How to Prepare for a Data Science InternshipMay 4, 2022
Are you looking for a career with great growth potential? Do you enjoy math, statistics, pattern recognition, or science classes? If you answered yes to any of these questions and are looking for a career on the forefront of the trending future, then data science could be the career path for you.
If you stop to think about it, data is involved in pretty much everything we do throughout our day. As we live in the age of technology, it’s no surprise that data science has become more prevalent. Take the internet for example. If you were to take two different individuals searching for the same topic online, they won’t get the same results. This is because of data science at work. When you type a search term into Google, data is being created. This data is then collected from many outlets and is used to personalize all forms of media that are delivered to you based on your search history, location, age, and myriad other factors.
What Is Data Science?
So, what exactly is data science? The term “data science” was first coined in 2008. Data science is defined as preparing data for analysis, including aggregating and manipulating data to make informed decisions, and meaningful business insights based on the patterns and trends. In a recent study, IBM predicted 2.7 million open jobs in the field of data science. This career is one of the fastest growing careers of the decade with a 39 percent growth in demand and with a base salary of or more than $100,000 in the United States in 2020, the potential for growth in this field is limitless.1
Before you set out on your journey to becoming a data scientist, let’s take a moment to understand what they do. Here are some of the areas data scientists specialize in: data visualization, marketing data analysis, machine learning, engineering, data mining, statistical analysis, and the list can go on. The best way to think of data science is a multidisciplinary approach to find stories, insights, and patterns from large data sets.
What Does A Data Scientist Actually Do?
If you’re still wondering what a data scientist actually does on a daily basis role, here are a few helpful examples:
Data visualization: One example of a data scientist role is data visualization, or graphic representation of data and information. This could include creating graphics, such as graphs, charts, or maps to visually display information. The graphic would illustrate and explain patterns, trends, key findings, and other outliers from the data that was analyzed.
For example, Data visualization can be used in many different respects. Let’s say you’re trying to find the common color palettes of great artists to use for an art project. You would take data from many artists in history and find out what colors each artist prefers (or if there are commonalities) according to the data collected. Data science can even help you take things a step further and pull data from the consumers to see what colors they prefer.
Machine learning: Another example is machine learning. Machine learning automates the process of data analysis and makes data-informed predictions without any human intervention. A data model is built automatically and further trained to make real-time predictions. This is where the machine learning algorithms are used in the data science life cycle.3
These are just two examples, but the point is data science can be used anywhere, so be creative!
What Will it Take to Become a Data Scientist?
If you’re wondering what you need to do now to prepare for a career as a data scientist, you might be wondering if you need a specific degree or major. One of the great things about pursuing data science is that you don’t need a specific degree, but you will need the right hard and soft skills to be considered. This is why an internship in data science is a great way to start out, so you can gain experience that helps prepare you for the future.
College degree: When choosing a bachelor degree in a data science program, there are a few factors to consider. First and foremost, what are your interests and career goals? Do you want a more general degree or are you interested in a specific research area? For example, maybe you want to specialize in marketing analytics about computer products. Or maybe you love math and want to work with numbers to help companies with their budgets. Find out if your desired program offers the specialization you want. If you’re unsure if you want to major in data science, you can look up universities that offer data science as a major and a minor and check out what types of courses are included.
What Skills Are Necessary?
Here are a few helpful skills that are necessary to have or focus on in your data science internship:
Communication: Communication and being able to draw conclusions from complex information is important. A data scientist will need to present their findings to businesses and explain what their analytics/insights can bring to the business.
Creativity: Talented data scientists have a sense of creativity, not necessarily painting a picture, but the ability to think outside of the box to find a solution to a problem.
Curiosity: Intellectual curiosity is key. This will lead you to look for answers to business problems. Be curious enough to unlock solutions and dig deeper into data to uncover insights to help the business.
Business understanding: As a data scientist, you will have to deal with a colossal amount of knowledge and information, and you may also have to meet with or present findings to high-level executives. Being business savvy and professional will also help ensure your success.
Critical thinking: Critical thinking is also a key element for a data science career. Critical thinking skills mean being able to analyze problems objectively while interpreting data, find solutions to complex problems, and ask questions to learn more about a topic.
There are also some common “hard skills” that are helpful:
Scripting languages: Python., R., SQL., Java., are the most common programming languages, but the list goes on. In a worldwide survey, it was found that 83% of the almost 24,000 data professionals used Python.
Computer programming and databases: Basic knowledge of computers and computer programming is important for any future data scientist.
Experience in SaaS like AWS: SaaS or “software as a service” knowledge is also helpful. AWS stands for Amazon Web Services, which is a huge cloud computing platform that can be used in many different cases, offering over 200 fully featured services from data centers globally. AWS is the computing powerhouse that drives the technology behind many modern companies such as the government to gaming.
Now that you understand the definition of data science and what skills you need, you might be wondering what’s next. An internship is a great way to dive in and discover if a career in data science is ideal for you. Most internships are typically unpaid, as you will be learning and receiving valuable hands-on experience. However, there are also plenty of paid options available. Check out more information on internship salaries here.
How to Land a Data Science Internship
So, how do you land an internship in data science? Apart from having some of the helpful soft and hard skills for becoming a data scientist, here are some ways to help set you apart from the competition.
If you don’t have any work data-related work experience yet, a great way to show off your skills is to set up a side project that peaks your interests. This will show businesses or hiring managers what you are capable of creating, and in addition shows how passionate you are. Also, highlight the high school classes or any extra-curricular events you’ve attended that relate to data science.
As with all business adventures, marketing and networking can be very valuable. Try to get out there, write blog posts on new concepts you’re interested in, participate in extracurricular activities and community events outside of school, share ideas on social networks, and get involved. Showcase your work to the world!
Creating a Portfolio for Data Science Internship
Finally, create a resume, LinkedIn profile, or even a website that shares information about yourself, your interests, and your experience. There are many free platforms out there that make this process fairly simple. Create your own website using one of the many platforms out there like Squarespace or WordPress.
Your website should include the following:
- Home Page
Your Home page should be welcoming, introducing yourself and your portfolio.
- About Page
Your About page show allows visitors to get an idea of who you are and what your story is. Be mindful that prospective employers may check out your social media pages to learn more about you.
Some questions you can answer about yourself could be:
What excites you about data science?
How did you get into data science?
Share interesting facts about yourself.
- Skills Page
Be sure to attach your resume
Skills Page can include technologies you are proficient in.
- Work/Project Page
This is your portfolio. This is why people came to your page: they want to see your work. Show all of your best projects, and explain the details that went into these projects.
- Contact Page
Your Contact page will direct users to contact you if they would like to. Include an email address or phone number here.There are so many different potential career paths to pursue when it comes to data science, the sky is really the limit! Having a background of knowledge and experience from an internship will help set you up for success as you choose an area of study for college, select which college or university to attend, and think about your career path ahead.
THE QUANT CRUNCH – IBM
What Is Machine Learning? A Definition. – Expert.ai