The Data Science Method from edX

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

Despite and influx in computing power and access to data over the last couple of decades, our ability to use data within the decision-making process is either lost or not maximized all too often. We do not have a strong grasp of the questions asked and how to apply the data correctly to resolve the issues at hand.

The purpose of this course is to share the methods, models and practices that can be applied within data science, to ensure that the data used in problem-solving is relevant and properly manipulated to address business and real-world challenges.

You will learn how to identify a problem, collect and analyze data, build a model, and understand the feedback after model deployment.

Advancing your ability to manage, decipher and analyze new and big data is vital to working in data science. By the end of this course, you will have a better understanding of the various stages and requirements of the data science method and be able to apply it to your own work.

What you'll learn

Explain why a methodology for approaching data science problems is needed
List the major steps involved in tackling a data science problem
Determine appropriate data sources for your data science analysis methodology
Describe the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study
Demonstrate your understanding of the data science methodology by applying it to a problem that you define

What's inside

Learning objectives

Explain why a methodology for approaching data science problems is needed
List the major steps involved in tackling a data science problem
Determine appropriate data sources for your data science analysis methodology

Describe the six stages in the cross-industry process for data mining (crisp-dm) methodology to analyze a case study
Demonstrate your understanding of the data science methodology by applying it to a problem that you define

Explain why a methodology for approaching data science problems is needed
List the major steps involved in tackling a data science problem
Determine appropriate data sources for your data science analysis methodology
Describe the six stages in the cross-industry process for data mining (crisp-dm) methodology to analyze a case study
Demonstrate your understanding of the data science methodology by applying it to a problem that you define

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores an industry standard methodology, CRISP-DM, for approaching data science problems as seen in business and real world settings

Led by instructors Alex Aklson, you can learn about data collection, analysis, model-building, and post-deployment feedback

Demonstrates the hands-on implementation of data science methodology in problem-solving

Ensures data utilization is optimized by teaching learners the correct approach to problem-solving using data

Requires learners to have a basic understanding of data science concepts and terminology

Does not provide instruction on data science software and tools, which may require additional learning

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 The Data Science Method with these activities:

Brush Up on Linear Algebra

Show steps

Refreshing your knowledge of linear algebra will help you better understand the mathematical foundations of data science.

Browse courses on Linear Algebra

Show steps

Review your notes or textbook from a previous linear algebra course
Complete practice problems to test your understanding
Take an online refresher course or tutorial

Read "Data Science for Business"

Show steps

This book provides a comprehensive overview of the data science process, from problem definition to model deployment.

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

Show steps

Read the book's introduction and first three chapters
Read the remaining chapters, focusing on the sections that are most relevant to your interests
Complete the practice exercises at the end of each chapter

Follow Tutorials on Data Science Techniques

Show steps

Following tutorials on data science techniques will help you learn new skills and expand your knowledge.

Show steps

Follow the tutorials step-by-step
Find tutorials on topics that you are interested in
Complete the exercises and quizzes that are included in the tutorials

Five other activities

Expand to see all activities and additional details

Show all eight activities

Participate in a Data Science Study Group

Show steps

Participating in a data science study group will provide you with an opportunity to learn from and collaborate with other students.

Show steps

Find a study group or start your own
Meet regularly to discuss course material, work on projects, and prepare for exams

Practice Data Cleaning and Manipulation Techniques

Show steps

Practicing data cleaning and manipulation techniques will help you become more proficient in working with data.

Browse courses on Data Cleaning

Show steps

Find a dataset and load it into a data analysis tool
Identify and correct data errors
Transform the data into a format that is suitable for analysis

Develop a Machine Learning Model

Show steps

Creating a machine learning model will provide you with hands-on experience in applying the data science methodology to a real-world problem.

Browse courses on Machine Learning Models

Show steps

Identify a dataset and define the problem statement
Preprocess and explore the data
Select and train a machine learning model
Evaluate the model's performance
Deploy the model and monitor its performance

Participate in Data Science Competitions

Show steps

Participating in data science competitions will help you test your skills, learn from others, and stay up-to-date on the latest trends.

Browse courses on Kaggle

Show steps

Find a competition that interests you and fits your skill level
Team up with other students to work on the competition
Submit your solution and track your progress on the leaderboard

Develop a Data Science Portfolio

Show steps

Developing a data science portfolio will provide you with a tangible way to showcase your skills and experience to potential employers.

