Data science is an immensely attractive and lucrative field. Data scientists get to work on a plethora of interesting projects and get generous compensations for their work. What’s more, people in this field learn new things every day – it makes their job interesting and motivates them to show up for work. It is a significant reason why more and more people are trying to enter this field, so much so, that it is arguably the most competitive field out there.

Becoming a data scientist is not easy. Students go through a lot of trials and tribulations as they have to learn a vast amount of complex theories, ensuring that they are fit for this field. Do you want to have a career in data science? If the answer is yes, you will need an internship to show that you are a good fit. In most cases, getting an internship position can take time due to the plethora of people in this industry.

However, it doesn’t mean that getting a data science internship within thirty days is impossible. This article will discuss everything you need to know about landing a data science internship, ensuring you don’t have to wait for a long period.

data science

Familiarize Yourself with Data Science Terminology

Having a clear, detailed understanding of data science is essential before applying for internee positions. The last thing any company wants to do is hire someone who does not speak their language. So, it would be best if you looked at the various roles in this industry and learned about the common terminologies used in it.

You should also focus on the specific tasks you aspire to work on and learn its various technical details, but before doing so, ask yourself – why do you want to work in this field in the first place? Is it because of your passion for programming, statistics, and math? Or is it because machine learning and data science are highly popular and trending fields?

Remember, your purpose behind becoming a data scientist should be authentic, as people who join it just because this field is popular often lose interest very quickly.

Understanding Data Science

Organizations are generating a tremendous amount of data on a daily basis, and this will increase exponentially in the coming years. The ability to store and collect data from numerous sources is significantly better compared to the last decade. Companies have a plethora of techniques and tools to mine data patterns for collecting useful insights, and that is precisely what data science is.

To simplify things, data scientists use various techniques and methods to create predictive models and understand data for making business decisions. Some popular data science applications include airline route planning, sports analytics, fraud detection, and other similar activities.

So, if this field is about finding patterns and deriving insights from data, what makes data scientists different from statisticians? Well, let us find out!

Difference between Data Scientists and Statisticians

Statisticians and data scientists utilize data for deriving useful insights. Statisticians focus on determining the relationship of the data, whereas data scientists utilize those relationships while building models for predicting future outcomes. What’s more, data scientists create generalized models with maximum accuracy.

Statisticians tend to utilize tools lie MATLAB OR excel, since they contain numerous data analysis libraries. On the other hand, data scientists use Apache Spark, Python, and similar platforms to build models and explore data.

Popular Data Science Technologies

As mentioned earlier, data science is a vast and complex field.  Understanding its various components could help you narrow your focus, ensuring you don’t face much trouble when searching for a job or internship.

Machine Learning

Machine learning uses algorithms like decision trees, logistic regression, and linear regression in order to learn from data. These algorithms are quite helpful when making informed decisions, for instance, utilizing the past data of loan receivers to predict whether or not they will return for more loans.

Natural Language Processing

NLP is a data science branch that deals with deriving, understanding, and analyzing info from text based data. Companies often use natural language processing to understand people’s sentiments from their comments on various online platforms. NLP is arguably the most popular data science field these days.

Deep Learning

Deep learning is a machine learning subset, designed for making human-like decision making abilities. This could include, identifying an image’s objects, or classifying whether an image has a dog or cat.

Recommendation Engines

Anyone who uses Amazon, Flipkart, Wish, or other e-commerce platforms gets regular product recommendations. Do you know how that happens? Well, the answer is: Recommendation engines. This data science technology thoroughly analyzes the user’s past behavior to provide them with relevant suggestions or recommendations.

Computer Vision

The computer vision technology helps machines see and comprehend their surroundings. This data science technology is becoming quite popular in social media platforms like Facebook (for suggesting picture tags.) You may also find computer vision used in self driving cars for detecting objects on roads. Like the technologies mentioned above, computer vision is also becoming an in demand field, with thousands of people looking for internships and full time jobs.

data science machine learning internship

What you Need to Land a Data Science Internship

Having the aforementioned information in mind is one of the best ways to set yourself up for success in the data science field. It will help you choose a line that suits you and minimize confusion. Internships are great for aspiring data scientists with minimal to no experience in the industry.

