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Data Analysis for Business

Foundations

Renata Trinca Colonel

The course provides an overview of data analysis for business: an introduction and review of methods to describe data using tables, charts and summary measures. The course is designed to respond to the needs of a non-specialist audience and also for participants with “non-scientific” backgrounds (humanities and other classical disciplines), who have not been extensively exposed to quantitative methods and basic calculation procedures or, more generally, for people who do not feel comfortable with basic statistical techniques. At the end of this course the participants will be able to manage the most common basic data analysis techniques: descriptive and data visualization to properly analyze categorical and numerical business variables.

Read more

The course provides an overview of data analysis for business: an introduction and review of methods to describe data using tables, charts and summary measures. The course is designed to respond to the needs of a non-specialist audience and also for participants with “non-scientific” backgrounds (humanities and other classical disciplines), who have not been extensively exposed to quantitative methods and basic calculation procedures or, more generally, for people who do not feel comfortable with basic statistical techniques. At the end of this course the participants will be able to manage the most common basic data analysis techniques: descriptive and data visualization to properly analyze categorical and numerical business variables.

What you'll learn

The Program aims to provide basic knowledge of data analysis. The main topics covered in the Program are:

  • Introduction and Data Collection
  • Data Visualization (describing data using tables and graphs)
  • Summary Statistics (describing data using numerical measures)
  • Reporting
  • Random Variables and Normal Probability Distribution
  • Linear Correlation and Regression Line
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What's inside

Learning objectives

  • Introduction and data collection
  • Data visualization (describing data using tables and graphs)
  • Summary statistics (describing data using numerical measures)
  • Reporting
  • Random variables and normal probability distribution
  • Linear correlation and regression line

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Designed for non-specialists and those without extensive quantitative or statistical backgrounds
Provides basic knowledge of data analysis, covering essential topics such as data visualization, summary statistics, and probability distributions
Aims to develop skills in managing common data analysis techniques, enabling learners to analyze business variables effectively
May not be suitable for learners seeking advanced or specialized data analysis knowledge
Does not include hands-on labs or interactive materials

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Reviews summary

Data analysis foundations for business professionals

According to students, this course is a highly accessible and effective introduction to data analysis for business, particularly beneficial for those from non-scientific or non-quantitative backgrounds. Learners frequently praise the instructor's ability to deliver clear and digestible explanations of complex topics like summary statistics and regression. The content is considered highly relevant for business professionals seeking to understand and apply data, with a strong focus on interpreting results and real-world scenarios. While it provides a solid foundational understanding, some learners with prior exposure found parts too basic, and a few desired more hands-on practice with specific software tools. Overall, it successfully demystifies data for business decision-making.
Highlighted modules include data visualization and summary statistics.
"The data visualization modules were particularly useful, especially learning to describe data using tables and graphs effectively."
"The 'Summary Statistics' module was particularly eye-opening, showing how simple numbers can tell a powerful story."
"I felt the linear correlation and regression part was a bit rushed compared to other modules, and I had to do some external reading."
Focuses on applying data analysis in real-world business contexts.
"I particularly appreciated the focus on interpreting results rather than just calculations."
"Very relevant for making informed business decisions."
"The focus on 'Data Analysis for Business' is evident throughout, making every lesson feel relevant."
"The fundamental concepts are well-taught and directly applicable to business problems."
Excellent for those with no prior data or statistical background.
"This course was exactly what I needed to grasp the basics of data analysis for my business role."
"As someone from a non-scientific background, I was apprehensive... This course really breaks down concepts..."
"Perfect for non-technical managers! I finally understand what my data analysts are talking about."
"It perfectly aligns with its promise of being for non-specialists."
Makes complex data concepts easy to understand for beginners.
"The instructor explains complex ideas like regression in a very approachable way."
"This course really breaks down concepts like summary statistics and normal probability distributions into digestible chunks."
"The explanations are crystal clear, and the pace is perfect for someone like me who hasn't touched statistics in years."
"It demystified data for me... the effort made to keep it accessible."
Pacing can be slow for some, fast for others with zero background.
"I found the pacing a little slow at times, as I had some prior exposure to statistics, but for a true beginner, it's probably perfect."
"Some of the initial modules felt too basic for even a relative beginner. I was hoping for a bit more challenge."
"Found some parts quite difficult, especially the statistical concepts like random variables... for someone with zero math background, it still felt like a jump."
Less emphasis on direct software application or practical exercises.
"More hands-on tools or specific software walkthroughs could enhance it."
"I felt a strong reliance on theoretical explanations without enough practical, hands-on exercises using actual tools like Excel or Python."
"Good for conceptual understanding, less for practical skill building."

