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Neil Govier, CFA
Aimed at investment professionals or those with investment industry knowledge, this course offers an introduction to the basic data and statistical techniques that underpin data analysis and lays an essential foundation in the techniques that are used in big data and machine learning. It introduces the topics and gives practical examples of how they are used by investment professionals, including the importance of presenting the “data story" by using appropriate visualizations and report writing.
In this course you will learn how to:
- Explain basic statistical measures and their application to real-life data sets
- Calculate...
Read more
Aimed at investment professionals or those with investment industry knowledge, this course offers an introduction to the basic data and statistical techniques that underpin data analysis and lays an essential foundation in the techniques that are used in big data and machine learning. It introduces the topics and gives practical examples of how they are used by investment professionals, including the importance of presenting the “data story" by using appropriate visualizations and report writing.
In this course you will learn how to:
- Explain basic statistical measures and their application to real-life data sets
- Calculate...
Read more
Aimed at investment professionals or those with investment industry knowledge, this course offers an introduction to the basic data and statistical techniques that underpin data analysis and lays an essential foundation in the techniques that are used in big data and machine learning. It introduces the topics and gives practical examples of how they are used by investment professionals, including the importance of presenting the “data story" by using appropriate visualizations and report writing.
In this course you will learn how to:
- Explain basic statistical measures and their application to real-life data sets
- Calculate and interpret measures of dispersion and explain deviations from a normal distribution
- Understand the use and appropriateness of different distributions
- Compare and contrast ways of visualizing data and create them using Python (no prior knowledge of Python necessary)
- Explain sampling theory and draw inferences about population parameters from sample statistics
- Formulate hypotheses on investment problems
This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.
Register for this course and see more details by visiting:
OpenCourser.com/course/6u28x7/data
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
An accessible introduction to fundamental concepts in data analysis
Provides a strong foundation for professionals seeking to enhance their data analysis capabilities
Demonstrates the practical applications of statistical techniques commonly used in the investment industry
Emphasizes the significance of effective data visualization for communicating insights
Taught by an experienced CFA instructor with expertise in investment analysis
Assumes prior knowledge of investment industry concepts
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Save Data and Statistics Foundation for Investment Professionals 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 and Statistics Foundation for Investment Professionals with these
activities:
Review Python Basics
Show steps
Review the basics of Python to ensure a solid foundation for the course materials.
Browse courses on
Python Basics
Show steps
-
Review basic data types and data structures in Python.
-
Practice writing simple functions and control flow statements.
-
Review how to manipulate and analyze data using NumPy and Pandas.
Attend a Data Science Meetup
Show steps
Connect with professionals in the data science field.
Show steps
-
Research and find a relevant meetup.
-
Attend the meetup and actively participate in discussions.
-
Network with other attendees and exchange insights.
Statistics Practice Problems
Show steps
Reinforce statistical concepts through practice problems.
Show steps
-
Solve practice problems on measures of central tendency and dispersion.
-
Apply statistical principles to real-world data sets.
-
Interpret the results and draw conclusions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Python for Data Analysis Workshop
Show steps
Gain hands-on experience with Python for data analysis.
Show steps
-
Find a relevant Python for data analysis workshop.
-
Attend the workshop and actively participate in the exercises.
-
Practice and apply the concepts learned.
Data Visualization Report
Show steps
Demonstrate proficiency in data visualization and storytelling.
Show steps
-
Choose a data set related to the course topics.
-
Explore and clean the data.
-
Create visualizations to present the data effectively.
-
Write a report interpreting the results and insights.
Interactive Data Dashboard
Show steps
Showcase data analysis skills and storytelling through an interactive dashboard.
Show steps
-
Gather and clean data from multiple sources.
-
Design and develop an interactive dashboard using a data visualization tool.
-
Present and share the dashboard with peers or stakeholders.
Kaggle Competition
Show steps
Apply knowledge and skills in a real-world data science challenge.
Show steps
-
Identify a relevant Kaggle competition.