They provide you a chance to obtain industry experience by letting you work with veteran data scientists, ensuring you are ready for a career in this field. While internships are for people who are new to a particular field, they should have particular skills and attributes to qualify for it. Here is a list of must-have qualities every aspiring data scientists should have to land a decent data science internship.

Understanding Probability and Statistics

Probability and statistics are critical skills that every aspiring data scientists should possess. Without a firm understanding of these, making headway in this field is impossible. From analyzing data and making critical inferences to understand how a model operates, the fundamental concepts of probability and stats are abundantly present in data science’s vast ecosystem.

We can make use of a variety of probability distributions and statistical techniques o understand any data’s structure. Here is a list of essential topics you will use while working on data science problems:

  • Descriptive statistics
  • Standard and variance deviation
  • Mean, Median, Mode
  • Probability
  • Normal Distribution
  • Central Limit Theorem
  • Probability Mass Function and Bernoulli trials
  • Inferential Statistics
  • Correlation
  • Hypothesis Testing
  • Confidence Interval

While all of these topics are immensely important, interviewers often tend to ask questions related to probability and statistics. Therefore, it would be best if you paid special attention to it, making sure you ace your internship interview.

Competent Coding Skills

Programming is a critical part of data science and you should be well-versed with it. Sure, automated machine learning is slowly making its way in the industry but you should never underestimate the importance of cold hard manual coding skills.

R and Python are the most common programming tools used in data science. Being familiar with one of these tools could do you a world of good for your internship. Why? Because these open source programming languages have a massive community that is increasing with each passing day.

R and Python

Data scientists mainly use R for tasks with statistical analysis and exploratory work because it has a massive library base for stat related packages. However, people prefer python for deep learning and machine learning tasks as it has a vast range of deep and machine learning packages and libraries.

That said, Python’s demand is significantly higher compared to R. It is a particularly easy choice if you want to understand advanced machine learning and deep learning. What’s more, the flexibility Python is second to none when it comes to machine learning tasks.

However, R serves a purpose as well, and is perfect for exploratory analysis, which includes producing visually pleasing and insightful plots.

Fundamental Machine Learning Algorithms

Once you become familiar with the fundamentals of probability and statistics, and improve your coding skills, consider learning machine learning basics. It would be best to familiarize yourself with popular machine learning algorithms such as k-nearest neighbor, naïve bayes, random forest, decision tree, logistic regression, and linear regression etc.

You should also try understanding every algorithm individually, ensuring you comprehend each technique’s intuition. Possessing a theoretical knowledge about algorithms and their operation is as essential as implementing them. The process to understand the algorithm’s parameters will become significantly easier if you understand how the algorithm operates. It could also help you choose the right algorithm for the right data type.

Create Your Online Profile

Learning the concepts mentioned above is quite difficult and time consuming. Therefore, you should complement your effort by showcasing your knowledge and skills the right way. Contrary to popular belief, stats, machine learning, and programming alone, will not be enough to land you an internship. You will; need to develop your digital presence, showcasing your potential and demonstrating the skills you gained.

Let us look at a few things you can do to improve your digital presence:

Participate in Projects

The best way to understand anything is by using your knowledge and putting it into practice. Showcasing your skills in a data science project is one of the best ways to get noticed and increasing your chances of getting hired. Working on an end to end project is a great way to help you understand the different challenges and possibilities data scientist face on a regular basis.

Consider looking for open source tasks relevant to your preferred field. You will find no shortage of options on the internet and finding a project that really speaks to you wouldn’t be that hard.