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 Analysis for Business: Foundations with these activities:
Review statistical concepts and principles
Strengthens foundational knowledge of statistical concepts, enhancing understanding of data analysis.
Browse courses on Summary Statistics
Show steps
  • Refer to textbooks or online resources
  • Review key concepts and principles
  • Solve practice problems or answer review questions
Consolidate and review course materials
Enhances retention and recall of key concepts by organizing and reviewing course materials.
Show steps
  • Gather all course notes, assignments, and readings.
  • Organize and categorize materials based on topics.
  • Review the materials regularly and identify areas for improvement.
Seek mentorship from experienced data analysts
Provides access to guidance, support, and insights from industry professionals.
Show steps
  • Identify potential mentors through professional networks or online platforms.
  • Reach out to mentors and express interest in their guidance.
  • Schedule regular meetings or connect through online platforms.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Follow video tutorials on data analysis with real-world examples
Builds a practical understanding of data analysis techniques used in real-world scenarios.
Browse courses on Data Analysis
Show steps
  • Find relevant video tutorials
  • Watch and follow along with the tutorials
  • Complete any exercises or activities provided in the tutorials
  • Apply the techniques learned to analyze and interpret data
Review 'The Data Analysis Handbook' by J. Scott Armstrong and Kesten C. Green
Provides a comprehensive overview of data analysis techniques and methodologies commonly used in business.
View The Scientific Method on Amazon
Show steps
  • Read through the chapters that align with course topics.
  • Take notes and highlight key concepts.
  • Complete the exercises at the end of each chapter.
Solve practice problems on data analysis techniques
Enhances understanding and retention of data analysis concepts through repetitive practice.
Browse courses on Descriptive Statistics
Show steps
  • Identify relevant practice problems
  • Solve problems using appropriate techniques
  • Review solutions and identify areas for improvement
Create a collection of data visualization for a business scenario
Solidifies an understanding of representing and summarizing business data using charts and tables.
Browse courses on Data Visualization
Show steps
  • Select a business scenario and identify key variables
  • Choose appropriate data visualization techniques
  • Create data visualizations using software or online tools
  • Interpret and analyze the visualizations
  • Present the visualizations and insights to stakeholders
Participate in peer review sessions for data analysis projects
Provides opportunities to learn from peers, receive feedback, and enhance analytical skills.
Browse courses on Peer Review
Show steps
  • Form a study group or join an online peer review platform.
  • Share progress on data analysis projects with the group.
  • Provide constructive feedback to peers on their projects.
Solve data analysis practice problems
Reinforces understanding of data analysis concepts and improves problem-solving skills.
Browse courses on Data Interpretation
Show steps
  • Find practice problems online or in textbooks.
  • Solve the problems step-by-step.
  • Check your answers and identify areas for improvement.
Follow guided tutorials on advanced data analysis techniques
Expands knowledge of data analysis beyond the scope of the course, providing a foundation for further exploration.
Browse courses on Machine Learning
Show steps
  • Identify areas where you want to enhance your skills.
  • Find reputable online tutorials or courses.
  • Follow the tutorials step-by-step and complete the exercises.
Create data visualizations to present business insights
Develops skills in communicating data effectively and visually to stakeholders.
Browse courses on Data Visualization
Show steps
  • Gather and analyze data relevant to a business scenario.
  • Choose appropriate visualization techniques to present the data.
  • Create the visualizations using a data visualization tool.
  • Present the insights derived from the visualizations.
Develop a data analysis dashboard to track business performance
Applies data analysis skills to a practical business scenario, reinforcing understanding and showcasing capabilities.
Browse courses on Data Visualization
Show steps
  • Define the key performance indicators (KPIs) to be tracked.
  • Gather data from relevant sources.
  • Design and develop the dashboard using a data visualization tool.
  • Set up automated data refresh to ensure up-to-date information.
  • Share the dashboard with stakeholders and present insights.

