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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.
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
Register for this course and see more details by visiting:
OpenCourser.com/course/c13dkt/data
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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
- 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
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Data Analysis for Business: Foundations to your list so you can find it easily later:
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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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Review statistical concepts and principles
Show steps
Strengthens foundational knowledge of statistical concepts, enhancing understanding of data analysis.
Browse courses on
Summary Statistics
Show steps
Show steps
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
Show steps
Enhances retention and recall of key concepts by organizing and reviewing course materials.
Show steps
Show steps
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
Show steps
Provides access to guidance, support, and insights from industry professionals.
Show steps
Show steps
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
Show steps
Builds a practical understanding of data analysis techniques used in real-world scenarios.
Browse courses on
Data Analysis
Show steps
Show steps
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
Show steps
Provides a comprehensive overview of data analysis techniques and methodologies commonly used in business.
View
The Scientific Method
on Amazon
Show steps
Show steps
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
Show steps
Enhances understanding and retention of data analysis concepts through repetitive practice.
Browse courses on
Descriptive Statistics
Show steps
Show steps
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
Show steps
Solidifies an understanding of representing and summarizing business data using charts and tables.
Browse courses on
Data Visualization
Show steps
Show steps
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
Show steps
Provides opportunities to learn from peers, receive feedback, and enhance analytical skills.
Browse courses on
Peer Review
Show steps
Show steps
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
Show steps
Reinforces understanding of data analysis concepts and improves problem-solving skills.
Browse courses on
Data Interpretation
Show steps
Show steps
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
Show steps
Expands knowledge of data analysis beyond the scope of the course, providing a foundation for further exploration.
Browse courses on
Machine Learning
Show steps
Show steps
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
Show steps
Develops skills in communicating data effectively and visually to stakeholders.
Browse courses on
Data Visualization
Show steps
Show steps
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
Show steps
Applies data analysis skills to a practical business scenario, reinforcing understanding and showcasing capabilities.
Browse courses on
Data Visualization
Show steps
Show steps
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 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.
Business 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.
Operations Research 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.
Market Researcher
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.
Financial 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.
Consultant
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.
Statistician
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.
Data Scientist
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.
Quantitative Analyst
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.
Actuary
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.
Software Engineer
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.
Marketing 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.
Product Manager
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.
Financial Planner
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.
Data Visualization Specialist
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.
For more career information including salaries, visit:
OpenCourser.com/course/c13dkt/data
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.
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.
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.
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.
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c13dkt/data
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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
Good to know
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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