From personalized education, to cleaning the oceans, or transforming healthcare – data science is poised to continue revolutionizing how businesses operate by producing actionable insights from data. Digitalization has made it easier for companies to collect data about their internal processes, but this data can’t be used to its full potential without data scientists.
But what exactly is data science and how does one become a data scientist?
What is Data Science?
A data scientist combines business acumen and expertise in statistics to use programming and machine-learning techniques to extract meaningful, actionable insights. These insights — based on numbers, statistics, and trends from data — are used to make decisions towards achieving a specific business goal. What makes data science so exciting is its variety of uses. Each project presents a unique set of questions that might lead to a different technology roadmap.
Data science’s integration across various industries means that data scientists are required to have a diverse set of skills. In spite of that, there are many data science internships and entry-level opportunities available because the field is still young, and this number will only continue to grow as businesses continue to accumulate data.
Getting a Data Science Internship
If you have the foundational skills — namely, proficiency in programming languages and SQL— in addition to a passion for learning, a great way to get your foot in the door is to intern at a company. An internship will teach you how to organize data projects and provide work experience that can lead directly to full-time positions.
If you’re interested in learning about what a data science internship might entail, read on for an overview of the tasks and challenges common to data science projects.
Understanding the Problem
Data science is a varied field. You might work on building a credit-score rating model for a fintech company, or a recommender system for a streaming website. On another day, you could be tasked with using data to develop a campaign to increase sales for an eCommerce store. Whatever the domain, you likely won’t know much about it at the beginning.
During your internship, you’ll quickly notice that there’s sometimes no clear answer to specific questions or in deciding on what techniques to use. This is why it’s crucial to have a solid understanding of the problem and the domain you’re working in. The stakes are high on real-world data projects, so there is no room for unsupported assumptions about the problem.
Your data science internship will also teach you about collaboration. In most cases, there’s no universally applicable solution to a given problem; each solution entails different trade-offs and only the specifics of the problem and its context will dictate which solution is optimal. For example, if another team is awaiting your analysis to start working, a quick but imprecise analysis might be necessary and bring greater value than a precise but slow one. Being an effective problem-solver requires understanding the interests of all project stakeholders.
You should take considerable time to understand the given problem, learn about the domain, and ask relevant questions. Only upon the clear definition of the business goals can you dive into the data.
Working With Data
The 80/20 rule of data science says that data scientists spend 80% of their time finding and cleaning data and the remaining 20% on generating insights. Learning settings often try to emulate the complexity of data retrieval in real-world environments, but this complexity can easily be understated; there are a multitude of ways in which real-world data can be disorganized.
You might need to retrieve data from multiple spreadsheets, pull it from a data warehouse, collect it from an API or even scrape it from the web. While these aren’t difficult tasks, they can be quite time-consuming. Improving your data-wrangling know-how will considerably speed up your training in data science.
Once data is stored in a single location, you’ll need to preprocess, explore, and transform it to make it actionable. Data scientists use the expression “garbage in, garbage out” to emphasize the critical nature of preprocessing — a nonsensical input will result in a nonsensical output. Using preprocessing, standardization, and visualization techniques, you’ll discover trends and hidden patterns in the data, which will set the direction in which the project will develop.
These tasks will require an understanding of SQL and a proficiency in one programming language, usually Python or R, along with the respective data-manipulation and data-visualization ecosystems — tools like pandas, matplotlib, and ggplot2. On the job, you’ll also probably get to work with bash, data warehouses, big-data tools, and cloud services.
There are many intermediate steps between data ingestion and deploying a machine-learning model. Your data science internship should provide you with a solid understanding of the entire data pipeline. That’s not to say that you’ll need to work through all the tasks yourself, but that to be a well-rounded data scientist, you’ll need to understand the gamut of roles and tasks within the data space.
Solving the Problem
After data preprocessing, you’ll need to create a model to make predictions about the future of business operations. For many data scientists, building models is the most interesting part of the job; it’s also the one they might spend the least amount of time on. This involves an iterative process of prototyping, testing, and fine-tuning until your model achieves the desired predictive power.
Many people enter this field because they want to build state-of-the-art neural networks and work on cutting-edge technologies. Don’t be discouraged if you find that your work revolves around building simpler machine-learning models such as linear regression. Older machine-learning algorithms still have a lot of untapped potential, and unlike deep learning, they offer interpretability that’s often worth the sacrifice in performance.
Modeling requires a working knowledge of the typical machine-learning stack: Jupyter notebooks, scikit-learn, caret and XGBoost, for general-purpose machine-learning tools and algorithms; Tensorflow and Pytorch, for deep learning; and various tools for tracking experiments, packaging models and orchestration that you’ll learn on the job.
Your data science internship should teach you how to systematically organize machine-learning projects. This might also include deploying your model into production, although this is frequently performed by a DevOps team.
Communicating Results
The science in data science comes from the scientific method, the last step of which is communicating one’s findings. Apart from being able to learn on the fly, the ability to create a compelling story is the most important non-technical skill; no matter how strong your analysis, your company won’t get any value from it if you fail to properly sell it to the decision-makers. To effectively explain their findings, data scientists must account for their audience and adjust their language and use of technical terminology accordingly.
While you might not be given the chance to manage stakeholder relationships as a data science intern, you should seek out opportunities to watch and learn from your seniors.
Summary
In this article, we described the typical data science workflow with the aim of demystifying it for those looking to enter the field. If you’re interested to learn more about the concepts covered in this article, or want to dive right into working on practical data science projects, click here to Learn to Become a Data Scientist Online.
