The profile of a data scientist is changing slightly as the profession becomes more solidified. Data Science 365 conducts a study to determine some of the characteristics of a “typical data scientist.” The below infographic covers a wealth of information from programming languages used to educational backgrounds to locations. It is definitely worth looking at to understand the attributes of a data scientist in 2019.
The fine folks at DataCamp, a great site for learning data science right in your browser, have come up with another great infographic. This time it compares some of the many job titles in the data science field.
The infographic lays out the roles and skills needed for the following job titles. Note: not all the job roles can be confused with a data scientist, but all the roles can be important when completing an entire data science project.
- Data Scientist
- Data Analyst
- Data Architect
- Data Engineer
- Database Administrator
- Business Analyst
- Data & Analytics Manager
The lines between analytics and data science can definitely be very blurry. Different companies might call the same position by two different names, but at their core, they do have some differences.
Below is an infographic from the faculty of the Online MS in Analytics at American University. I think the infographic is accurate.
In my opinion, a true data scientist should spend more time creating and programming new algorithms while a business analyst should spend more time applying existing algorithms.
A couple of notes
- Years of Education are not much different, but the academic disciplines are very different. Data Scientists tend to have degrees with more rigorous mathematical training. For me, this is the biggest differentiator.
- It appears financial institutions prefer business analysts while the government and colleges prefers data scientists
- Surprisingly, Business analyst jobs are projected to grow faster than data scientists (27% to 15%), not sure I totally agree with that!
Know Of Other Differences?
Please, Leave a Comment.
Brought to you by American University’s Analytics@American, a masters in business analytics
Data Visualization is not new. Check out this historical collection of 11 visualizations. Here are 2 big takeways for me.
- Even many many, years ago, data was being used to make decisions
- Visualizations have come a long way
Yes, this is an infographic of infographics.
The great team over at DataCamp, an online site for learning R , has put together another wonderful infographic. This time, the topic is Data Science Wars (R versus Python). This has been a rather hot topic for quite some time. I even wrote about the debate back in 2013, R vs Python, The Great Debate.
DataCamp did an amazing job packing information into the infographic. Honestly, it is impressive they were able to pack so much information into a single infographic. Some of the topics covered are:
- Who uses the language?
- Purpose of the language
- And way more great stuff
Enough about the description. Have a look for yourself. It is packed with great arguments for your next “R vs Python” debate.
This infographic is packed with good data. I especially enjoyed the section about big data startups that were acquired in 2013.
Here is a great infographic from Data Science @ Berkeley. Just how big is a Gigabyte(GB)? Be sure to look all the way to the bottom. It mentions/explains a few of the latest innovations in hard drives, for example: helium, SMR, HAMR. You will have to scroll to the bottom to see what those acronyms mean.
Brought to you by datascience@berkeley: Master of Information and Data Science