-
Build a model and develop a solution.
-
Submit the solution and track the results.
Develop a Stock Prediction Model
Show steps
Put the course concepts into practice by building a project.
Show steps
-
Gather historical stock market data.
-
Clean and analyze the data.
-
Develop and train a predictive model.
-
Evaluate the model's performance and make predictions.
Review Python Basics
Show steps
Review the basics of Python to ensure a solid foundation for the course materials.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review basic data types and data structures in Python.
- Practice writing simple functions and control flow statements.
- Review how to manipulate and analyze data using NumPy and Pandas.
Attend a Data Science Meetup
Show steps
Connect with professionals in the data science field.
Show steps
Show steps
Show steps
- Research and find a relevant meetup.
- Attend the meetup and actively participate in discussions.
- Network with other attendees and exchange insights.
Statistics Practice Problems
Show steps
Reinforce statistical concepts through practice problems.
Show steps
Show steps
Show steps
- Solve practice problems on measures of central tendency and dispersion.
- Apply statistical principles to real-world data sets.
- Interpret the results and draw conclusions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Python for Data Analysis Workshop
Show steps
Gain hands-on experience with Python for data analysis.
Show steps
Show steps
Show steps
- Find a relevant Python for data analysis workshop.
- Attend the workshop and actively participate in the exercises.
- Practice and apply the concepts learned.
Data Visualization Report
Show steps
Demonstrate proficiency in data visualization and storytelling.
Show steps
Show steps
Show steps
- Choose a data set related to the course topics.
- Explore and clean the data.
- Create visualizations to present the data effectively.
- Write a report interpreting the results and insights.
Interactive Data Dashboard
Show steps
Showcase data analysis skills and storytelling through an interactive dashboard.
Show steps
Show steps
Show steps
- Gather and clean data from multiple sources.
- Design and develop an interactive dashboard using a data visualization tool.
- Present and share the dashboard with peers or stakeholders.
Kaggle Competition
Show steps
Apply knowledge and skills in a real-world data science challenge.
Show steps
Show steps
Show steps
- Identify a relevant Kaggle competition.
- Build a model and develop a solution.
- Submit the solution and track the results.
Develop a Stock Prediction Model
Show steps
Put the course concepts into practice by building a project.
Show steps
Show steps
Show steps
- Gather historical stock market data.
- Clean and analyze the data.
- Develop and train a predictive model.
- Evaluate the model's performance and make predictions.
Career center
Learners who complete Data and Statistics Foundation for Investment Professionals will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
Quantitative analysts translate complex financial data into actionable investment strategies. This course provides a solid foundation in data analysis techniques, statistics, and data visualization, all of which are essential to success in this role. You'll learn how to use these techniques to identify patterns and trends in data, develop predictive models, and make informed investment decisions.
Data Analyst
Data Analyst
Data analysts collect, clean, and analyze data to identify trends and patterns. This course provides a strong foundation in data analysis techniques, statistics, and data visualization. You'll learn how to use these techniques to extract meaningful insights from data, which is essential for success in this role.
Financial Analyst
Financial Analyst
Financial analysts provide investment advice to individuals and institutions. This course provides a solid foundation in data analysis techniques, statistics, and data visualization. You'll learn how to use these techniques to analyze financial data, make investment recommendations, and manage portfolios.
Market Researcher
Market Researcher
Market researchers collect and analyze data to understand market trends and consumer behavior. This course provides a solid foundation in data analysis techniques, statistics, and data visualization. You'll learn how to use these techniques to conduct market research, identify opportunities, and develop marketing strategies.
Risk Analyst
Risk Analyst
Risk analysts identify and assess financial risks. This course provides a solid foundation in data analysis techniques, statistics, and data visualization. You'll learn how to use these techniques to analyze risk data, develop risk models, and make informed decisions.
