Analytics for Decision Making from edX

Want to know how to avoid bad decisions with data?

Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.

These big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.

We will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?

This course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor's degree contemplating graduate study in a business field.

Opportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.

This course is part of the Business Principles and Entrepreneurial Thought XSeries.

What's inside

Learning objectives

Variability in the real world and implications for decision making
Data types and data quality with appropriate visualizations

Apply data analysis to managerial decisions, especially in start-ups
Making effective decisions from no data to big data (what should we collect and then what do we do with all this data?)

Variability in the real world and implications for decision making
Data types and data quality with appropriate visualizations
Apply data analysis to managerial decisions, especially in start-ups
Making effective decisions from no data to big data (what should we collect and then what do we do with all this data?)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops general statistical thinking that can be applied in many contexts beyond data science

Examines how statistical methods evolved and continue to be used in modern analytics

Taught by instructors experienced in business management and analytics

Applies these ideas through business-centric case studies

Could be useful for those who have not taken a prior course in statistics

May be helpful for students considering a graduate degree in business

Reviews summary

Affordable course covering basics

Learners say that this short course provides a basic introduction to analytics with helpful examples. While the content is seen as a good review, learners didn't think it was worth the price, especially since there are many free resources available online.

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 Analytics for Decision Making with these activities:

Compile Course Materials

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Establish a systematic approach to organizing and reviewing course materials, including lectures, notes, assignments, and readings, ensuring efficient access and comprehension throughout the course.

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Set up a dedicated workspace for course materials.
Develop a clear filing system for different types of materials.
Create summaries or notes for each lecture and reading.
Organize assignments by due date and difficulty.

Read "Naked Statistics: Stripping the Dread from the Data"

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Reinforce course concepts and gain a deeper understanding of statistical thinking by reading the introductory book "Naked Statistics: Stripping the Dread from the Data", which simplifies complex statistical ideas.

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Read the book, focusing on understanding the key concepts.
Highlight and annotate important passages.
Summarize the main ideas in your own words.

Form Study Groups

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Establish peer support by joining or forming study groups, engaging in collaborative discussions, and providing mutual assistance to enhance understanding of course concepts.

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Identify classmates with similar learning goals or interests.
Establish regular meeting times and a study schedule.
Take turns leading discussions and presenting summaries of key concepts.

Three other activities

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Explore Data Visualization Tools

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Supplement course learning by exploring tutorials on data visualization tools and techniques, gaining practical experience in presenting data effectively.

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Identify a data visualization tool, such as Tableau, Power BI, or Google Data Studio.
Follow tutorials or online courses to learn the basics of the chosen tool.
Practice creating different types of visualizations, such as charts, graphs, and maps.

Contribute to Open-Source Projects

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Enhance your understanding of data analysis and statistical methods by contributing to open-source projects related to data science.

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Identify open-source projects in the field of data science.
Choose a project to contribute to, based on your interests and skills.
Fork the project and make your changes.
Submit a pull request to the project.

Develop a Statistical Model

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Apply course knowledge by developing a statistical model to solve a real-world problem, demonstrating proficiency in data analysis and decision-making.

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Identify a problem or question that can be addressed with a statistical model.
Collect and analyze relevant data.
Develop a statistical model that fits the data.
Validate and interpret the model's results.
Communicate the findings and insights.

Career center

Learners who complete Analytics for Decision Making will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts work with data to produce useful information that can be used to make informed decisions. They use their knowledge of statistics and data analysis to clean, analyze, and interpret data. This course can provide Data Analysts with the foundational concepts they need to excel in their role. The course covers topics such as variability in the real world, data types, data quality, and data visualization. This knowledge will help Data Analysts make better decisions about the data they are working with and how to present it to others.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts help businesses improve their performance by analyzing data and identifying areas for improvement. They use their knowledge of statistics and data analysis to understand the current state of the business and to make recommendations for changes that could lead to better outcomes. This course can provide Business Analysts with the foundational concepts they need to excel in their role. The course covers topics such as variability in the real world, data types, data quality, and data visualization. This knowledge will help Business Analysts make better decisions about the data they are working with and how to present it to others.

