Data Bootcamp: Description & Sales Pitch
Data Bootcamp is about nuts and bolts data analysis. You will learn about economic, financial, and business data, and enough about computer programming to work with it effectively. Applications include some or all of: leading economic indicators; emerging market country indicators; bond and equity returns; stock options; income by zip code; long tail sales data; innovation diffusion curves; and many others. We will use Python, a popular high-level computer language that’s widely used in finance, consulting, technology, and other parts of the business world. “High-level” means it’s less painful than most (the hard work is done by the language), but it’s a serious language with extensive capabilities. “Data analysis” means primarily graphical descriptions that summarize data in ways that are helpful to managers. “Bootcamp” is a reminder that expertise takes work. Don’t worry, it’s worth it.
Frequently asked questions
Why should I take this course? It’s an investment in your future. You will learn how to process data and communicate its content effectively and efficiently. You will be more valuable to current and future employers.
Can’t I do what I need in Excel? Excel is a great program, but once you have a little programming experience it will remind you of doing arithmetic on your fingers. With Python, you will be able do routine tasks more efficiently (“automate the boring stuff,” as one guide suggests), handle larger data sets, merge different data sets, and generally do things that spreadsheet programs can’t do.
What are the prerequisites? There are none, other than the desire to take on a challenge and the patience to debug programs that don’t quite work – and they never work the first time, and often not the second or third time either.
What if my quant skills are weak or nonexistent? This is the course for you! We will start at the beginning and do our best to make the material accessible to everyone. We’re looking beyond quants to marketing, management, and humanities majors. One of our design team was an English major.
Is this a standard Python course? No, it’s a data course that uses Python. We’ll cover aspects of Python related to data analysis, specifically data management and visualization, and ignore the rest. We estimate the overlap with a typical programming class to be 15-20 percent.
Will this turn me into a programmer? You will come out of the course somewhere between Brad Pitt and Jonah Hill in “Moneyball,” with the skills to deal with whatever data comes your way. You will not be ready for a career as a programmer, but you will be able to do things with data that Excel users can only dream about. And you will be able to work effectively with people who know more.
Will this turn me into a data scientist? Sadly, no. But you will have a solid foundation for pursuing the many technical topics that fall under the rubrics data science and machine learning. See, for example, the extensive collection of courses on business analytics and data science offered by our IOMS and CS groups.
Can I do this on my own? In principle, yes, but it’s much easier in a supportive environment.
Should I take this course if I already know how to code? You’re welcome to, and will learn a lot about data and the data components of Python. But please don’t scare the other students.
Will this course cover SQL? Short answer: No. Longer answer: We will use some of the same functionality in Python’s data-management tools, but we will not cover SQL explicitly. We will also skip such computer science standards as regular expressions (yes, that’s a thing) and data formats (XML, JSON, etc).
Not that you need any more, but here are a couple good pieces:
- Steve Levitt on “big data” in business (55m, very entertaining)
Mevan on learning to code
Noah Lorang on what businesses need: “The dirty little secret of the ongoing ‘data science’ boom is that most of what people talk about as being data science isn’t what businesses actually need. Businesses need accurate and actionable information to help them make decisions about how they spend their time and resources. There is a very small subset of business problems that are best solved by machine learning; most of them just need good data and an understanding of what it means that is best gained using simple methods.”