Data Science Methodology from Coursera

If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems.

Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python.

What's inside

Syllabus

From Problem to Approach and From Requirements to Collection

In this module, you will discover what makes data science interesting, learn what a data science methodology is, and why data scientists need a data science methodology. Next, you’ll gain more in-depth knowledge of the first two data science methodology stages: Business Understanding and Analytic Approach. You’ll discover how to identify considerations and steps needed to define the data requirements for decision tree classification during the Data Requirements stage. Next, learn about the processes and techniques data scientists use to assess data content, quality, and initial insights and how data scientists manage data gaps. Round out this week with practical hands-on experience learning how to approach the Business Understanding and the Analytic Approach stage tasks and the Data Requirements and Collection stage tasks for any data science problem.

From Understanding to Preparation and From Modeling to Evaluation

In this module, you will learn what data scientists do when their tasks and goals are to understand, prepare, and clean the data. You’ll examine the purposes, characteristics, and goals of the data modeling process. You’ll also explore how to prepare a data set by handling missing, invalid, or misleading data. Then check out the hands-on labs where you can gain experience completing tasks relevant to the Data Understanding, Data Preparation, and Modeling and Evaluation stages. You’ll be able to apply the skills you learn to future data science problems.

From Deployment to Feedback and Final Evaluation

When you complete this module, you’ll be able to describe the deployment and feedback stages of the data science methodology. You’ll learn how to assess a data model’s performance, impact, and readiness. You’ll be able to identify the stakeholders who usually contribute to model refinement. You’ll also be able to explain why deployment and feedback should be an iterative process. To complete your hands-on lab experience, you’ll devise a business problem to solve using data related to email, hospitals, or credit cards. You’ll demonstrate your understanding of data science methodology by applying it to a given problem. You’ll construct responses that address each phase of the CRISP-DM based on a chosen business problem. After submitting your work, you’ll evaluate your peers’ final projects and provide constructive ideas and suggestions that fellow learners can apply right away.

Final Project and Assessment

Before completing your final project, learn how CRISP-DM data science methodology compares to John Rollins’ foundational data science methodology. Then, apply what you learned to complete a peer-graded assignment using CRISP-DM data science methodology to solve a business problem you define. You'll first take on both the client and data scientist role and describe how you would apply CRISP-DM data science methodology to solve the business problem. Then, take on the role of a data scientist and apply your knowledge of CRISP-DM data methodology stages to describe how you would solve the business problem. After you submit your assignment, you'll grade the assignment of one peer who is enrolled in this session. Let's get started!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Students interested in data science methodology and practices who want to become professional Data Scientists will benefit highly from this course

Students who want to develop their data science skills in a structured and practical way will find this course helpful

Individuals looking to sharpen their data science methodology skills and learn proven techniques for solving data science problems will find this course valuable

Learners who want to gain practical experience in data analysis and problem-solving by applying data science methodology stages will find this course beneficial

Students who want to learn how to approach a data science project systematically and effectively will find this course valuable

Individuals who want to enhance their understanding of the different stages of the CRISP-DM data science methodology and how to apply them will benefit from this course

Reviews summary

Data science methodology

IBM's Data Science Methodology course is a popular option for learners seeking a structured approach to data science problem-solving. The course is well-received for its clear explanation of the end-to-end process, emphasis on business understanding, and provision of practical examples and exercises. Many students appreciate the use of a consistent narrator and the inclusion of a single case study throughout the course, which helps them connect the concepts to a real-world scenario. However, some learners have expressed concerns about the course's length, the technical complexity of certain topics, and the quizzes, which they find to be overly detailed and sometimes unrelated to the actual content. Overall, the course is generally recommended for those with some prior knowledge in data science or programming, and students are advised to allocate sufficient time to complete the assignments and exercises.

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 Data Science Methodology with these activities:

Create a Comprehensive Data Science Resource Compilation

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Gather and organize valuable resources to support your data science journey.

Browse courses on Data Science Tools

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Identify and collect relevant resources such as online courses, tutorials, books, articles, and tools.
Organize the resources into categories or topics.
Create a central repository or document to store and share the compilation.

Read 'Data Science for Business'

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Gain insights into real-world data science applications and industry best practices.

View Data Science for Business: What You Need to... on Amazon

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Read the book thoroughly, taking notes and highlighting important concepts.
Participate in online discussions or forums to share insights and ask questions.
Summarize key takeaways and how they relate to the course material.

Data Manipulation Exercises

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Enhance your data manipulation skills by completing a set of exercises and drills.

Browse courses on Data Manipulation

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Use Python libraries like Pandas and NumPy to perform data manipulations.
Clean and preprocess real-world datasets.
Practice data wrangling techniques.
Participate in online coding challenges or hackathons focused on data manipulation.

Five other activities

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Preliminary Data Science Project

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Start a project where you will practice applying the CRISP-DM data science methodology. This is an opportunity to test your skills, reinforce your knowledge, and gain practical experience.

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Identify a problem or research question.
Gather and clean the data.
Explore and analyze the data.
Build and evaluate a model.
Deploy and monitor the model.

Kaggle Competition Participation

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Test your data science skills and learn from others by participating in a Kaggle competition.

