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Dennis Davenport
and
MOUSSA DOUMBIA
In this course, you'll review the specifics of the Capstone project. In addition, you will create and run your regression model and share your results with your peers. Let's get started!
Register for this course and see more details by visiting:
OpenCourser.com/course/a2b4pw/capstone
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What's inside
Syllabus
Introduction to Specialization and Course
In module 1, you’ll learn about the steps you will take to complete the Capstone Project. We will cover the following learning objectives.
Read more
Syllabus
Introduction to Specialization and Course
In module 1, you’ll learn about the steps you will take to complete the Capstone Project. We will cover the following learning objectives.
Read more
Data Wrangling & Using the PCA Function
Let’s recap! In module 1, you were introduced to the Capstone project and Tasks 1-4. In module 2, you’ll complete Task 1: Data wrangling and Task 2: Use PCA to reduce the number of dimensions. We will cover the following learning objectives.
Run Your Model and Interpret Your Results
Let’s recap! In module 2, you learned how to complete Tasks 1 & Task 2. In module 3, you will learn how to complete Task 3: Run your regression model and Task 4: Interpret the results from your model. We will cover the following learning objectives.
Peer Review: Interpreting Results Using Your Model
Welcome to the final module of this course! Over the past 3 modules, you have been introduced to and gained knowledge on the following topics: Data wrangling (Task 1),Use PCA to Reduce the Number of Dimensions (Task 2), Run your regression model (Task 3) and, Interpret the results from your model (Task 4). In this final module you will prepare your final Capstone Project, submit it, discuss what you learned and complete peer reviews. We will cover the following learning objectives.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches regression modeling, which is highly relevant to roles like data analyst
Taught by Dennis Davenport and MOUSSA DOUMBIA, who are recognized experts in regression modeling
Provides hands-on experience in running regression models and interpreting results
Build a strong foundation for learners who are new to regression modeling
This course is part of a specialization, which indicates comprehensiveness and detail
Requires learners to come in with some background knowledge in statistics
Save this course
Save Capstone: Data Science Problem in Linear Algebra Framework to your list so you can find it easily later:
Save
Save
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 Capstone: Data Science Problem in Linear Algebra Framework with these
activities:
Organize course materials for future reference
Show steps
Organizing your course materials will ensure easy access to important information and facilitate future review.
Show steps
-
Create a dedicated folder or notebook for course materials
-
Categorize and file lecture notes, assignments, quizzes, and exams
-
Digitize or scan important documents for easy retrieval
Review linear regression concepts
Show steps
Reviewing linear regression concepts will provide a strong foundation for understanding the course material and applying it to real-world problems.
Browse courses on
Linear Regression
Show steps
-
Read through lecture notes or textbooks on linear regression
-
Solve practice problems on linear regression
-
Review online tutorials or videos on linear regression
Discuss linear regression models with peers
Show steps
Engaging in discussions with peers will enhance your understanding of linear regression models and provide valuable feedback.
Show steps
-
Join study groups or online forums dedicated to linear regression
-
Organize or participate in discussions on specific linear regression topics
-
Share your experiences and insights with your peers
-
Provide constructive feedback and engage in critical thinking
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice running linear regression models
Show steps
Regularly practicing running linear regression models will enhance your proficiency in using the techniques taught in the course.
Show steps
-
Use a statistical software package (e.g., R, Python) to run linear regression models on different datasets
-
Experiment with different model parameters and evaluate the results
-
Solve case studies or challenges involving linear regression
-
Participate in online coding challenges or hackathons that focus on linear regression
Apply linear regression to analyze real-world data
Show steps
Applying linear regression to analyze real-world data will provide practical experience and demonstrate your understanding of the course concepts.
Show steps
-
Gather and clean a dataset relevant to your interests
-
Apply linear regression techniques to build a model for predicting a target variable
-
Interpret the results of the model and draw meaningful conclusions
-
Create a presentation or report summarizing your findings
Present your linear regression project to others
Show steps
Presenting your linear regression project to others will enhance your communication and presentation skills, while also receiving feedback from your peers.
