A data scientist intern does not run around troubleshooting and printing letters for the bosses. In fact, it is a job that needs to be taken seriously; not only to learn new skills but also to apply your current data science knowledge practically to solve real-world problems.
In this article, we’ll go over what a data scientist intern does, their roles, responsibilities, and more.
Let’s get started.
Being a data scientist intern simply rocks
It’s not just a tech-savvy job involving big data. It’s actually one of the best professions out there, especially for GenZ.
Data science is an emerging field and has already been named the sexiest job of the 21st Century. But this is no surprise as data science is an expanding branch of science and its application possibilities are endless.
Data science actually targets responses and observes if a certain change or tweak was made in the current system. Data science is basically detective work. You’ll be searching for clues in how a particular problem can be solved, with a whole team dedicated to the task. In short, be prepared for teamwork.
As a data scientist intern, you’ll meet professionals in your field of career and work alongside them; cultivating your growth. As we all know, growth is always fundamental to an internship experience.
Apart from the usual data analysis tasks, you’ll always be gunning for three significant kinds of results:
These terms become self-explanatory as you explore the rest of the article.
To gain a better understanding of the roles and responsibilities of a data scientist intern and the challenges they can face, just keep reading.
Data Scientist Internship Summarized
Corporate dealings, attending lots of meetings, and observing company members are what interns usually have to do as their day-to-day operations. But that is just the boring side of the data scientist internship experience.
Right from the start, you’ll get a proper computerized entry card, and trust me when I say this, the satisfaction of swiping a card is far too great to be overlooked.
Interns usually start off as Data Science Business Analysts, and they’re assigned to analyze the company’s mode of operations. An obvious skill gap is observed between interns and personnel, nevertheless, everyone knows that training will always be required for ANY internship. Thus, it is provided accordingly.
So, what do data scientist interns do? They just sit back in their chairs and surf the internet. But we’re not talking about Facebook here. Data science interns spend most of their time exploring the data they are given to process and present their findings to the panel. To sum up, it’s all about creative data analysis.
For example, say you’re working on the management of a demand tracking system. Your tasks can range from monitoring, tracking, and documentation of hundreds of product launches to cleaning data. This was just an example. That being said, your internship can vary according to company specialty or the internship program.
Such an internship might sound geeky, but in practice, the life of a data science intern is not only fast-paced but full of new exciting experiences every day. Apart from being creative and innovative, you must have a knack for problem-solving.
What Does a Data Scientist Intern Do? – Roles and Responsibilities
The tasks usually depend on the company itself. In traditional companies, the methodology typically involves simple data analysis and visualization. Basic SQL and database knowledge is required to receive your results.
R&D-oriented companies give you other forms of work - various types of machine learning and/or profound learning. Constructing those features or functions, for instance, to explore research articles. This requires a strong history in mathematics and computer science.
Some of the general tasks relevant to both types of companies are given below:
1. The Business Requirements:
Once interns are given a run-down of the business side of the job, their approach to all tasks must be customer-centric, as the needs of the customer are a top priority. This sets forth a straightforward roadmap with future tasks and obstacles, especially with changing market demands.
Keep in mind, that in the end, you're working for a business and all your problems are business-related. So, either be prepared for a run-down of how businesses work or remember to ask.
2. Problem Solving:
Interns review data to address problems and also figure out how to classify the data until those data criteria are well established. This is where they observe and record correlations in the datasets but keep one thing in mind: Correlation is not causation.
Here’s where creative problem-solving plays a vital role. Not only does a data scientist intern try to reach solutions to rising problems in data, but they also creatively tackle new problems that arise as a result of changing dynamics in data, due to the fast-paced IT industry.
3. Build Data Set OR Combine Items
Once the data is classified, another obstacle would be to define essential characteristics or even to create features if data dimensions do not exist. A crucial step for this process is the assessment of functions and setting up of the training/test results.
4. Assess Company Models
If a model exists at this time, as it does not in most cases, interns would go on and on to compare different models specifically for the purpose of learning and to improve their own performance. The later phases would include model improvisations and spontaneous analysis of data during presentations.
Spontaneous analysis of data requires quick thinking and on-the-spot improvisation. This can only be achieved through regular practice. Most importantly, it involves being able to connect data problems to previous examples of similar problems in order to come up with the best-known solutions.
5. Final Analysis
If there is an acceptable model for a particular problem, the obvious decision would be to try it out and compare it to any prior models. This is to ensure if the effort and energy in drawing the model lead to changes by a few decimal points or not. If all this is Greek to you, it’s time to take a few online courses.
Challenges that data scientist interns face:
When you walk into the company for the first time as a data science intern, you’ll notice that the work environment is not really perfect so to speak. Your perfect bubble will soon burst as you’re required to perform to the mark and face a multitude of challenges along the way.
An important challenge is switching from Windows-based OS to Mac OS. Data science has certain applications that are supported by Apple’s operating system. Basic tasks like installing a particular application or moving files can seem tedious at the very least. Rest assured, it will just take some time to get the hang of it.
Another associated problem is the sudden change in tech which can be pretty drastic for data science interns. A lot of new programs and database management systems are being used by companies for instance open-source visual tools like Grafana.
Another key challenge would be the shift from a learning environment to a work environment. The magnitude of this challenge would however be lessened if core communication with mentors is carried out effectively. Therefore, the key to gaining a full internship experience is communication.
You’re planning to apply, but will you make the cut?
Firstly, we’ll talk about specifics that data science companies look for in individuals who are planning on applying for internships. The key element will be to start early, build an online resume, conduct research, and network with professionals who are willing to guide you.
Hiring managers, for data science internships, often search for people who will be the best fit in their particular environment. However, this should not bring your hopes down because we have gathered a list of soft skills that will definitely push your profile up:
A Basic Requirement: Creativity and Innovation!
Recruiters know that applicants will be college students, so they will definitely expect you to have an imaginative approach towards problems. The thing that they will be on the lookout for is how you break down a particular problem presented to you in a clever way. Keep in mind that this could also be part of your interview.
Perhaps, for a data science internship, the most critical personality trait has been detailed as an example of following a curriculum vitae; "market research analysts have to be careful because they always do a precise review of their numbers."
In addition, recruiters prefer interns in information and data science that have a new and innovative approach towards real-world challenges in the data science field. This ability for innovation is considered an important skill as new analytical problems, that have never been experienced before, arise on a daily basis.
Data Scientist Intern Checklist
Apart from the basic requirements of an internship, a knowledge of technical skills is preferred. Elements of the skill checklist of a qualified applicant include:
- Machine learning with the use of algorithms
- Using statistics for analysis
- Map/Reduce familiarity
- NoSQL databases familiarity (MongoDB, Riak, Hadoop)
- Python language coding (Ruby as a plus)
- Presentation skills for presenting results of a project to non-technical people
These skills are needed for every aspiring data scientist. They are what justify the data scientist salary.
A Healthy Note to Remember
Keep in mind that an internship is usually considered a learning opportunity, therefore, you’re not expected to be as technically adept as a full-time data scientist. In addition, it depends on what year of college you are in. Obviously a senior will be expected to have a higher level of knowledge than a junior.
As a data science intern, you have the opportunity to work hand in hand, in a low-pressure work setting with a skilled team of data analysts. It is common knowledge to your team that you’re on a learning mission. Naturally, if you’re serious about a career in data science or related fields, you must give the internship your best.
This could potentially be the place where you land a job. Therefore, take advantage of the opportunity of being a "learner" as much as you can.
When you’re not analyzing data, you can create a perspective on the data scientist’s lifestyle while running commodities or grabbing a coffee from time to time.