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Alex Aklson
and
Polong Lin
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/msshzf/data
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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.
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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.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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
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Reviews summary
Essential introduction to data science methodology
According to learners, this course provides a clear and structured introduction to the data science process, primarily focusing on the CRISP-DM methodology. Many found the content helpful for understanding the step-by-step approach to solving data science problems, making it particularly valuable for beginners or those looking for a conceptual overview. The hands-on labs using Python and Jupyter Notebooks are frequently mentioned as a positive aspect, offering practical application of the concepts. However, some reviewers noted that the course is more theoretical than technical and might be too basic for experienced practitioners seeking in-depth coding or statistical knowledge. The peer-graded assignments were also occasionally cited as inconsistent, a common challenge in MOOCs. Overall, it is considered a strong foundation in data science methodology.
Labs help apply concepts using Python and Jupyter.
"The hands-on labs in Jupyter Notebooks using Python were useful for practicing the steps taught."
"I appreciated the practical exercises that let me apply the methodology to real data."
"Working through the labs helped solidify my understanding of the process."
"The exercises are relevant and give a good feel for the practical application."
Well-suited for those new to data science concepts.
"As someone new to data science, this course was a perfect starting point to understand the workflow."
"It simplifies complex ideas and makes them accessible for beginners."
"If you want an introduction to how data science projects work, this is a good fit."
"It doesn't assume much prior knowledge about the process itself."
Provides a clear framework for the data science process.
"This course provided a great overview of data science methodology. It is structured step-by-step. It was clear."
"I really liked learning about the CRISP-DM methodology. It helps frame how to approach problems."
"The way the course breaks down the data science lifecycle is very helpful for beginners to grasp."
"It gives you a structured way of thinking about data problems from start to finish."
Variability in feedback quality for assignments.
"Peer grading for the final project was inconsistent; some feedback was great, others were not helpful."
"Reliability of peer reviews can be a challenge in this format."
"Wish there was instructor grading for the final project."
"My grade seemed to depend heavily on who reviewed my work."
May be too simple for those with prior experience.
"If you've worked on data science projects before, much of this might be common sense."
"Found the content quite basic compared to what I already knew about project management."
"Intermediate learners might find it doesn't go deep enough on any single topic."
"Better suited for someone completely new than someone looking to fill gaps."
Focuses on process, not advanced technical skills.
"The course is heavy on theory and methodology, but lacks deep dives into coding or statistics."
"Don't expect to learn advanced Python or machine learning algorithms here; it's about the 'how' and 'why' of the steps."
"Could use more technical detail or more challenging coding exercises."
"It's a high-level overview, not a deep technical course."
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
Show steps
Gather and organize valuable resources to support your data science journey.
Browse courses on
Data Science Tools
Show steps
-
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'
Show steps
Gain insights into real-world data science applications and industry best practices.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
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
Show steps
Enhance your data manipulation skills by completing a set of exercises and drills.
Browse courses on
Data Manipulation
Show steps
-
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
Expand to see all activities and additional details
Show all eight activities
Preliminary Data Science Project
Show steps
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.
Show steps
-
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
Show steps
Test your data science skills and learn from others by participating in a Kaggle competition.
Browse courses on
Kaggle
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
Expand your data science knowledge by following guided tutorials on specific techniques and algorithms.
Browse courses on
Machine Learning Algorithms
Show steps
-
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
Show steps
Reinforce your understanding by mentoring junior data scientists and sharing your knowledge with others.
Browse courses on
Mentoring
Show steps
-
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.
Create a Comprehensive Data Science Resource Compilation
Show steps
Gather and organize valuable resources to support your data science journey.
Browse courses on
Data Science Tools
Show steps
Show steps
Show steps
- 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'
Show steps
Gain insights into real-world data science applications and industry best practices.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- 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
Show steps
Enhance your data manipulation skills by completing a set of exercises and drills.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all eight activities
Preliminary Data Science Project
Show steps
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.
Show steps
Show steps
Show steps
- 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
Show steps
Test your data science skills and learn from others by participating in a Kaggle competition.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Expand your data science knowledge by following guided tutorials on specific techniques and algorithms.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- 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
Show steps
Reinforce your understanding by mentoring junior data scientists and sharing your knowledge with others.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- 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 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.
Data Analyst
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.
Machine Learning Engineer
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.
Business Analyst
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.
Data Engineer
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.
Statistician
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.
Operations Research Analyst
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.
Quantitative 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.
Risk 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.
Data Architect
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.
Database Administrator
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.
Software Engineer
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.
Data Visualization Specialist
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.
Data Journalist
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.
Actuary
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.
For more career information including salaries, visit:
OpenCourser.com/course/msshzf/data
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
Data Science Methodology.
Provides a comprehensive overview of data science, including the underlying concepts, techniques, and applications. It valuable resource for anyone who wants to learn more about data science.
Provides a practical introduction to machine learning, with a focus on data science applications. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a hands-on introduction to data science, using Python. It covers the entire data science pipeline, from data acquisition and cleaning to model building and evaluation.
Provides a comprehensive introduction to Python for data science. It covers all the essential topics, including data structures, data manipulation, and data visualization.
Save
Provides a practical introduction to data science, using R. It covers a wide range of topics, including data acquisition and cleaning, data analysis, and machine learning.
Provides a practical introduction to machine learning, using Python. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including regression, classification, and clustering.
Provides a comprehensive overview of data science methodology. It covers the entire data science pipeline, from problem definition to model deployment.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including regression, classification, and clustering.
Provides a gentle introduction to data science. It covers a wide range of topics, including data acquisition and cleaning, data analysis, and machine learning.
Provides a comprehensive introduction to data science. It covers a wide range of topics, including data acquisition and cleaning, data analysis, and machine learning.
Save
Provides a practical introduction to data science. It covers a wide range of topics, including data acquisition and cleaning, data analysis, and machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/msshzf/data
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Rating
4.6
Effort
Approx. 6 hours
Level
Introductory
Via
Coursera
Institution
IBM
Instructors
Alex Aklson
Polong Lin
Language
English
Transcripts
Español
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Português
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آذربایجان دیلی
This course belongs to a
collection
called
Introduction to Data Science. It's comprised of four courses:
- What is Data Science?
- Databases and SQL for Data Science with Python
- Tools for Data Science
- Data Science Methodology (viewing)
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