehensive introduction to algorithms.
Provides a comprehensive introduction to computer organization and design.
Provides a comprehensive introduction to operating system concepts.
Provides a comprehensive overview of natural language processing with Python. It covers topics such as text preprocessing, feature extraction, and machine learning for natural language processing.
Provides a comprehensive overview of speech and language processing. It covers topics such as speech recognition, natural language understanding, and speech synthesis.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers topics such as image processing, feature extraction, and object recognition.
Textbook on applied statistics with S-PLUS. It provides a comprehensive overview of statistical methods and techniques, including data exploration, hypothesis testing, and regression analysis.
Practical guide to machine learning with Python. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Practical guide to R programming. It covers topics such as data manipulation, visualization, and statistical modeling.
Practical guide to data analysis with Python. It covers topics such as data manipulation, visualization, and statistical modeling.
Practical guide to data science from scratch. It covers topics such as data wrangling, data analysis, and machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mb6cd6/practical
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Effort
4 weeks of study, 6-8 hours/week
Via
Coursera
Institution
University of Washington
Instructor
Bill Howe
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Data Science at Scale. It's comprised of four courses:
- Communicating Data Science Results
- Data Science at Scale - Capstone Project
- Data Manipulation at Scale: Systems and Algorithms
- Practical Predictive Analytics: Models and Methods (viewing)
Good to know
Emphasizes foundational concepts and up-to-date techniques in statistical experiment design and analysis
Prepares learners to critically evaluate statistical arguments, especially those associated with big data
Instructs modern methods in statistical inference, resampling, machine learning, optimization, and unsupervised learning
Includes real-world applications and practical problem-solving
Provides exposure to the common idioms of large-scale graph analytics, including structural query, traversals, and recursive queries
Addresses prevalent concerns in the scientific community, such as publication bias and reproducibility
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