Show steps
-
Prepare a presentation summarizing your linear regression project
-
Deliver your presentation to a group of peers or classmates
-
Receive feedback and engage in a discussion about your project
-
Reflect on the feedback and consider how to improve your project
Explore advanced linear regression techniques
Show steps
Venturing into advanced linear regression techniques will broaden your knowledge and equip you with valuable skills for future research or applications.
Show steps
-
Identify specific advanced linear regression techniques that align with your interests
-
Locate online tutorials or courses that cover these techniques
-
Follow the tutorials and complete the associated exercises
-
Implement the techniques in your own projects or research
Participate in linear regression competitions
Show steps
Participating in linear regression competitions will challenge your skills, foster collaboration, and enhance your professional reputation.
Show steps
-
Identify relevant linear regression competitions or hackathons
-
Form a team or collaborate with others
-
Develop and implement innovative solutions to complex linear regression problems
-
Showcase your work and compete against other teams
Organize course materials for future reference
Show steps
Organizing your course materials will ensure easy access to important information and facilitate future review.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials
- Categorize and file lecture notes, assignments, quizzes, and exams
- Digitize or scan important documents for easy retrieval
Review linear regression concepts
Show steps
Reviewing linear regression concepts will provide a strong foundation for understanding the course material and applying it to real-world problems.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Read through lecture notes or textbooks on linear regression
- Solve practice problems on linear regression
- Review online tutorials or videos on linear regression
Discuss linear regression models with peers
Show steps
Engaging in discussions with peers will enhance your understanding of linear regression models and provide valuable feedback.
Show steps
Show steps
Show steps
- Join study groups or online forums dedicated to linear regression
- Organize or participate in discussions on specific linear regression topics
- Share your experiences and insights with your peers
- Provide constructive feedback and engage in critical thinking
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice running linear regression models
Show steps
Regularly practicing running linear regression models will enhance your proficiency in using the techniques taught in the course.
Show steps
Show steps
Show steps
- Use a statistical software package (e.g., R, Python) to run linear regression models on different datasets
- Experiment with different model parameters and evaluate the results
- Solve case studies or challenges involving linear regression
- Participate in online coding challenges or hackathons that focus on linear regression
Apply linear regression to analyze real-world data
Show steps
Applying linear regression to analyze real-world data will provide practical experience and demonstrate your understanding of the course concepts.
Show steps
Show steps
Show steps
- Gather and clean a dataset relevant to your interests
- Apply linear regression techniques to build a model for predicting a target variable
- Interpret the results of the model and draw meaningful conclusions
- Create a presentation or report summarizing your findings
Present your linear regression project to others
Show steps
Presenting your linear regression project to others will enhance your communication and presentation skills, while also receiving feedback from your peers.
Show steps
Show steps
Show steps
- Prepare a presentation summarizing your linear regression project
- Deliver your presentation to a group of peers or classmates
- Receive feedback and engage in a discussion about your project
- Reflect on the feedback and consider how to improve your project
Explore advanced linear regression techniques
Show steps
Venturing into advanced linear regression techniques will broaden your knowledge and equip you with valuable skills for future research or applications.
Show steps
Show steps
Show steps
- Identify specific advanced linear regression techniques that align with your interests
- Locate online tutorials or courses that cover these techniques
- Follow the tutorials and complete the associated exercises
- Implement the techniques in your own projects or research
Participate in linear regression competitions
Show steps
Participating in linear regression competitions will challenge your skills, foster collaboration, and enhance your professional reputation.
Show steps
Show steps
Show steps
- Identify relevant linear regression competitions or hackathons
- Form a team or collaborate with others
- Develop and implement innovative solutions to complex linear regression problems
- Showcase your work and compete against other teams
Career center
Learners who complete Capstone: Data Science Problem in Linear Algebra Framework will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts apply analytical and technical skills to transform data into valuable insights. Leveraging statistical and machine learning techniques, they build and maintain data models, gather and interpret data, and present findings to stakeholders to help organizations make data-driven decisions. This course can help build a foundation and enhance skills required for this role, such as data wrangling, dimension reduction, regression modeling, and interpretation of results. It can help prepare individuals for success as Data Analysts by providing practical, hands-on experience in these key areas.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers leverage their expertise in programming, algorithms, and data analysis to design, build, and maintain machine learning models for a variety of applications. They work on data preparation, model development, training, and deployment. This course can be especially helpful for aspiring Machine Learning Engineers, providing a structured approach to understanding the principles and practical aspects of regression modeling. It covers essential steps such as data wrangling, dimension reduction, and interpreting results, enhancing their ability to develop and execute successful machine learning solutions.