Browse courses on Portfolio Development

Show steps

Identify projects that you have worked on that are relevant to data science
Create a portfolio website or GitHub repository to showcase your projects
Write up project descriptions and include screenshots or demos
Seek feedback on your portfolio from peers or mentors

Career center

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

Chief Data Officer

A Chief Data Officer is responsible for the overall data strategy of an organization. They work with senior executives to develop and implement strategies for using data to improve the organization's performance. The Data Science Method may be useful to a Chief Data Officer by providing a structured approach to understanding the data science process and how to best support it.

See salaries and explore the career path for Chief Data Officer

Machine Learning Engineer

Machine Learning Engineers collaborate with data scientists to build, deploy, and maintain machine learning models. They create software that solves specific problems by using machine learning algorithms. The Data Science Method may be useful to a Machine Learning Engineer to provide a clear understanding of the process of machine learning and how to successfully implement it within a system.

See salaries and explore the career path for Machine Learning Engineer

Data Architect

A Data Architect designs and builds the infrastructure used to store, process, and analyze data for an organization. They work with data scientists and other stakeholders to understand the data needs of the business and design and implement solutions to meet those needs. The Data Science Method may be useful to a Data Architect by providing a structured approach to understanding the data science process and how to best support it.

See salaries and explore the career path for Data Architect

Data Analyst

Data Analysts collect, analyze, interpret and present data in a way that helps organizations make informed decisions. They may work with structured or unstructured data, using a variety of techniques and tools to draw meaningful insights from the data to answer business questions and solve problems. The Data Science Method may be useful to a Data Analyst by providing a structured approach to gathering, analyzing, and interpreting data more effectively.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer designs, builds, and maintains the infrastructure used to store, process, and analyze data for an organization. They work with data scientists and other stakeholders to understand the data needs of the business and design and implement solutions to meet those needs. The Data Science Method may be useful to a Data Engineer by providing a structured approach to understanding the data science process and how to best support it.

See salaries and explore the career path for Data Engineer

Statistician

A Statistician collects, analyzes, and interprets data to draw conclusions about a population. They may work in a variety of fields, such as healthcare, finance, and education. The Data Science Method may be useful to a Statistician by providing a structured approach to understanding and analyzing data and how to use it to draw informed conclusions.

See salaries and explore the career path for Statistician

Data Scientist

A Data Scientist applies scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data in various forms, both within an organization's internal data or from external sources. The Data Science Method may be useful to a Data Scientist by helping to organize and understand the steps needed to achieve this goal.

See salaries and explore the career path for Data Scientist

Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical techniques to solve complex problems in a variety of industries, such as manufacturing, transportation, and healthcare. They may use data to build models and simulations to analyze and improve systems and processes. The Data Science Method may be useful to an Operations Research Analyst by providing a structured approach to understanding and analyzing complex problems and how to use data to develop solutions.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

A Financial Analyst analyzes financial data to make recommendations on investments and other financial decisions. They may work for investment banks, hedge funds, or other financial institutions. The Data Science Method may be useful to a Financial Analyst by providing a structured approach to understanding and analyzing financial data and how to use it to make informed decisions.

See salaries and explore the career path for Financial Analyst

Product Manager

A Product Manager is responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market and ensure that they meet the needs of customers. The Data Science Method may be useful to a Product Manager by providing a structured approach to understanding and analyzing customer needs and how to use data to develop products that meet those needs.

See salaries and explore the career path for Product Manager

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. They may work for investment banks, hedge funds, or other financial institutions. The Data Science Method may be useful to a Quantitative Analyst by providing a structured approach to understanding and analyzing financial data and how to use it to make informed decisions.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

A Market Researcher conducts research to understand consumer behavior and market trends. They may use data to analyze market trends, identify new opportunities, and develop marketing campaigns. The Data Science Method may be useful to a Market Researcher by providing a structured approach to understanding and analyzing market data and how to use it to make informed decisions.

See salaries and explore the career path for Market Researcher

Business Analyst

A Business Analyst works with stakeholders to understand their business needs and then analyzes and documents those needs. They may also design and implement solutions to meet those needs. The Data Science Method may be useful to a Business Analyst by providing a structured approach to understanding and analyzing business needs and how to use data to meet those needs.

See salaries and explore the career path for Business Analyst

Management Consultant

A Management Consultant provides advice and solutions to businesses on how to improve their performance. They may work in a variety of areas, such as strategy, operations, finance, and human resources. The Data Science Method may be useful to a Management Consultant by providing a structured approach to understanding and analyzing business problems and how to use data to develop solutions.

See salaries and explore the career path for Management Consultant