Why Data Science Internship?
Before applying to any Data Science Internship, the first question that should arise in your mind should be why Data Science Internship.
First of all, Data science internships don’t happen overnight, and they are not a cup of tea, though. They need time and effort. There are many things you need to work on before you can start applying to become a data science intern, that we will discuss in this article late on.
Internships are a great way to get your foot in the door at the company you want to work for.
Especially if you’re going to land a data science internship. There are plenty of benefits for data science interns. They get the opportunity to learn from professionals, gain practical experience in their field, and they can build a robust professional network.
Currently, data science is a really popular area to work in. Searches for the term “Data Scientist” have increased by 6 times in 5 years. At the moment data science is ranked as the sixth-best job on Glassdoor, namely because of its high rate of pay, career opportunities and availability of job openings.
Data science is quickly becoming a vital skill as more companies scramble to capitalize on the hordes of data they can collect on clients and customers.
Naturally, as the demand for data scientists grows, more students want to break into the field, which means they’ll be looking for internships.
In fact, a recent McKinsey report study on big data found that demand for deep analytical talent in the United States could be 50 to 60 percent greater than its projected supply by 2019.”
What is Data Science?
“Data Science is the concept of collecting, storing and analyzing data (structured or unstructured) for extracting useful insights from it.”
The amount of data being generated every day is increasing exponentially! The sources of data and the ability to collect and store it has come a long way in the last decade. Companies are using a variety of tools and techniques to mine patterns in the data and gather useful insights. That, in a nutshell, is what data science is all about.
A lot of people think that to be a good data scientist, they just need to have good coding skills and understand machine learning statistical concepts. But what they lack—and what we look out for—are problem-solving skills and business acumen.
How to Start Data Science Journey
For a starting position in Data Science, as an intern, you should at least have your basic statistics and programming concepts cleared.
Technical Skills for Data Science Internship
Some of the general technical knowledge you need:
- Basic knowledge of either Python or R. Whether you have more of a computer science background or statistics background, you need to have the basics down of either language.
- You should know the basics of SQL or any other database in order to retrieve data from a database. It is not very hard to learn and you can have the basics down within a weekend. You should also familiarize yourself with git.
- Familiarize yourself with the Linux environment.
- Makes sure your stats background is rock solid. If you’re majoring in economics, you’ll probably have completed some math coursework that dived into statistics/modelling. Review those notes if you can to help you refresh your memory, it will synergize well with everything.
- Hadoop, Scala and Spark are useful technologies to learn in relation to Big Data, though not necessarily essential. It would also be good to have a working knowledge of Cloud Computing.
- I think you are good to go with only 2–3 undergraduate courses in statistics and 2–3 courses in computer science. Know basic probability theory, what confidence intervals, t-tests, and p-values are. Understand what bias is and how easily it can be introduced in the data you are analysing. Know statistical learning methods such as linear regression, logistic regression and one or two more classification techniques. Also, understand the basics of computing. How to name variables, functions etc. Know how to construct a basic for loop and if-else statements.
Soft Skills companies look for when hiring data science interns
If you want to become a fully-fledged Data Science intern you should also focus on Soft Skills. Some of the common Soft Skills are mentioned below:
- Indirect experience: If you don’t have any work experience, start volunteering or working in your community (ex. cashier, database handling). Before your data science interview, be sure to take time to think about what skills you have gained working at past jobs and how you can best apply these to your data science internship.
- Attitude: Companies want someone who is willing to go above and beyond the call of duty and someone who is adaptable to different situations. In addition always keep in mind that one of the main goals of an internship, for a company, is to test out potential candidates for full-time positions.
- Structured Thinking: Having a structure not only helps an analyst understand the problem at a macro level, but it also helps by identifying areas which require deeper understanding.
- Networking: Take part in Events taking place outside your university will have a mixed crowd where students, young professionals and managers will be in attendance. You never know when someone you meet might think you are the right fit for their company and put in a word that gets you at the top of that pile of resumes.
- Culture: Every new employee in a company adds a bit to the company culture. It’s important that you understand the company’s culture and it fits how you work. However, always keep in mind that asking your interviewer these questions is amazingly important, as an interview involves a mutual decision.
