We may earn an affiliate commission when you visit our partners.

Capstone

Data Science Problem in Linear Algebra Framework

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!

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

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.

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

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

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

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 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Market Researcher

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Management Consultant

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.

See salaries and explore the career path for Marketing Manager

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.

Linear Algebra for Everyone

Save

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.

Capstone

Data Science Problem in Linear Algebra Framework

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

Share

Similar courses