Data Science is a rapidly growing field with numerous opportunities. And it’s fantastic that you’ve decided to dive headfirst into this field! The first step is to secure an internship with your ideal company. While doing online projects and courses is a great way to learn Data Science, an internship is essential. It gives you real-world industry experience and the opportunity to collaborate with experienced Data Science professionals. This can only benefit your job search, and who knows, you might even get an offer from the same company! So this article will teach you how to get your first Data Science internship.
What Technical Skills Do You Need for a Data Science Internship?
Let’s look at some of the skills required for a Data Science internship. Don’t be concerned if you aren’t an expert in these fields; this will come with time and experience. However, having some of these skills will only improve your chances of landing an internship!
1. Knowledge of Statistics and Probability
Statistical and Probability Skills are required for an internship in Data Science. That means you should understand the fundamentals of statistical analysis, such as statistical tests, distributions, linear regression, probability theory, maximum likelihood estimators, and so on. And that’s not all! While it is critical to understand which statistical techniques are appropriate for a given data problem, it is even more critical to understand which are not. Furthermore, there are many analytical tools that are extremely useful in Statistical Analysis, such as SAS, Hadoop, Spark, Hive, Pig, and so on, so you should be familiar with them.
2. Programming Skills
Programming skills are also required for obtaining an internship in Data Science. Python and R are the most commonly used data science programming languages, so you should be familiar with at least one of them. Python is popular due to its statistical analysis capabilities and ease of use. Pyt.hon also has a number of packages for machine learning, data visualisation, data analysis, and other data science-related tasks (such as Scikitlearn). R also makes it very simple to solve almost any Data Science problem
3. Machine Learning
You should also be familiar with fundamental Supervised and Unsupervised Machine Learning algorithms such as Linear Regression, Logistic Regression, K-means Clustering, Decision Tree, K Nearest Neighbor, and so on. Because the majority of Machine Learning algorithms can be implemented using R or Python libraries, you do not need to be an expert in them. However, knowing how the algorithms work and which algorithm is required based on the type of data you have is still beneficial.
4. Data Wrangling and Management
You must be knowledgeable in data management, which includes data extraction, transformation, and loading. This means gathering data from various sources, transforming it into the appropriate format for analysis, and finally loading it into a data warehouse. There are various frameworks available to handle this data, such as Hadoop, Spark, and others. Data Wrangling is an important part of Data Science because it involves cleaning and unifying data in a coherent manner before it can be analysed for actionable insights.
5. Communication Skills
Yes, this is not a technical skill, but it can help you stand out as a candidate for a Data Science internship! This is due to the fact that, while you understand the data better than anyone else, you must translate your findings into quantifiable insights for a non-technical team to aid in decision making. Data storytelling is another aspect of this. If you can present your data in a storytelling format with concrete results and an engaging story, your value will automatically increase.
Create a Digital Presence (Online Data Science Portfolio)
Projects must be accomplished.
I believe that putting your knowledge into practice is the best way to learn anything. Nothing says “I know this technique” like putting it on display in a project. Building an end-to-end project gives you an idea of the various possibilities and challenges that a data scientist may face on a daily basis.
Look for open source projects that are relevant to your field of interest. There is no shortage of data on the internet, believe me. I’m a huge fan of fiction, so I enjoy analysing the work of my favourite authors using NLP. This demonstrates your enthusiasm for data science and gives you an advantage in the eyes of your potential employer.
Here are a few practise problems to help you gain valuable experience.
Detecting credit card frauds
Detection of breast cancer
Detection of fake news
Forecasting web traffic
Uber data analysis
Climate change’s impact on food
Predicting forest fire
Gender and age detection
Detecting Parkinson’s disease
Create a GitHub Profile
At this point, you should also start building your GitHub profile. This is essentially your data science resume, which is accessible to anyone in the world.
To assess a candidate’s potential, most data science recruiters and interviewers look at his or her GitHub profile. While working on your projects, you can list the problem statement and code on GitHub at the same time. I’ve created a small checklist that you can use the next time you upload code to GitHub:
Include the problem statement.
Create a simple readme file.
Create clean code
Include comments in the code.
Include as many personal/course projects as you can.
If you’re at that level, contribute to open source projects.