Data Scientist
Data Scientist
Data Scientists combine knowledge of data analysis, machine learning, and statistics to extract meaningful insights from complex data. They play a crucial role in solving business problems and driving data-informed decision-making. This course can provide a solid foundation for individuals aiming to become Data Scientists. It introduces regression modeling, a key statistical technique used in data science, and guides learners through the process of preparing data, selecting variables, training models, and interpreting results. By gaining these skills, participants can increase their competitiveness in this in-demand field.
Business Analyst
Business Analyst
Business Analysts bridge the gap between business needs and technical solutions. They analyze business processes, identify areas for improvement, and leverage data to make recommendations for optimizing operations. This course can be beneficial for Business Analysts, as it provides a framework for understanding data analysis techniques. It covers data wrangling, dimension reduction, regression modeling, and interpretation, equipping individuals with the skills to analyze data, draw insights, and communicate findings to stakeholders effectively.
Financial Analyst
Financial Analyst
Financial Analysts assess financial data and make recommendations for investment decisions. They use data to evaluate the performance of companies, analyze market trends, and forecast future earnings. This course may be helpful for Financial Analysts, as it provides a foundation in regression modeling, a technique commonly used in financial forecasting. By understanding how to build and interpret regression models, Financial Analysts can enhance their ability to make informed investment decisions and provide valuable insights to clients.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They play a critical role in the insurance and financial industries, evaluating the likelihood of events and pricing insurance policies accordingly. This course can be beneficial for aspiring Actuaries, as it provides a foundation in regression modeling, a key technique used in actuarial science. By understanding how to build and interpret regression models, Actuaries can enhance their ability to assess risk and make informed decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts design and implement mathematical models to improve decision-making in various industries. They analyze data, identify patterns, and develop solutions to optimize processes and systems. This course can provide a valuable foundation for Operations Research Analysts, as it covers regression modeling, a technique used in optimization and decision-making. By understanding how to build and interpret regression models, these professionals can enhance their ability to analyze data, identify inefficiencies, and develop effective solutions.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They work in a wide range of fields, including healthcare, finance, and education, providing insights and evidence-based decision-making. This course can be useful for aspiring Statisticians, as it provides a foundation in regression modeling, a widely used statistical technique. By understanding how to build and interpret regression models, Statisticians can enhance their ability to analyze data, draw conclusions, and communicate findings effectively.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts develop and implement mathematical models to analyze financial data and make investment decisions. They use statistical techniques, including regression modeling, to forecast market trends and assess risk. This course can be beneficial for Quantitative Analysts, as it provides a structured approach to understanding regression modeling and its application in financial analysis. By gaining practical experience in building and interpreting regression models, Quantitative Analysts can enhance their ability to make informed investment decisions.
Market Researcher
Market Researcher
Market Researchers conduct research to understand consumer behavior and market trends. They use data analysis techniques to gather insights into consumer preferences, buying habits, and market dynamics. This course may be useful for Market Researchers, as it provides a foundation in regression modeling, a technique used in market research. By understanding how to build and interpret regression models, Market Researchers can enhance their ability to analyze data, identify trends, and make informed recommendations.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. They ensure that data is accessible, reliable, and secure. This course may be helpful for Data Engineers, as it provides a practical understanding of data wrangling techniques and dimension reduction, which are essential for data preparation and management. By gaining experience in these areas, Data Engineers can enhance their ability to build and maintain efficient data pipelines.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work on various aspects of software development, including programming, testing, and deployment. While this course may not directly align with the technical skills required for Software Engineers, it can provide a foundational understanding of data analysis techniques, which can be beneficial in certain areas of software development, such as data-driven applications and data visualization.