Career center

Learners who complete Data Analysis for Business: Foundations will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts apply analytical skills to transform raw data into meaningful insights. They use statistical techniques and data visualization tools to communicate their findings to stakeholders across an organization. This course offers a solid foundation in data analysis techniques, including data visualization, summary statistics, and linear regression, making it a valuable asset for aspiring Data Analysts. By equipping learners with these essential skills, the course empowers them to effectively analyze and interpret data, contributing to better decision-making and business outcomes.
See salaries and explore the career path for Data Analyst
Business Analyst
Business Analysts bridge the gap between business and technology by analyzing business processes and identifying areas for improvement. They use data analysis techniques to evaluate current operations and recommend solutions that enhance efficiency and effectiveness. This course provides Business Analysts with the foundational knowledge they need to analyze data, understand business requirements, and develop data-driven recommendations. It covers topics such as data visualization, summary statistics, and reporting, which are essential for success in this role.
See salaries and explore the career path for Business Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve complex business problems and improve operational efficiency. They analyze data to identify patterns, develop models, and make recommendations that optimize processes and resource allocation. This course provides a strong foundation in data analysis techniques, including probability, statistics, and linear programming, which are essential for success in this role. It helps aspiring Operations Research Analysts build the skills they need to analyze data, develop models, and drive data-driven decision-making.
See salaries and explore the career path for Operations Research Analyst
Financial Analyst
Financial Analysts assess the financial performance and health of companies or investments. They use data analysis techniques to evaluate financial statements, analyze market trends, and make recommendations on investment strategies. This course provides a strong foundation in data analysis techniques, including financial ratios, probability, and statistics, which are essential for success in this role. It helps aspiring Financial Analysts develop the skills they need to analyze financial data, understand market dynamics, and make informed investment decisions.
See salaries and explore the career path for Financial Analyst
Market Researcher
Market Researchers analyze market trends, customer behavior, and industry dynamics to provide insights that inform marketing strategies and product development. They use data analysis techniques to collect, analyze, and interpret data from surveys, experiments, and other sources. This course provides a strong foundation in data analysis techniques, including data visualization, summary statistics, and regression analysis, which are essential for success in this role. It helps aspiring Market Researchers develop the skills they need to analyze market data, understand consumer behavior, and make data-driven recommendations.
See salaries and explore the career path for Market Researcher
Consultant
Consultants provide expert guidance and advice to businesses on a wide range of issues, including strategy, operations, and technology. They use data analysis techniques to analyze business processes, identify areas for improvement, and develop recommendations that enhance performance. This course provides a strong foundation in data analysis techniques, including data visualization, summary statistics, and regression analysis, which are essential for success in this role. It helps aspiring Consultants develop the skills they need to analyze data, understand business challenges, and make data-driven recommendations.
See salaries and explore the career path for Consultant
Data Scientist
Data Scientists use advanced statistical and machine learning techniques to extract insights from data. They build predictive models, develop algorithms, and apply data analysis techniques to solve complex business problems. While this course may not directly cover advanced topics such as machine learning, it provides a solid foundation in data analysis techniques, including data visualization, summary statistics, and probability, which are essential for aspiring Data Scientists. It helps build a foundation that can be further developed through specialized training or coursework in data science.
See salaries and explore the career path for Data Scientist
Statistician
Statisticians collect, analyze, and interpret data to draw meaningful conclusions. They use statistical techniques to design experiments, analyze data, and develop models that provide insights into complex phenomena. This course provides a strong foundation in data analysis techniques, including probability, statistics, and regression analysis, which are essential for success in this role. It helps aspiring Statisticians develop the skills they need to analyze data, draw inferences, and communicate their findings.
See salaries and explore the career path for Statistician
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They develop trading strategies, evaluate risk, and manage portfolios. While this course may not directly cover advanced topics in quantitative finance, it provides a solid foundation in data analysis techniques, including probability, statistics, and regression analysis, which are essential for aspiring Quantitative Analysts. It helps build a foundation that can be further developed through specialized training or coursework in quantitative finance.
See salaries and explore the career path for Quantitative Analyst
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They develop models to predict future events, such as mortality rates and insurance claims, and use these models to make informed decisions about risk management and financial planning. This course provides a strong foundation in data analysis techniques, including probability, statistics, and regression analysis, which are essential for success in this role. It helps aspiring Actuaries develop the skills they need to analyze data, assess risk, and make data-driven recommendations.
See salaries and explore the career path for Actuary
Software Engineer
Software Engineers design, develop, and maintain software applications. While this course may not directly cover software engineering concepts, it provides a strong foundation in data analysis techniques, including data visualization, summary statistics, and regression analysis, which are increasingly used in software development for tasks such as data-driven decision-making and quality assurance. It helps aspiring Software Engineers develop the skills they need to analyze data, understand user behavior, and improve software products.
See salaries and explore the career path for Software Engineer
Product Manager
Product Managers are responsible for the development and success of products. They use data analysis techniques to understand customer needs, define product requirements, and make data-driven decisions throughout the product lifecycle. This course provides a strong foundation in data analysis techniques, including data visualization, summary statistics, and regression analysis, which are essential for success in this role. It helps aspiring Product Managers develop the skills they need to analyze data, understand customer feedback, and make data-driven decisions.
See salaries and explore the career path for Product Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products or services. They use data analysis techniques to track campaign performance, measure customer engagement, and make data-driven decisions to optimize marketing strategies. This course provides a strong foundation in data analysis techniques, including data visualization, summary statistics, and regression analysis, which are essential for success in this role. It helps aspiring Marketing Managers develop the skills they need to analyze data, understand customer behavior, and make data-driven marketing decisions.
See salaries and explore the career path for Marketing Manager
Financial Planner
Financial Planners help individuals and families manage their finances to achieve their financial goals. They use data analysis techniques to assess financial situations, develop financial plans, and make investment recommendations. This course provides a strong foundation in data analysis techniques, including financial ratios, probability, and statistics, which are essential for success in this role. It helps aspiring Financial Planners develop the skills they need to analyze financial data, understand investment strategies, and make data-driven financial recommendations.
See salaries and explore the career path for Financial Planner
Data Visualization Specialist
Data Visualization Specialists design and develop data visualizations to communicate insights from data. They use data analysis techniques to transform raw data into visually appealing and informative representations. This course provides a strong foundation in data visualization techniques, including data visualization tools and best practices, which are essential for success in this role. It helps aspiring Data Visualization Specialists develop the skills they need to analyze data, create effective visualizations, and communicate insights effectively.
See salaries and explore the career path for Data Visualization Specialist