Browse courses on Kaggle

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Identify a relevant Kaggle competition that aligns with your interests and skills.
Form a team or work individually to develop a solution.
Submit your solution and track your progress on the leaderboard.
Analyze the winning solutions and learn from the best practices of others.

Develop a Data Science Project Proposal

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Apply the data science methodology learned in the course to define a data science project and create a compelling proposal.

Browse courses on Data Science Project

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Identify a real-world problem or business need that can be addressed using data science.
Define the project scope, goals, and expected outcomes.
Describe the data sources, data collection methods, and data analysis techniques to be used.
Outline the project timeline, budget, and resource requirements.
Present your project proposal to receive feedback and suggestions.

Guided Tutorials on Advanced Data Science Techniques

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Expand your data science knowledge by following guided tutorials on specific techniques and algorithms.

Browse courses on Machine Learning Algorithms

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Identify areas where you want to deepen your understanding.
Find reputable online courses or tutorials that cover these topics.
Follow the tutorials step-by-step, implementing the techniques and algorithms.
Apply the learned techniques to your own projects or datasets.

Mentor Junior Data Scientists

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Reinforce your understanding by mentoring junior data scientists and sharing your knowledge with others.

Browse courses on Mentoring

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Identify opportunities to mentor junior data scientists through online platforms or local meetups.
Provide guidance and support on data science concepts, tools, and techniques.
Review their work, provide feedback, and encourage their growth and development.
Share your experiences and insights to help them navigate the field.

Career center

Learners who complete Data Science Methodology will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are increasingly sought after in almost every industry today. Their ability to collect, store, and analyze data to find trends could boost efficiency and save a company money. Taking this course can help you apply data science methodologies to any real-life scenario and can help you gain the skills and knowledge you need to break into this lucrative field.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst is a business professional who uses data to solve business problems. They analyze data to identify trends, patterns, and insights that can help businesses make better decisions. This course, with its in-depth look at data science methodologies, can help you become a more well-rounded Data Analyst who can tackle any data-related challenge.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers build, deploy, and maintain machine learning models. They use data science methodologies to ensure that their models are accurate and efficient. This course can help you build a solid foundation in data science methodologies, which will be essential for success as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to identify and solve business problems. They work with stakeholders to gather requirements, analyze data, and develop recommendations. This course can help you develop the data science skills you need to be a successful Business Analyst, including how to apply data science methodologies to real-world business problems.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines. They use data science methodologies to ensure that their pipelines are efficient and reliable. This course can help you build a solid foundation in data science methodologies, which will be essential for success as a Data Engineer.

See salaries and explore the career path for Data Engineer

Statistician

Statisticians collect, analyze, interpret, and present data. They use data science methodologies to ensure that their findings are accurate and reliable. This course can help you develop the data science skills you need to be a successful Statistician, including how to apply data science methodologies to real-world statistical problems.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations Research Analysts use data to improve the efficiency of business operations. They use data science methodologies to develop mathematical models that can be used to solve business problems. This course can help you develop the data science skills you need to be a successful Operations Research Analyst, including how to apply data science methodologies to real-world business problems.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

Quantitative Analysts use data to make investment decisions. They use data science methodologies to develop mathematical models that can be used to predict the performance of financial assets. This course may be useful for you as a Quantitative Analyst by helping you develop the data science skills you need to build and evaluate financial models.

See salaries and explore the career path for Quantitative Analyst

Risk Analyst

Risk Analysts use data to identify and manage risks. They use data science methodologies to develop mathematical models that can be used to assess the likelihood and impact of risks. This course may be useful for you as a Risk Analyst by helping you develop the data science skills you need to build and evaluate risk models.

See salaries and explore the career path for Risk Analyst

Data Architect

Data Architects design and build data warehouses and data lakes. They use data science methodologies to ensure that their designs are efficient and scalable. This course may be useful for you as a Data Architect by helping you develop the data science skills you need to design and build data warehouses and data lakes.

See salaries and explore the career path for Data Architect

Database Administrator

Database Administrators manage and maintain databases. They use data science methodologies to ensure that their databases are efficient and reliable. This course may be useful for you as a Database Administrator by helping you develop the data science skills you need to manage and maintain databases.

See salaries and explore the career path for Database Administrator

Software Engineer

Software Engineers design, develop, and maintain software applications. They use data science methodologies to develop software applications that are efficient and reliable. This course may be useful for you as a Software Engineer by helping you develop the data science skills you need to develop software applications that use data.

See salaries and explore the career path for Software Engineer

Data Visualization Specialist

Data Visualization Specialists design and develop data visualizations. They use data science methodologies to ensure that their visualizations are clear and effective. This course may be useful for you as a Data Visualization Specialist by helping you develop the data science skills you need to design and develop data visualizations that communicate data effectively.

See salaries and explore the career path for Data Visualization Specialist

Data Journalist

Data Journalists use data to tell stories. They use data science methodologies to ensure that their stories are accurate and reliable. This course may be useful for you as a Data Journalist by helping you develop the data science skills you need to find and analyze data, and to communicate your findings effectively.

See salaries and explore the career path for Data Journalist

Actuary

Actuaries use data to assess risk and uncertainty. They use data science methodologies to develop mathematical models that can be used to predict the likelihood and impact of future events. This course may be useful for you as an Actuary by helping you develop the data science skills you need to build and evaluate actuarial models.

See salaries and explore the career path for Actuary