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Sarah Haq,
Stacey McBrine,
and
Megan Smith Branch
This course is designed for business professionals that want to learn how to determine if a business issue is appropriate for a data science project and apply the data science process.
The typical student in this course will have experience in a business setting and a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.
Register for this course and see more details by visiting:
OpenCourser.com/course/ycr1tl/address
What's inside
Syllabus
Initiate a Data Science Project
Before you dive into the technical details of data science, you need to understand how data science fits within a larger business context. On the job, you'll be applying your skills to achieve one or more business goals, so it's important to keep those goals in mind all throughout the project. In this first module, you'll begin developing a data science project that can achieve these goals.
Read more
Syllabus
Initiate a Data Science Project
Before you dive into the technical details of data science, you need to understand how data science fits within a larger business context. On the job, you'll be applying your skills to achieve one or more business goals, so it's important to keep those goals in mind all throughout the project. In this first module, you'll begin developing a data science project that can achieve these goals.
Read more
Formulate a Data Science Problem
Now that you have a greater understanding of the project's goals and design principles, you can start thinking about how to approach the project using the power of data science. In this module, you'll contextualize your work as a potential solution to a data-driven problem.
Apply What You've Learned
You'll work on a project in which you'll apply your knowledge of the material in this course to practical scenarios.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers the fundamental principles of data science, making it suitable for beginners
Develops skills in initiating and formulating data science projects, which are valuable in various industries
Provides practical application opportunities through hands-on projects
Assumes a baseline understanding of data science concepts, potentially limiting accessibility for complete beginners
The course does not delve into advanced data science techniques, which may not meet the requirements of experienced practitioners
Save this course
Save Address Business Issues with Data Science to your list so you can find it easily later:
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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 Address Business Issues with Data Science with these
activities:
Follow Tutorials on Data Science Project Initiation
Show steps
Enhance understanding of data science project initiation by following guided tutorials and practical examples.
Show steps
-
Explore online resources and tutorials on data science project initiation.
-
Identify and follow reputable tutorials that provide step-by-step guidance.
-
Apply the concepts and techniques learned to develop a project initiation plan.
Practice Formulating Data Science Problems
Show steps
Practice formulating data science problems to develop the ability to define and scope data science projects effectively.
Show steps
-
Identify a business problem or opportunity that could benefit from data analysis.
-
Gather data from various sources to understand the problem context.
-
Define the specific data science problem to be solved.
-
Develop a testable hypothesis or research question.
Create a Presentation on Data Science Business Cases
Show steps
Enhance communication and presentation skills by creating and presenting on real-world applications of data science.
Show steps
-
Research and identify successful data science implementations in various industries.
-
Create a presentation that showcases the business value and impact of these case studies.
-
Practice delivering the presentation for clarity and effectiveness.
Show all three activities
Follow Tutorials on Data Science Project Initiation
Show steps
Enhance understanding of data science project initiation by following guided tutorials and practical examples.
Show steps
Show steps
Show steps
- Explore online resources and tutorials on data science project initiation.
- Identify and follow reputable tutorials that provide step-by-step guidance.
- Apply the concepts and techniques learned to develop a project initiation plan.
Practice Formulating Data Science Problems
Show steps
Practice formulating data science problems to develop the ability to define and scope data science projects effectively.
Show steps
Show steps
Show steps
- Identify a business problem or opportunity that could benefit from data analysis.
- Gather data from various sources to understand the problem context.
- Define the specific data science problem to be solved.
- Develop a testable hypothesis or research question.
Create a Presentation on Data Science Business Cases
Show steps
Enhance communication and presentation skills by creating and presenting on real-world applications of data science.
Show steps
Show steps
Show steps
- Research and identify successful data science implementations in various industries.
- Create a presentation that showcases the business value and impact of these case studies.
- Practice delivering the presentation for clarity and effectiveness.
Show all three activities
Career center
Learners who complete Address Business Issues with Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist plays a crucial role in the field of data science, utilizing their expertise in statistics, computer science, and domain knowledge to extract meaningful insights from data. This course, "Address Business Issues with Data Science," provides a solid foundation for Data Scientists by equipping them with the skills to identify business issues suitable for data science projects and apply the data science process effectively. Through hands-on projects, learners gain practical experience in formulating data science problems and applying their knowledge to real-world scenarios.
Data Analyst
Data Analyst
Data Analysts are responsible for analyzing data to uncover trends, patterns, and insights that can inform decision-making. This course aligns well with the role of a Data Analyst as it teaches how to initiate and formulate data science projects. By understanding the business context and applying the data science lifecycle, Data Analysts can effectively translate business needs into actionable data science solutions.
Business Analyst
Business Analyst
Business Analysts bridge the gap between business and technology, analyzing business processes and identifying areas for improvement. This course empowers Business Analysts with the knowledge to evaluate the suitability of data science projects for addressing business issues. By understanding the data science process and its potential benefits and challenges, Business Analysts can make informed recommendations and contribute to data-driven decision-making.