Reading list

We've selected seven 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 Analysis for Business: Foundations.
Cover image
Data Visualisation
Save
Offers more in-depth information on data visualization and complements this course's coverage of the topic. It useful reference book for data analysts and practitioners.
Data Visualisation: A Handbook for Data Driven...
Paperback
$$
Data Visualisation: A Handbook for Data Driven...
Paperback
$$$
Data Visualisation: A Handbook for Data Driven...
Paperback
$$$
Data Visualisation: A Handbook for Data Driven...
Kindle Edition
$$$
Data Visualisation: A Handbook for Data Driven...
Kindle Edition
$$$$
Cover image
Naked Statistics
Save
Good resource for those who lack a statistics background and need introductory materials on foundational statistical techniques. It will help build a foundational knowledge for this course.
Naked Statistics: Stripping the Dread from the Data
Paperback
$$
NEW-Naked Statistics
Paperback
$$
Naked Statistics: Stripping the Dread from the Data
Kindle Edition
$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Covers machine learning concepts and techniques using Python libraries such as Scikit-Learn, Keras, and TensorFlow. While it is not directly related to the course content, it can be useful for those interested in applying machine learning algorithms in real-world projects.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Introductory Econometrics
Save
Covers econometrics methods and models. While it is not directly related to data analysis, it can be useful as a reference for the 'Linear Correlation and Regression Line' topic in the course.
Introductory Econometrics
Paperback
Check
Introductory Econometrics
Kindle Edition
Check
Cover image
Data Analytics & Visualization All-in-One For...
Save
Offers a beginner-friendly introduction to machine learning concepts and algorithms. While it is not directly related to data analysis, it can be useful for those interested in exploring machine learning in more detail.
Data Analytics & Visualization All-in-One For...
Paperback
$$
Data Analytics & Visualization All-in-One For...
Kindle Edition
$$
Cover image
SQL Queries for Mere Mortals
Save
While this book is not directly related to the course content, it offers a beginner-friendly introduction to SQL, a database programming language. It can be useful for those who have little to no programming background but want to further explore data analysis and data manipulation.
SQL Queries for Mere Mortals: A Hands-On Guide to...
Kindle Edition
$$
SQL Queries for Mere Mortals: A Hands-On Guide to...
Paperback
$$
SQL Queries for Mere Mortals: A Hands-on Guide to...
Paperback
$$
SQL Queries for Mere Mortals: A Hands-On Guide to...
Paperback
$$
Cover image
Data Analysis
Save
Provides an introduction to Bayesian data analysis methods. It covers topics such as probability theory, Bayesian inference, and Markov chain Monte Carlo methods. While it is not directly related to the course content, it can be useful for those interested in exploring Bayesian methods.
Data Analysis: A Bayesian Tutorial
Paperback
$$
Data Analysis: A Bayesian Tutorial
Kindle Edition
$$

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Course image
Effort
3 to 4 hours per week for 6 weeks
Level
Elementary
Via
edX
Institution
SDA Bocconi School of Management
Instructor
Renata Trinca Colonel
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Designed for non-specialists and those without extensive quantitative or statistical backgrounds
Provides basic knowledge of data analysis, covering essential topics such as data visualization, summary statistics, and probability distributions
Aims to develop skills in managing common data analysis techniques, enabling learners to analyze business variables effectively
May not be suitable for learners seeking advanced or specialized data analysis knowledge
Does not include hands-on labs or interactive materials
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