Building Your Online Brand
What makes a good data science portfolio?
Building a digital portfolio takes time and effort. To build one, you need to:
- Identify your knowledge, skills, and abilities (what do you know? What can you do?)
- Similarly Gather evidence (how can you demonstrate your knowledge, skills, and abilities?)
- Above all organize and assemble your portfolio (how can you categorize the evidence you have gathered?)
In conclusion, a digital portfolio or brand is a collection of files (projects, documents, photos, videos, etc.) that you can use to reflect on your experiences and demonstrate your skills and accomplishments to potential employers, investors or customers.
A digital portfolio is a popular alternative to a traditional, paper-based portfolio because of it:
- Is easy and quick to update
- Can support multimedia file formats
- Is easy to share with people, even if they are not nearby
- Can be less expensive than paying for portfolio cases, paper, printing and photocopying
- Saves paper and is environmentally friendly
It takes time and effort to develop a digital portfolio. However, the process of developing and updating it will help you reflect on what you have accomplished and determine what you would like to do in the future.
Therefore a strong portfolio can help you to review and evaluate past experiences and learning, and present your knowledge, skills and accomplishments in a compelling, visual manner.
Work on Projects
We believe the best way to learn anything is by putting your knowledge into practice. Nothing says “I know this technique” like showcasing it in a project. In other words, building an end-to-end project gives you an idea about the different possibilities and challenges a data scientist potentially faces in a day-to-day role.
Building your Github Repository
Anything which shows your project code or programming skills is what Data Science internship recruiters most preferably look for. GitHub/Bitbucket/other or your own VCS/SVN is the place where you host your code. This is a convenient way to show all your code. You should also start building your GitHub profile at an early stage. This is essentially your data science resume which anyone in the world can access. Anything which shows your project code or programming skills is what Data Science internship recruiters most preferably look for.
Mistakes while learning for Data Science Internship/ Job
Spending too much time on theory.
Many beginners fall into the trap of spending too much time on theory. Whether it is math-related (linear algebra, statistics, etc.) or machine learning related (algorithms, derivations, etc.).
Coding too many algorithms from scratch.
This next mistake also causes students to miss the forest for the trees. At the start, you really don’t need to code every algorithm from scratch.
While it’s nice to implement a few just for learning purposes, the reality is that algorithms are becoming commodities. Thanks to mature machine learning libraries and cloud-based solutions, most practitioners actually never code algorithms from scratch.
Having too much technical jargon in your Data Science resume.
The biggest mistake many applicants make when writing their resume is suffocating it with technical jargon.
Instead, your resume should paint a picture and your bullet points should tell a story. Your resume should advocate the impact you could bring to an organization, especially if you’re applying for entry-level positions.
Overestimating the value of academic degrees.
Sometimes, graduates can overestimate the value of their education. While a strong degree in a related field can definitely boost your chances. It’s neither sufficient nor is it usually the most important factor.
Neglecting communication skills.
Currently, in most organizations, data science teams are still very small compared to developer teams or analyst teams. So while an entry-level software engineer will often be managed a senior engineer, data scientists tend to work in more cross-functional settings.
Top Companies That Offer Data Science Internships
- Google: To get an internship in Google, you can search for various internship portals or directly send your intern resumes to the HR.
- Amazon: Amazon is one of the top companies where data science students can apply for internships and training. Above all, the position of Amazon in the digital world is something which needs no clarification and an internship with them can totally give a kick start to your career and learning experience.
- Hewett Packard: Data Science internships at HP requires you to be from the discipline of data analytics or computer science. In addition to that adequate knowledge of various computer languages, engineering principles and methodologies, data and artificial intelligence services, and other basics are required to get through the screening process.
- Capital One: Capital One is innovating financial services through a number of initiatives. Data scientists at Capital One are responsible for helping customers solve financial challenges. You will be evaluating tools, integrating internal data with external data sources and designing visualizations to find insights.
- IBM: IBM offers a 12-week internship program not only for freshers but for people who wish to redirect their career. The program enables the interns to gain knowledge and expertise by working with the expert teams of on-going projects.