Data Scientist
Data Scientist
Data scientists use data to solve business problems. This course provides a foundation in data analysis techniques, statistics, and data visualization. You'll learn how to use these techniques to extract insights from data, develop predictive models, and make informed decisions.
Business Analyst
Business Analyst
Business analysts use data to improve business processes. This course provides a foundation in data analysis techniques, statistics, and data visualization. You'll learn how to use these techniques to identify inefficiencies, develop solutions, and improve business outcomes.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course provides a foundation in data analysis techniques, statistics, and data visualization. You'll learn the principles of probability and statistics, and how to apply them to real-world problems.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. This course may be useful for software engineers who want to specialize in data analysis or machine learning. You'll learn the basics of data analysis techniques, statistics, and data visualization.
Product Manager
Product Manager
Product managers are responsible for the development and launch of new products. This course may be useful for product managers who want to gain a better understanding of data analysis techniques, statistics, and data visualization. This knowledge can help you make better decisions about product development and marketing.
Actuary
Actuary
Actuaries assess risk and uncertainty. This course may be useful for actuaries who want to specialize in financial risk management. You'll learn the basics of data analysis techniques, statistics, and data visualization.
Investment Banker
Investment Banker
Investment bankers provide financial advice and services to corporations and governments. This course may be useful for investment bankers who want to specialize in data-driven investing. You'll learn the basics of data analysis techniques, statistics, and data visualization.
Hedge Fund Manager
Hedge Fund Manager
Hedge fund managers invest money on behalf of clients. This course may be useful for hedge fund managers who want to gain a better understanding of data analysis techniques, statistics, and data visualization. This knowledge can help you make better investment decisions.
Private Equity Investor
Private Equity Investor
Private equity investors invest money in private companies. This course may be useful for private equity investors who want to gain a better understanding of data analysis techniques, statistics, and data visualization. This knowledge can help you make better investment decisions.
Venture Capitalist
Venture Capitalist
Venture capitalists invest money in startups. This course may be useful for venture capitalists who want to gain a better understanding of data analysis techniques, statistics, and data visualization. This knowledge can help you make better investment decisions.
For more career information including salaries, visit:
OpenCourser.com/course/6u28x7/data
Reading list
We've selected 11 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 and Statistics Foundation for Investment Professionals.
This advanced textbook commonly assigned text in both academic and professional machine learning programs.
This advanced textbook on machine learning is commonly assigned in both academic and professional machine learning programs.
Save
This advanced textbook on deep learning is commonly assigned in both academic and professional data science programs.
This advanced textbook on statistical learning is commonly assigned in both academic and professional data science programs.
This practical guide to deep learning using the Keras library is commonly assigned in deep learning courses.
This statistics textbook useful reference for those interested in developing a strong statistical background.
This practical introduction to data science techniques is commonly assigned in data science courses for beginners.
Save
This practical introduction to machine learning is commonly assigned in machine learning courses for beginners, and provides a good introduction to the practical applications of machine learning.
This practical introduction to machine learning is commonly assigned in machine learning courses for beginners, and provides a good introduction to the practical applications of machine learning.
This practical introduction to data science techniques and machine learning is commonly assigned in data science courses for beginners.
This practical introduction to data science techniques is well suited for beginners in the field and provides practical examples of using Python for data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6u28x7/data
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Time
34 hours
Via
Coursera
Institution
CFA Institute
Instructor
Neil Govier, CFA
This course belongs to a
collection
called
Data Science for Investment Professionals. It's comprised of three courses:
- Machine Learning for Investment Professionals
- Statistics for Machine Learning for Investment Professionals
- Data and Statistics Foundation for Investment Professionals (viewing)
Good to know
An accessible introduction to fundamental concepts in data analysis
Provides a strong foundation for professionals seeking to enhance their data analysis capabilities
Demonstrates the practical applications of statistical techniques commonly used in the investment industry
Emphasizes the significance of effective data visualization for communicating insights
Taught by an experienced CFA instructor with expertise in investment analysis
Assumes prior knowledge of investment industry concepts
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