What is a Data Scientist

Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s 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 scientist’s work typically involves making sense of messy, unstructured data, from sources such as smart devices, social media feeds, and emails that don’t neatly fit into a database.

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Technical skills are not the only thing that matters, however. Data scientists often exist in business settings and are charged with communicating complex ideas and making data-driven organizational decisions. As a result, it is highly important for them to be effective communicators, leaders and team members as well as high-level analytical thinkers.

Experienced data scientists and data managers are tasked with developing a company’s 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 today’s data and tech heavy economy, and their salaries and job growth clearly reflect that.

Steps to Become a Data Scientist

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

  1. Pursue an undergraduate degree in data science or a closely related field
  2. Learn required skills to become a data scientist
  3. Consider a specialization
  4. Get your first entry-level data scientist job
  5. Review additional data scientist certifications and post-graduate learning
  6. Earn a master’s degree in data science

How to Become a Data Scientist in 2021

1. Pursue an undergraduate degree in data science or a closely related field

You will need at least a bachelor’s 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 master’s degree. Degrees also add structure, internships, networking and recognized academic qualifications for your résumé. However, if you’ve received a bachelor’s degree in a different field, you may need to focus on developing skills needed for the job through online short courses or bootcamps.

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2. Learn the required skills to become a data scientist

  • Programming
  • Machine Learning techniques
  • Data Visualization and Reporting
  • Risk Analysis
  • Statistical analysis and Math
  • Effective Communication
  • Software Engineering Skills
  • Data Mining, Cleaning and Munging
  • Research
  • Big Data Platforms
  • Cloud Tools
  • Data warehousing and structures

3. Consider a specialization

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. 

4. Get your first entry level job as a data scientist

Once you’ve 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 there’s room for growth since your first data science job may not have the title data scientist, but could be more of an analytical role. You’ll quickly learn how to work on a team and best practices that will prepare you for more senior positions.

5. Review additional data scientist certifications and post-graduate learning

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.

6. Earn a master’s degree in data science

Academic qualifications may be more important than you imagine. When it comes to most data science jobs, is a master’s required? It depends on the job and some working data scientists have a bachelor’s 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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Master of Science in Applied Data Science

Syracuse University’s online Master of Science in Data Science can be completed in as few as 18 months.

  • Complete in as little as 18 months
  • No GRE scores required to apply

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Master of Science in Data Science

Earn your MS in Data Science at SMU, where you can specialize in Machine Learning or Business Analytics, and complete in as few as 20 months.

  • No GRE required.
  • Complete in as little as 20 months.

School of Information info

Master of Information and Data Science

Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.

  • Complete in as few as 12 months
  • No GRE required

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Data Scientist Responsibilities

On any given day, a data scientist’s responsibilities may include:

  • Solving business problems through undirected research and framing open-ended industry questions
  • Extract huge volumes of structured and unstructured data. They query structured data from relational databases using programming languages such as SQL. They gather unstructured data through web scraping, APIs, and surveys.
  • Employ sophisticated analytical methods, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
  • Thoroughly clean data to discard irrelevant information and prepare the data for preprocessing and modeling
  • Perform exploratory data analysis (EDA) to determine how to handle missing data and to look for trends and/or opportunities
  • Discovering new algorithms to solve problems and build programs to automate repetitive work
  • Communicate predictions and findings to management and IT departments through effective data visualizations and reports
  • Recommend cost-effective changes to existing procedures and strategies

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.

Characteristics of a Successful Data Scientist Professional

Data scientists don’t 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.

–Lisa Qian, Data Scientist at Airbnb

Required Skills for a Data Scientist

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

Data Science Job Outlook

According to the Bureau of Labor and Statistics (BLS), employment growth of computer information and research scientists, which include data scientists, from 2020 to 2030 is 22%. 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 you’d 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 GoogleLinkedIn 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.

Data Scientist Salary

data scientist’s 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 Scientist
Average Data Scientist Salary: $126, 830 per year
Lowest 10%: $72,210
Highest 10%: $194,430

Senior Data Scientist
Median Sr. Data Scientist Salary: $194,430
Total Pay Range: $190,000– $200,000

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Data Scientist Career FAQ:

How do I become a data scientist?

The first step to becoming a data scientist is typically earning a bachelor’s 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 master’s degree in data science before getting your first entry-level data scientist job.

What skills are needed to be a data scientist?

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.

How long does it take to be a data scientist?

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 bachelor’s degrees in data science available, as well as three-month bootcamps. If you’ve already earned a bachelor’s degree or completed a bootcamp, you may want to consider earning a master’s 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.

Learn more about Data Science Bootcamps to get a Data Science Job

Tech bootcamps are a quick way to gain experience with data science and become knowledgeable in programming languages such as PythonR 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, you’ll 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 you’d 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