Data Science Q&A 01.21.21 Recap

The University of Notre Dame chapter held a Q&A event about the data science field with Codecademy senior curriculum developer Alex Kuntz and Codecademy data science curriculum developer (and statistics course host on YouTube) Sophie Sommer. A big thank you goes out to Alex and Sophie for sharing their insights with the chapter! 

If you couldn’t make the event, here’s the recap:

Question: What is the rationale behind the design of the data science pathway?

Answer: The Data Scientist Career Pathway is designed as a linear journey through Python with options to dig deeper into specific areas. For example, someone with an interest in data acquisition can drill databases and SQL (and check out the databases path on Codecademy). An aspiring machine learning specialist can put extra effort into the concluding sections of the pathway. And someone interested in a niche topic like natural language processing can skip down to chapter 14 of the syllabus. The new version of the pathway is all about customization, so take advantage of that!

Q: How should Codecademy learners best supplement the data scientist career pathway?

A: The hardest part of being new to independent projects is getting set-up. It’s normal that this part is frustrating or intimidating, so make StackOverflow your friend and power through. If you’re getting started in python, go ahead and download Anaconda so that everything you need is downloaded in one go. Then, find a data set that you’re genuinely interested in! Some of Alex’s favorite datasets are about baseball and Jeopardy, while Sophie recommends reading the news with an eye for how you could analyze the data yourself. News sources, including The New York Times and FiveThirtyEight, have GitHub repositories where they provide the datasets. Codecademy learners should also be cognizant about developing their statistics and math knowledge alongside technical programming skills. 

Q: A lot of chapter members are hoping to make a career switch into the data science space. What’s important besides the technical skills?

A: It depends on the size of your potential company. Really small companies might not have a clearly-defined role of a “Data Scientist,” and your tasks might be closer to database engineering or data analytics depending on what’s most needed at the firm. In the current environment, data scientists often collaborate with several different departments, such as marketing or business, to fulfill their main role of answering questions and making decisions based on data. So be sure to beef up your presentation and communication skills if you’re not so confident with giving reports about what your findings mean to people at your company. Also keep in mind that visualizing data in unique and meaningful ways requires a certain artistic ability. Keep seeking out examples of engaging visualizations to inspire your own work.

Q: It seems like data-driven decisions are understandably becoming crucial in fields like economic policy and business. In your opinions, how is data science influencing traditionally non-tech fields?

A: Data literacy is so important no matter the field. The more familiar you are with how to perform statistical analyses, the more you can think critically about the data in front of you, such as by noticing if a graph in a news source is misleading. Even non-technical roles require people to make decisions based on data and various statistical tools like A/B testing.  

Q: Data is pervading nearly all aspects of life, and the field itself isn’t immune to change. How do you see the data science field evolving in the next 5 years? 

A: Data science is quite a new field that has already changed a lot in the past 5 years - it’s still evolving right now in terms of its definition - so we can expect lots of changes in the future. Because of its interdisciplinary nature, the role of a data scientist at different companies can vary so much, even if people have the exact same job title. It seems natural that such a nebulous field will split into several different subfields in the future. 

Q: What’s going to make people stand out as data science becomes more popular?

A: Today, every field has data associated with it, and data scientists are going to be employable in nearly every sector of the economy. One potentially advantageous path would be to combine data literacy skills with subject expertise from the liberal arts. Alex and Sophie both have backgrounds in education and find it beneficial to draw on those skills when they’re designing curricula for Codecademy. 

Questions from meet-up attendees: 

Q: What’s the level of someone who finishes the Data Scientist Career Path? Would they be ready to go to a data science master's program? Would they be able to go get data analyst internships or jobs?

A: The ultimate intention is that people will be prepared for entry-level jobs, but there are several considerations that distinguish serious candidates. If you post in the forums, spend a lot of time getting holistic feedback from code partners, and invest effort into off-platform projects (ie: go above and beyond!), you can be prepared for a master's program or even an entry-level job.  

Q: Both of your backgrounds are interesting with the connection to education. Do you have any projects where you work with younger people (i.e. high school)?

A: The best way to make sure you’ve learned something is to teach it to others. For example, Sophie joined the group RLadies to help run workshops for younger students learning R.

Q: What’s the difference between the data analyst path and the data science path? 

A: The data analytics path focuses more on methods for exploring a dataset as opposed to building a predictive model, heavy programming projects, etc that you would find in the data science path. Data analysts are more likely to be found creating reports using data whereas data scientists are often more focused on automating processes, building data pipelines, and writing algorithms.    

Extra Tidbits:

  • Set up a GitHub profile as early as possible in your learning journey (repositories can be made private if you’re self-conscious) to document your personal projects for future potential employers. 

  • For data analytics in particular, an understanding of Business Intelligence (BI) dashboards and Tableau can prove very useful in your career journey in addition to programming skills.   

  • If you’re interested in doing a master’s program, take a look at applied statistics for social science (Sophie’s degree!). 


Join the Notre Dame chapter for our next event: FEBRUARY COFFEE AND CODE on February 9th, 2021 at 11:00am EST.