Market Researcher
Market Researcher
Market Researchers gather and analyze data to understand market trends, customer behavior, and industry dynamics. This course provides valuable insights into how data science can enhance market research practices. By learning to formulate data science problems and apply the data science process, Market Researchers can gain a competitive edge in extracting actionable insights from data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems and improve efficiency. This course complements the skillset of Operations Research Analysts by providing a foundation in data science. By learning how to initiate and formulate data science projects, Operations Research Analysts can expand their problem-solving capabilities and leverage data-driven insights to optimize operations.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a strong foundation for Quantitative Analysts by introducing the principles of data science. By understanding the data science process and its applications in finance, Quantitative Analysts can enhance their ability to identify and evaluate investment opportunities.
Data Engineer
Data Engineer
Data Engineers design and build the infrastructure and systems that support data science projects. This course provides a complementary skill set for Data Engineers by teaching how to initiate and formulate data science projects. By understanding the business context and the data science lifecycle, Data Engineers can effectively collaborate with data scientists and ensure that the necessary data infrastructure is in place.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. While this course may not directly align with the core responsibilities of a Software Engineer, it can provide valuable insights into the role of data science in modern software development. By understanding how to formulate data science problems and apply the data science process, Software Engineers can contribute to the development of data-driven software solutions.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. This course may be useful for Product Managers who are interested in incorporating data science into their product strategy. By understanding the principles of data science, Product Managers can make informed decisions about the use of data to improve product design, functionality, and user experience.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns to promote products and services. This course may be useful for Marketing Managers who want to leverage data science to enhance their marketing efforts. By understanding how to formulate data science problems and apply the data science process, Marketing Managers can gain insights into customer behavior, market trends, and campaign effectiveness.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investments. This course may be useful for Financial Analysts who want to incorporate data science into their investment analysis process. By understanding how to formulate data science problems and apply the data science process, Financial Analysts can enhance their ability to identify and evaluate investment opportunities.
Business Consultant
Business Consultant
Business Consultants provide advice and guidance to businesses on various aspects of their operations. This course may be useful for Business Consultants who want to expand their knowledge of data science and its applications in business. By understanding how to formulate data science problems and apply the data science process, Business Consultants can provide more informed recommendations to their clients.
Operations Manager
Operations Manager
Operations Managers plan and oversee the operations of an organization. This course may be useful for Operations Managers who are interested in using data science to improve operational efficiency. By understanding how to formulate data science problems and apply the data science process, Operations Managers can gain insights into operational performance and identify areas for improvement.
Human Resources Manager
Human Resources Manager
Human Resources Managers plan and oversee the human resources function of an organization. This course may be useful for Human Resources Managers who want to leverage data science to enhance their HR practices. By understanding how to formulate data science problems and apply the data science process, Human Resources Managers can gain insights into employee performance, engagement, and retention.
Sales Manager
Sales Manager
Sales Managers plan and oversee the sales function of an organization. This course may be useful for Sales Managers who want to use data science to improve sales performance. By understanding how to formulate data science problems and apply the data science process, Sales Managers can gain insights into customer behavior, market trends, and sales pipeline effectiveness.
For more career information including salaries, visit:
OpenCourser.com/course/ycr1tl/address
Reading list
We've selected 12 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
Address Business Issues with Data Science .
Provides a comprehensive overview of data science, including the different roles involved, the lifecycle of a data science project, and the benefits and challenges of data science.
Provides a comprehensive overview of business data science, including the different roles involved, the lifecycle of a data science project, and the benefits and challenges of data science.
Save
Provides a comprehensive overview of data science, including the different roles involved, the lifecycle of a data science project, and the benefits and challenges of data science.
Save
Provides a comprehensive overview of data mining, including the different roles involved, the lifecycle of a data science project, and the benefits and challenges of data science.
Provides a comprehensive overview of data science, including the different roles involved, the lifecycle of a data science project, and the benefits and challenges of data science.
Provides a comprehensive overview of statistical learning, including the different roles involved, the lifecycle of a data science project, and the benefits and challenges of data science.
Provides a practical guide to machine learning, with a focus on supervised and unsupervised learning. It covers topics such as feature engineering, model evaluation, and model selection.
Provides a practical introduction to machine learning, with a focus on business applications. It covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical guide to machine learning, with a focus on supervised and unsupervised learning. It covers topics such as feature engineering, model evaluation, and model selection.
Provides a practical guide to machine learning, with a focus on supervised and unsupervised learning. It covers topics such as feature engineering, model evaluation, and model selection.
Provides a practical guide to machine learning, with a focus on supervised and unsupervised learning. It covers topics such as feature engineering, model evaluation, and model selection.
Provides a practical guide to machine learning, with a focus on supervised and unsupervised learning. It covers topics such as feature engineering, model evaluation, and model selection.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ycr1tl/address
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Effort
2 weeks of study, 1–3 hours per week
Level
Intermediate
Via
Coursera
Institution
CertNexus
Instructors
Sarah Haq
Stacey McBrine
Megan Smith Branch
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
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हिंदी
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This course belongs to a
collection
called
CertNexus Certified Data Science Practitioner. It's comprised of five courses:
- Extract, Transform, and Load Data
- Train Machine Learning Models
- Address Business Issues with Data Science (viewing)
- Analyze Data
- Finalize a Data Science Project
Good to know
Covers the fundamental principles of data science, making it suitable for beginners
Develops skills in initiating and formulating data science projects, which are valuable in various industries
Provides practical application opportunities through hands-on projects
Assumes a baseline understanding of data science concepts, potentially limiting accessibility for complete beginners
The course does not delve into advanced data science techniques, which may not meet the requirements of experienced practitioners
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