Product Manager
Product Manager
Product Managers are responsible for the vision, development, and launch of products. They work closely with engineers, designers, and marketers to ensure that products meet customer needs and achieve business goals. While this course may not directly align with the core responsibilities of Product Managers, it can provide a basic understanding of data analysis techniques, which can be valuable for understanding user behavior and making data-informed product decisions.
Management Consultant
Management Consultant
Management Consultants advise businesses on strategy, operations, and technology. They help organizations improve their performance and achieve their objectives. While this course may not directly relate to the core responsibilities of Management Consultants, it can provide a foundational understanding of data analysis techniques, which can be valuable for analyzing business data and making data-driven recommendations.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing strategies to promote products and services. They work on various aspects of marketing, including market research, advertising, and public relations. While this course may not directly align with the core responsibilities of Marketing Managers, it can provide a foundational understanding of data analysis techniques, which can be valuable for understanding consumer behavior and evaluating marketing campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/a2b4pw/capstone
Reading list
We've selected 11 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
Capstone: Data Science Problem in Linear Algebra Framework.
This classic textbook provides a comprehensive introduction to linear algebra, covering both theoretical and practical aspects. It valuable resource for students and practitioners alike, and it is particularly useful for those who want to gain a deeper understanding of the mathematical foundations of data science.
Provides a practical introduction to linear algebra, with a focus on applications in data science. It is written in a clear and concise style, and it is packed with examples and exercises. It valuable resource for students and practitioners who want to learn how to use linear algebra to solve real-world problems.
Save
Provides a comprehensive introduction to matrix computations, with a focus on algorithms and their implementation. It valuable resource for students and practitioners who need to understand the computational aspects of linear algebra.
Provides a comprehensive introduction to numerical linear algebra, with a focus on methods for solving large-scale linear systems. It valuable resource for students and practitioners who need to understand the computational aspects of linear algebra.
Provides a rigorous and comprehensive introduction to linear algebra, with a focus on theoretical concepts. It valuable resource for students and practitioners who want to gain a deep understanding of the mathematical foundations of linear algebra.
Provides a clear and concise introduction to linear algebra, with a focus on applications in science and engineering. It valuable resource for students and practitioners who want to learn how to use linear algebra to solve real-world problems.
Provides a comprehensive introduction to linear algebra, with a focus on applications in economics and business. It valuable resource for students and practitioners who want to learn how to use linear algebra to solve real-world problems.
Save
Provides a gentle introduction to linear algebra, with a focus on making the concepts accessible to beginners. It valuable resource for students and practitioners who want to learn the basics of linear algebra without getting bogged down in technical details.
Provides a comprehensive introduction to advanced topics in linear algebra, such as matrix theory, group theory, and representation theory. It valuable resource for students and practitioners who want to gain a deeper understanding of the mathematical foundations of linear algebra.
Provides a comprehensive introduction to linear algebra and optimization, with a focus on applications in machine learning. It valuable resource for students and practitioners who want to learn how to use linear algebra and optimization to solve real-world problems in machine learning.
Provides a comprehensive introduction to linear algebra, with a focus on applications in computer graphics. It valuable resource for students and practitioners who want to learn how to use linear algebra to solve real-world problems in computer graphics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/a2b4pw/capstone
Share
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Via
Coursera
Institution
Howard University
Instructors
Dennis Davenport
MOUSSA DOUMBIA
Language
English
This course belongs to a
collection
called
Linear Algebra for Data Science Using Python. It's comprised of four courses:
- Fundamental Linear Algebra Concepts with Python
- Introduction to Linear Algebra and Python
- Capstone: Data Science Problem in Linear Algebra Framework (viewing)
- Building Regression Models with Linear Algebra
Good to know
Teaches regression modeling, which is highly relevant to roles like data analyst
Taught by Dennis Davenport and MOUSSA DOUMBIA, who are recognized experts in regression modeling
Provides hands-on experience in running regression models and interpreting results
Build a strong foundation for learners who are new to regression modeling
This course is part of a specialization, which indicates comprehensiveness and detail
Requires learners to come in with some background knowledge in statistics
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