- Dropbox: Dropbox is looking for PhD level data science interns who will be responsible for security data analysis projects. You will work on designing algorithms and machine learning models to identify security risks.
More Companies Which Offer Data Science Internship
- Intel: Intel hires interns to assist their research scientists. As the interns work one-on-one with experts, the learning experience gets unique and helpful. Therefore such internships for freshers enhance their capabilities as a data scientist and provide good hand-holding in managing individual projects in the future.
- Yahoo: Yahoo, one of the most popular search engines, is totally investing to reach a new zenith. The work of a data scientist or intern at Yahoo typically involves analyzing customer/visitor information to provide better services to them. Therefore for interns, it’s one of the leading industrial research labs where you learn the basics of all things data.
- SAP: SAP’s Innovation Center is looking for data science interns to work on a number of strategic projects. You will be working in interdisciplinary teams to understand machine learning and artificial intelligence.
- Oracle: In this data science internship, you will be working for Oracle Data Cloud to implement machine learning. In addition to that, you will be working in cross-functional teams to find the best solutions to target media campaigns.
- Facebook: Facebook hires data science interns for its Menlo Park, California office. Some internships are specifically for those pursuing their Masters or PhD, while others require current undergraduate status.
- Twitter: Twitter recruits for interns during the year for its summer internships, with updates that can be found on their very own Twitter account called The Terns. All interns for Twitter have a hand in building a product that’s responsible for having a change in the world. You’ll have the opportunity to meet key leaders and innovators across the company and industries.
Responsibilities Given to a Data Science Intern
- Assist in Projects in Data science, including machine learning, big data, robotic process automation, data visualization and natural language processing
- Organize knowledge sharing presentation and demonstration to the team
- Propose innovative technical solutions and implement them using the latest technologies
- Improve the existing systems by using the latest technology
Role of a Data Science Intern Examples:
Sample of the role offered to a Data Science intern at Gresearch.co.uk
Our business is to predict the future of financial markets, applying scientific techniques to find patterns in large, noisy, and rapidly changing datasets. Therefore our mission as a team is to help discover, enrich and analyse data sources that will drive tomorrow’s research initiatives.
You will apply your knowledge to support the exploration, enrichment and even creation of new datasets for research. You will use your knowledge of analysis, and machine learning to help scale and automate the way we analyse and visualise data sources, and you will help build tooling to enable data science initiatives across the company.
This is a team at the forefront of the company’s data strategy, finding solutions to many important data challenges. The successful candidate will be comfortable working within a multi-disciplinary team, collaborating with industry-leading data scientists and financial experts, developing solutions that adapt to a rapidly changing data landscape.
However the role will offer exposure to cutting-edge technologies in a high growth industry, with opportunities to learn about multi-asset class systematic investing and big data development in an innovative and forward-thinking firm.
Also, Read Machine Learning Algorithms for beginners.
Sample Responsibilities from a Data Science Intern
Example of responsibilities taken from Microsoft
As a Data Scientist intern, you will help formulate approaches to solve problems using well-defined algorithms and data sources. You will incorporate an understanding of product functionality and customer perspective to provide context for those problems. You will use data exploration techniques to discover new questions or opportunities within your problem area and propose applicability and limitations of the data. In addition to that Successful Data Scientists will interpret the results of their analysis, validate their approach, and learn to monitor, analyze, and iterate to continuously improve.
You will engage with peer stakeholders to produce clear, compelling, actionable insights that influence product and service improvements that will impact millions of customers. As a Data Scientist, you will also engage in the peer review process and act on feedback while learning innovative methods, algorithms, and tools to increase the impact and applicability of your results.
Getting a Data Science Internship is no easy task. You will likely have put in months of hard work on your chosen career path.
For instance, it’s challenging to switch from an unrelated career to Data Science. As you need a mathematical background and other skill sets. Career switchers do best if they come from a related field such as programming or physics.
Of course, these things are always possible to learn but it takes time. Bootcamps are a great way to accelerate your learning but are no shortcut if you haven’t put in the legwork.
If you didn’t find what you were looking, then do suggest us in the comments below. We will be more than happy to add that.