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Fall classes are offered remotely—either live-online with an instructor, entirely self-paced, or in a blended online format. Please check our coronavirus update page for our latest announcements.
Making Money with Data
The exploding demand for data science professionals is well documented, with average salaries above six figures and LinkedIn reporting a shortage of over 150,000 people with data science skills. That demand aside, this course is designed for everyone in pretty much every walk of life because as Peter Drucker, the famed management consultant, once said that what you can’t measure, you can’t manage. And measure means asking the right questions about a problem at hand and to go about assimilating the right sets of data. And manage means making decisions and implementing actionable insights to grow a business, to design a better product, to improve a process, to reduce cost or to do anything and everything that helps the bottom-line.
Data-driven decisions are just better decisions - it’s as simple as that. So this course is not only for students looking to explore jobs in data science, but also for anyone interested in exploring the power of statistical thinking in less conventional areas of their lives and careers.
The course broadly covers the fundamentals of making money with data. Students will be exposed to data science concepts like cleaning, organizing and visualizing data. Each module also includes lessons on how to apply a data driven mindset to real world situations. Some examples:
- The need and the fundamentals of evidence-based decision making
- Capital markets theory and what it takes to start a business
- Personal finance basics and investment portfolio design concepts
- Business and personal risk management and the data science math behind it
- Writing, communication and persuasion skills
This course combines short video-based lessons & exercises with case-based homework assignments. All lessons and exercises are performed in Jupyter Notebook.
- Basic statistical concepts - random variables, variance, covariance, correlation, distributions, confidence intervals, statistical significance, hypothesis testing and the rest.
- Python for data analysis
- NumPy & pandas basics
- Regular expressions
- Data aggregation and merge techniques
- Data visualization
- Predictive analytics
This course is offered in a hybrid format. There are live-online meetings twice weekly with the instructor, short, video-based lessons, and case-based homework assignments. While some content within the course can be studied at your own pace, you will be expected to meet weekly homework assignments and to attend the live-online class. Online class participation is part of your final grade.
Synchronous online meeting days/time: Mondays & Wednesdays 7-8:00 pm
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