How to Become an Entry-Level Data Scientist

Are you ready to enter the world of data science? If so, you’ll need to start with acquiring entry-level data scientist skills. Learn more here.

entry level data scientist

With 2.7 million open jobs, 39% growth in employer demand, and median salaries of $130,000, data science is one of the most attractive career fields today. Even if you’ve never thought about data science as the path for you, here’s some good news: Two-thirds of data scientists transitioned out of another occupation. That means it’s totally plausible to build a successful career as a data scientist at an entry level.

What is data science in simple words?

Every time you browse the Internet, shop at a store or watch a TV show, you create data. Data also captures external phenomena, like electricity consumption, sea levels, and traffic conditions. Data can be structured - already living in a row-column database, but more often than not, it is unstructured, coming from sources like email logs, social media hashtags, and insurance claims. 

Simply put, data science is the art of processing large, complex data, managing datasets (collections/files containing the data), and extracting meaningful insights that organizations can use in decision-making.

This diverse career field blends statistics, mathematics, computer science, predictive analytics, data mining, and machine learning. 

Does data science require coding?

Organizations look for data science candidates with a wide variety of skills, not only solid programmers; however, many job descriptions list knowledge of a technical language as a prerequisite. Coding allows you to manipulate big data and build scalable systems. For example, Python has over 100,000 libraries that increase your capabilities to perform computations, plot trendlines, and conduct complex analyses. 

For non-programmers, GUI-based tools, like Tableau and VBA, require less coding, but allow you to round out your repertoire of business acumen and storytelling skills.

For those who enjoy data but are less interested in the programming side, “data analyst” is also a viable first career option. Learn more here about the differences between these two tracks and how you can move from a data analyst to a data scientist

What percentage of time in a data science project is spent preparing the data?

In the past, around 80% of data scientist time was spent importing and cleaning data, but now with advanced machine learning tools, more time is spent analyzing data at scale and using it to inform decision-making. Data scientists currently spend about 45% of time on data preparation, with the rest of the day dedicated to data visualization and historical or predictive modeling. 

Source: 2020 State of Data Science: Moving From Hype Toward Maturity

What do data scientists spend the most time doing?

Data science is a very intellectually stimulating career path, in part because it is interdisciplinary with many professional development opportunities. Although a day-in-the-life can look very different depending on your project, team, and business goals, here are some common responsibilities: 

  1. Data preparation. Data scientists need to make sure that the company has accurate data sources and that the data is in a usable format (e.g. dates in the same year/month/day structure). These data sources can be internal to the company, like receipt or transaction data, or external, showing market or weather conditions. 
  2. Experimentation. Should we invest more in marketing channels for new user acquisition? How would customers in a certain demographic react if we introduce a new product? Data scientists are involved in finding data-driven answers to business problems, figuring out everything from forecasts of pricing changes to customer segmentation based on purchasing trends. This can range from A/B testing to more advanced statistical tests. 
  3. Data analysis. Did the test or control cohorts respond better to the promotion? What is the time of the heaviest restaurant traffic and did locations in certain cities underperform at peak hours? Once the company has rolled out an experiment or pilot, data scientists need to dig into the details to uncover trends. Usually, this involves manipulating the data with SQL, Python, and R to spot patterns.
  4. Data visualization. Often, the last step is to translate quantitative insights into a visually appealing story that can influence stakeholders. Tools like Tableau, Datawrapper, Infogram, and Google Charts are useful for producing graphs and other graphics. If you can tell a compelling story, your work is more likely to make a larger impact within the firm and result in major operational or product changes. 
  5. Machine learning. Machine learning trains machines to make independent decisions based on patterns in inputs. Activities can involve supervised learning, unsupervised learning or reinforcement learning, depending on whether the datasets are labeled prior to analysis. Although you may not need advanced machine learning as an entry-level data scientist, as you specialize, you will want to learn concepts related to big data,  such as decision trees, logistic regression, recommendation engines, and natural language processing.

How do you get started as an entry-level data scientist?

If you’re searching for your first data science job, here are some helpful tips:

  1. Build your toolkit. Data scientists require a mix of soft and hard skills, and many people come in with certain key characteristics, like curiosity, creativity, detail orientation, independence, collaboration, and a quantitative bent (link to “Who Can Become a Data Scientist?” article). Some of the top technical skills include mathematics like probability, linear algebra, multivariate calculus, and statistics; computer science and programming languages; open-source software tools like Hadoop; SQL for data management; and data visualization techniques. You can acquire these skills through online or in-person courses, bootcamps or degree programs, as well as through self-study options
  2. Network. Top data science jobs are very competitive, especially during 2020, with many highly qualified applicants in tech hubs like San Francisco, Seattle, New York, and Washington DC. Cultivating relationships across industries and seeking professional expertise—whether through informal coffee chats and meetups or attending conferences and events—builds strong interpersonal and communication skills and can help you screen for which companies may be a strong cultural fit. 
  3. Craft your online presence. In addition to updating your LinkedIn profile, build a project portfolio to share how you’ve applied your data science skills in real-world settings. Learn more about how to build a data science portfolio here.
  4. Get ready for the interview. You can often peruse sample questions on Glassdoor or through a simple Google search. Practicing on Leetcode and other sites will prepare you to pass the technical portion. 
  5. Land the job and find a mentor. Although historically women have only held 27% of data jobs, many organizations are pushing for more inclusion, fair employment practices, and a culture of openness in the workplace. Equal opportunity employers cannot discriminate with regard to national origin, veteran status, disability, sexual orientation, age, or gender identity, so don’t worry if you don’t fit the traditional profile of a data scientist. Mentors who believe in you as a person can be valuable at every stage of the process, from job applications and warm intros to companies with diverse environments and progressive leadership, to salary negotiations. They can also provide advice on how to advance within an organization, network internally, and prepare for promotions or new growth opportunities. 

For those looking for their next career challenge and infinite experiences to learn and develop, Springboard has fantastic data science courses, mentorship, and programs to help you break into this up-and-coming industry and put forward your best work. Get a headstart today!

Is data science the right career for you?

Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.

Check out Springboard’s Data Science Career Track to see if you qualify.

Not quite ready to dive into a data science bootcamp?

Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.

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