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Operations Research Analyst

Operations Research Analysts use their knowledge of mathematics and statistics to solve complex problems in business and industry. They develop and implement mathematical models to improve efficiency, reduce costs, and increase profits. This course can provide Operations Research Analysts with the foundational concepts they need to excel in their role. The course covers topics such as variability in the real world, data types, data quality, and data visualization. This knowledge will help Operations Research Analysts make better decisions about the data they are working with and how to present it to others.

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Marketing Analyst

Marketing Analysts use their knowledge of statistics and data analysis to understand customer behavior and to develop and implement marketing campaigns. They use data to track the effectiveness of marketing campaigns and to make recommendations for changes that could lead to better results. This course can provide Marketing Analysts with the foundational concepts they need to excel in their role. The course covers topics such as variability in the real world, data types, data quality, and data visualization. This knowledge will help Marketing Analysts make better decisions about the data they are working with and how to present it to others.

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Financial Analyst

Financial Analysts use their knowledge of mathematics and statistics to analyze financial data and to make recommendations for investment decisions. They use data to identify trends and patterns in the financial markets and to make predictions about future performance. This course can provide Financial Analysts with the foundational concepts they need to excel in their role. The course covers topics such as variability in the real world, data types, data quality, and data visualization. This knowledge will help Financial Analysts make better decisions about the data they are working with and how to present it to others.

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Statistician

Statisticians use their knowledge of mathematics and statistics to collect, analyze, and interpret data. They work in a variety of fields, including research, government, and industry. This course can provide Statisticians with the foundational concepts they need to excel in their role. The course covers topics such as variability in the real world, data types, data quality, and data visualization. This knowledge will help Statisticians make better decisions about the data they are working with and how to present it to others.

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Data Scientist

Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They use data to develop predictive models, identify trends, and solve business problems. This course may provide Data Scientists with some of the core concepts they need to excel in their role. However, it is important to note that Data Scientists typically require a master's degree or PhD in a field such as mathematics, statistics, or computer science.

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Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models to solve complex problems in business and industry. They use their knowledge of mathematics, statistics, and computer science to develop models that can learn from data and make predictions. This course may provide Machine Learning Engineers with some of the core concepts they need to excel in their role. However, it is important to note that Machine Learning Engineers typically require a master's degree or PhD in a field such as mathematics, statistics, or computer science.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use their knowledge of computer science to develop software that meets the needs of users. This course may provide Software Engineers with some of the core concepts they need to excel in their role. However, it is important to note that Software Engineers typically require a bachelor's degree in computer science or a related field.

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Web Developer

Web Developers design and develop websites and web applications. They use their knowledge of HTML, CSS, and JavaScript to create websites that are both visually appealing and functional. This course may provide Web Developers with some of the core concepts they need to excel in their role. However, it is important to note that Web Developers typically require a bachelor's degree in computer science or a related field.

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Database Administrator

Database Administrators design, implement, and maintain databases. They use their knowledge of database management systems to ensure that databases are reliable, efficient, and secure. This course may provide Database Administrators with some of the core concepts they need to excel in their role. However, it is important to note that Database Administrators typically require a bachelor's degree in computer science or a related field.

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IT Manager

IT Managers plan, implement, and manage IT systems. They use their knowledge of IT to ensure that IT systems meet the needs of the organization. This course may provide IT Managers with some of the core concepts they need to excel in their role. However, it is important to note that IT Managers typically require a bachelor's degree in computer science or a related field.

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Project Manager

Project Managers plan, execute, and control projects. They use their knowledge of project management to ensure that projects are completed on time, within budget, and to the required quality. This course may provide Project Managers with some of the core concepts they need to excel in their role. However, it is important to note that Project Managers typically require a bachelor's degree in business administration or a related field.

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Business Consultant

Business Consultants help businesses improve their performance by providing advice and guidance. They use their knowledge of business to identify areas for improvement and to develop and implement solutions. This course may provide Business Consultants with some of the core concepts they need to excel in their role. However, it is important to note that Business Consultants typically require a bachelor's degree in business administration or a related field.

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Entrepreneur

Entrepreneurs start and run their own businesses. They use their knowledge of business to identify opportunities and to develop and implement new products and services. This course may provide Entrepreneurs with some of the core concepts they need to excel in their role. However, it is important to note that Entrepreneurs typically require a bachelor's degree in business administration or a related field.

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