I’ll reveal a big secret that helped launch my data science career: writing articles. When I’m learning a new concept, I make it a point to take notes. It’s simple to turn that into an article later. This helps me understand the technique much more clearly and lucidly.
You should follow suit! Our community is delighted to share their ideas and feedback with you. When you make your articles public, people frequently share their thoughts – for example, “adding a visualisation of actual vs predicted could be helpful,” which can help you improve.
Quora can be thought of as an alternative to blogging (which is where I first started writing). It aided in breaking down a complex topic into simple words.
Create and optimize your LinkedIn Profile
LinkedIn is the largest professional network on the planet. Even if you’re a recent graduate or still in school, you should be on it.
Recruiters frequently use LinkedIn to either verify your profile or contact you if an opportunity arises. Consider it your backup resume or a digital version of your paper resume. If you apply for an internship and your profile is not up to date (or does not exist), you may be disqualified.
Optimize your LinkedIn profile for the internship you’re interested in. Update your previous experience (if any), educational level, projects, and areas of interest. Make a profile now if you haven’t already. You should also start building your network by connecting with data science professionals.
Dos and Don'ts When Writing a Data Science Resume
Your resume is essentially a highlight reel of your professional career. Because it is the first thing a recruiter/hiring manager looks at, creating the perfect resume is critical in your quest for an internship.
Even if you have every skill listed in an internship’s requirements section, there’s a good chance you won’t be called in for an interview if your resume isn’t up to par.
You must, absolutely must, devote significant time to creating and perfecting your resume.
Get ready for your Data Science Internship Interview.
The interview process is undoubtedly the most difficult aspect of obtaining a data science internship. What aspects of your resume will the recruiter look at given that you have no prior work experience in this field? What skills should you highlight in your resume and during the interview?
Great questions! Knowing how to navigate these treacherous waters could mean the difference between getting the internship or not.
In the complex world of data science, the ability to structure your thoughts is a valuable skill. Your ability to break down a problem statement into smaller steps will be evaluated by the interviewer. The gold mine is in how you do it.
It is necessary to identify the end goal for any given problem statement. The next step is to comprehend the information provided and outline the steps necessary to achieve the desired outcome. And all of this takes place in a limited amount of time (the interviewer does not have all day!). Do you see why having a structured thinking mindset is so important?
To assess your structured thinking abilities, you will be asked a question such as, “How many emails are being sent right now?” That was the question I was asked during my interview. How many red cars are there on the road in Bangalore? How many cigarettes are sold in India per day?
Understanding of the Company to Which You Are Applying
You may believe that this point is irrelevant to the discussion. This isn’t necessary to mention because everyone reads the job description before applying. It’s an excellent point.
However, simply perusing the JD is insufficient.
Recruiters frequently tell us that prospective candidates arrive without having read about the role they are interviewing for. I’ve seen people start an internship and then quit after a few weeks because they didn’t like their job.
What will you discover during your internship?
What does an internship provide that textbooks, MOOCs, and videos do not?
When reviewing your profile, the hiring manager will value one thing above all others. During my internship with Vista Academy, I realised how useful this is.
You can learn a lot from your internship if you go in with an open mind and a willingness to learn every day. That is how you achieve success in data science!
How to Handle Real-World Problems
You would be working on a real-life project during your internship. This is invaluable experience. Once you’re on board, you might well find yourself entrenched in the end-to-end data science lifecycle, including defining the problem statement and building models.
If you previously participated in data science competitions, you will have an idea about the different challenges data scientists come across. But here’s the caveat.
The problem statements and the datasets provided in these competitions are very different from real-world scenarios. The datasets are messy and unstructured in the industry. There’s a ton of data cleaning work required before any model can be built.
In fact, don’t be surprised if 70-80% of your tasks involve data cleaning.
You will learn how to structure a problem statement, understand the domain and the data required to solve the problem, and then figure out sources to extract that data. The next step is to get knee deep into research. Find out the approaches other data scientists have taken to solve similar problems.
This will give you a fair idea about what should work well and what is not worth investing time on. While experiments are encouraged in data science, there’s a limit to how much creative freedom you’ll get from your manager. Filter out the aspects you know won’t work beforehand.