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Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientists role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.

Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions to uncover solutions to business challenges.

A data scientists work typically involves making sense of messy, unstructured data, from sources such as smart devices, social media feeds, and emails that dont neatly fit into a database.

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Experienced data scientists and data managers are tasked with developing a companys best practices, from cleaning to processing and storing data. They work cross functionally with other teams throughout their organization, such as marketing, customer success, and operations. They are highly sought after in todays data and tech heavy economy, and their salaries and job growth clearly reflect that.

Here are six common steps to consider if youre interested in pursuing a career in data science:

You will need at least a bachelors degree in data science or computer-related field to get your foot in the door as an entry level data scientist, although most data science careers will require a masters degree. Degrees also add structure, internships, networking and recognized academic qualifications for your rsum. However, if youve received a bachelors degree in a different field, you may need to focus on developing skills needed for the job through online short courses or bootcamps.

1QS World University Rankings (2020)

Data scientists may specialize in a particular industry or develop strong skills in areas such as artificial intelligence, machine learning, research, or database management. Specialization is a good way to increase your earning potential and do work that is meaningful to you.

Once youve acquired the right skills and/or specialization, you should be ready for your first data science role! It may be useful to create an online portfolio to display a few projects and showcase your accomplishments to potential employers. You also may want to consider a company where theres room for growth since your first data science job may not have the title data scientist, but could be more of an analytical role. Youll quickly learn how to work on a team and best practices that will prepare you for more senior positions.

Here are a few certifications that focus on useful skills:

Certified Analytics Professional (CAP)

CAP was created by the Institute for Operations Research and the Management Sciences (INFORMS) and is targeted towards data scientists. During the certification exam, candidates must demonstrate their expertise of the end-to-end analytics process. This includes the framing of business and analytics problems, data and methodology, model building, deployment and life cycle management.

SAS Certified Predictive Modeler using SAS Enterprise Miner 14

This certification is designed for SAS Enterprise Miner users who perform predictive analytics. Candidates must have a deep, practical understanding of the functionalities for predictive modeling available in SAS Enterprise Miner 14.

Academic qualifications may be more important than you imagine. When it comes to most data science jobs, is a masters required? It depends on the job and some working data scientists have a bachelors or have graduated from a data science bootcamp. According to Burtch Works data from 2019, over 90% of data scientists hold a graduate degree.

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Earn a Masters in Data Science online from UC Berkeley. Learn through a project-based curriculum.

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On any given day, a data scientists responsibilities may include:

Every company will have a different take on data science job tasks. Some treat their data scientists as data analysts or combine their duties with data engineers; others need top-level analytics experts skilled in intense machine learning and data visualizations.

As data scientists achieve new levels of experience or change jobs, their responsibilities invariably change. For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging. A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.

Data scientists dont need to just understand programming languages, management of databases and how to transpose data into visualizations they should be naturally curious about their surrounding world, but through an analytical lens. Possessing personality traits that resemble quality assurance departments, data scientists may be meticulous as they review large amounts of data and seek out patterns and answers. They are also creative in making new algorithms to crawl data or devising organized database warehouses.

Generally, professionals in the data science field must know how to communicate in several different modes, i.e to their team, stakeholders and clients. There may be a lot of dead ends, wrong turns, or bumpy roads, but data scientists should possess drive and grit to stay afloat with patience in their research.

Successful data scientists have a strong technical background, but the best data scientists also have great intuition about data. Are the features meaningful, and do they reflect what you think they should mean? Given the way your data is distributed, which model should you be using? What does it mean if a value is missing, and what should you do with it? The best data scientists are also great at communicating, both to other data scientists and non-technical people. In order to be effective at Airbnb, our analyses have to be both technically rigorous and presented in a clear and actionable way to other members of the company.

Lisa Qian, Data Scientist at Airbnb

Programming: Python, SQL, Scala, Java, R, MATLAB

Machine Learning: Natural Language Processing, Classification, Clustering,Ensemble methods, Deep Learning

Data Visualization: Tableau, SAS, D3.js, Python, Java, R libraries

Big data platforms: MongoDB, Oracle, Microsoft Azure, Cloudera

According to the Bureau of Labor and Statistics (BLS), employment growth of computer information and research scientists, which include data scientists, from 2019 to 2029 is 15%. Demand for experienced data scientists is high, but you have to start somewhere. Some data scientists get their foot in the door working as entry-level data analysts, extracting structured data from MySQL databases or CRM systems, developing basic visualizations in Tableau or analyzing A/B test results. If youd like to push beyond your analytical role think about what you could do with a career in data science:

Companies of every size and industry from Google, LinkedIn and Amazon to the humble retail store are looking for experts to help them wrestle big data into submission. In certain companies, new look data scientists may find themselves responsible for financial planning, ROI assessment, budgets and a host of other duties related to the management of an organization.

A data scientists salary depends on years of experience, skillset, education, and location. According to The Burtchworks Study, employers place greater value on data scientists with specialized skills, such as Natural Language Processing or Artificial Intelligence. The BLS claims skilled computer research and information scientists, which include data scientists, enjoy excellent job prospects because of high demand. Salary data below comes from 2019 data from the Bureau of Labor Statistics.

Data ScientistAverage Data Scientist Salary: $122,840 per yearLowest 10%: $69,990Highest 10%: $189,780

Senior Data ScientistMedian Sr. Data Scientist Salary: $171,755Total Pay Range: $147,000 $200,000

The first step to becoming a data scientist is typically earning a bachelors degree in data science or a related field, but there are other ways to learn data science skills such as a bootcamp or through the military. You may also consider pursuing a specialization or certification or earning a masters degree in data science before getting your first entry-level data scientist job.

Data scientists use a variety of skills depending on the industry they work in and their job responsibilities. Most data scientists are familiar with programming languages such as R and Python, as well as statistical analysis, data visualization, machine learning techniques, data cleaning, research and data warehouses and structures.

The time it takes to become a data scientist depends on your career goals and the amount of money and time you prefer to spend on your education. There are four-year bachelors degrees in data science available, as well as three-month bootcamps. If youve already earned a bachelors degree or completed a bootcamp, you may want to consider earning a masters degree, which can take as little as one year to complete. As shown in the aforementioned Burtch Works study, most data scientists do hold an advanced degree.

Tech bootcamps are a quick way to gain experience with data science and become knowledgeable in programming languages such as Python, R and SQL. Data science bootcamps are typically short programs offered in a variety of formats including part time, full time, online or on campus. Some bootcamps may take a couple of weeks to complete while others may take up to a couple of months. Bootcamps may help you expand your network and could offer dedicated career services to help with job placements after graduation.

During the bootcamp, youll work on projects and create a portfolio to demonstrate your abilities to potential employers. Data science bootcamps typically cover a variety of topics such as machine learning, natural language processing, different types of data analytics, data visualization and more.

When researching bootcamps, it is important to consider your career goals and what youd like to get out of the program. Some bootcamps are geared toward beginners, while others are better suited for those with some programming or computer science experience. You may also want to consider the background of the instructors teaching the bootcamp as well as cost. Are you able to take time off and commit to a full-time immersive experience? Does the bootcamp offer scholarships or discounts? Make sure to ask about all of your financing options.

Last updated: March 2021

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What is a Data Scientist? - Master's in Data Science