Start Your GitHub Profile

GitHub profiles are a great way to improve your chances of getting hired. It will essentially act as your data science CV that can be accessed by anyone. A large number of data science interviewers and recruiters look at GitHub profiles to evaluate a candidate’s potential.

You can also list down code and problem statements on your GitHub profile, showing people that you have a deep passion and understanding of data science. Here is a checklist you should consider following when adding code to your GitHub profile:

  • Enter clean codes
  • Create a readme file and make sure it is clear
  • Don’t forget the problem statement
  • Work on open source projects


Writing articles can propel your data science career to new heights. Most individuals who experienced success in this field have loads and loads of blogs to expand their online presence. It is one of the best ways to get noticed by the right people, making sure you attract numerous organizations. Create a habit of jotting down notes whenever you are trying to understand a new concept.

You can convert these notes into articles and post it on various online platforms. It is a great way to showcase your skills, while also making sure you help others in this field. Quora, Reddit, and LinkedIn are great places to post your articles, as these platforms have massive audiences. Alternatively, you can also start your own blog on WordPress, and help people looking for various data science solutions.

Don’t Neglect Your LinkedIn Profile

LinkedIn is a massive network full of people from different fields. You don’t have to be a professional to create a profile on this platform as it also welcomes graduates and freshers. Recruiters and interviewers often use LinkedIn to reach out to people for internship opportunities. So, it would be best if you treated LinkedIn as your digital resume, making sure you don’t miss out on interesting job call-ups.

Remember, merely creating a LinkedIn profile is not enough; you also have to optimize and update it on a regular basis. Furthermore, you should also network with people in your industry and connect with people that could help you out.

data science jobs

Craft Your Resume the Right Way

Your resume or CV is essentially the highlight reel of your career. Every recruiter will take a detailed look at your resume to determine whether you are the right candidate for the internship. Therefore, you should leave no stone unturned when it comes to creating an impressive, professional resume.

Sure, you may have every skill present in the requirement section of a particular internship, but it doesn’t guarantee an interview call if your resume doesn’t stand out. So, what should you do? Well, you should start by spending a decent amount of time perfecting your C.V. Make sure it is up to date and is free from spelling and grammatical errors. Checking the C.V twice would also help, and ensure it is up to the mark.

Prepare for Your Internship Interview

The interview process is arguably the biggest challenge faced by people applying for a data science internship. This is especially true for people who do not have much experience in this field and are applying for their first internship. Knowing how to handle tricky interview questions could help you ace the interview, making sure you get the position.

Do not forget to mention your completed projects or the ones you are working on. However, make sure you have enough information to talk about these projects in detail as interviewers often tend to cross check people to find out whether they are telling the truth.

In most cases, internship applicants go through a screening process (usually conducted by hiring managers or recruiters on phone.) This procedure gives interviewees a better understanding of their potential role, while also helping interviewers determine if the applicant is worth calling for a one on one meeting. While the screening process is quite short, you should take it seriously and show genuine interest in joining the company’s internship program.

Steer Clear from Procrastination and Apply Now

Landing a data science internship within thirty days might seem difficult, but it is not impossible, especially when you follow the tips and advice discussed in this piece. Do not worry if you lack experience, as that is precisely why you are applying for the internship. It will help you develop some skills and gain knowledge about pursuing a career in this field.

If you are looking for a data science internship, consider yourself lucky as many companies are not hiring people for direct experience. Why? Because most of them are well aware of the fact that most applicants do not have loads of experience, especially if they are fresh graduates. Instead, they hire people for particular traits that could help them succeed in the long run.

Companies can train people when it comes to hard skills but developing soft skills is something applicants should do by themselves. So, if you think you are ready to step foot in the data science landscape, consider applying for it right away and take advantage of any opportunity you come across. With thousands of people entering this field, make sure you have a decent academic qualification, skills, and knowhow to impress hiring managers.

Also, do not get demotivated if you don’t get any responses. Instead, keep working on your skills and C.V, making sure you are ready when an opportunity does come around.

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