Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, manage and organize them. Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges.
What is a data scientist?
Found at the cross section of business and information technology, a data scientist is a professional with the capabilities to gather large amounts of data to analyze and synthesize the information into actionable plans for companies and other organizations. Data scientists are analytical data experts who utilize their skills in both technology and social science to find trends and manage the data around them. With the growth of big data integration in business, they have evolved at the forefront of the data revolution.
On any given day, a data scientist is a mathematician, a statistician, a computer programmer and an analyst equipped with a diverse and wide-ranging skill set, balancing knowledge in different computer programming languages with advanced experience in data mining and visualization.
Technical skills are not all that count, however. Data scientists often exist in business settings and are charged with making complex 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. They are highly sought after in today’s data and tech heavy economy, and their salaries and job growth very clearly reflect that.
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Data Scientist Responsibilities
“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician”. – Josh Wills on Quora
On any given day, a data scientist’s responsibilities may include:
- Conduct undirected research and frame open-ended industry questions
- Extract huge volumes of data from multiple internal and external sources
- Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
- Thoroughly clean and prune data to discard irrelevant information
- Explore and examine data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
- Devise data-driven solutions to the most pressing challenges
- Invent new algorithms to solve problems and build new tools to automate 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 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.
An Interview with a Real Data Scientist
We caught up with Lisa Qian, Data Scientist at Airbnb, to find out what it’s like to work as a data scientist. Read on to learn about the impact data science has on Airbnb’s success, the programming languages they use on the job, and what students need to know in order to succeed.
A: Successful data scientists have a strong technical background, but the best data scientists also have great intuition about data. Rather than throwing every feature possible into a black box machine learning model and seeing what comes out, one should first think about if the data makes sense. 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 answers to these questions differ depending on the problem you are solving, the way the data was logged, etc., and the best data scientists look for and adapt to these different scenarios.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.
Data Scientist Salary
In Glassdoor’s 50 Best Jobs in America, as of March 2019, data scientist is ranked number one! According to The Burtch Works Study, 40% of data scientists work on the West Coast and earn a median base salary of $102,500 – about 13% more than their Northeast peers.
Average Data Scientist Salary: $117,345 per year
Median Data Scientist Salary: $91,3693 per year
Total Pay Range: $63,000 – $130,000
Senior Data Scientist
Median Sr. Data Scientist Salary: $126,129 per year
Total Pay Range: $89,000 – $163,000
Data Scientist Qualifications
1. Pursue an undergraduate, graduate, or certificate in data science or closely related field.
Broadly speaking, you have 3 education options if you’re considering a career as a data scientist:
- Degrees and graduate certificates provide structure, internships, networking and recognized academic qualifications for your résumé. They may also cost you more time and money.
- Self-guided learning courses are free/cheap, short and targeted. They allow you to complete projects on your own time – but they require you to structure your own academic path.
- Bootcamps are intense and faster to complete than traditional degrees. They may be taught by practicing data scientists, but they won’t give you degree initials after your name.
Academic qualifications may be more important than you imagine. As Burtch Works notes, data scientists typically have a graduate or advanced degree in a quantitative discipline. The Burtch Works Studies also shares that most data scientists have an advanced degree, either a master’s or Ph.D.
Note: Check out our list of 23 Great Schools with Master’s Programs in Data Science.
2. Learn required skills to become a data scientist.
- Programming Languages: Python, R, SAS
- Machine Learning Tools
- Data Visualization and Reporting
- Risk Analysis
- Statistics and Math
- Effective Communication
- Software Engineering Skills
- Data Mining, Cleaning and Munging
- Big Data Platforms
- Cloud Tools
This list is always subject to change. As Anmol Rajpurohit suggests, “generic programming skills are a lot more important than being the expert of any particular programming language.
3. Review additional data scientist certifications and post-graduate learning.
Here are a few certifications that focus on useful skills:
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.
The EMCDS certification training will enable you to learn how to apply common techniques and tools required for big data analytics. Candidates are judged on their technical expertise (e.g. employing open source tools such as “R”, Hadoop, and Postgres, etc.) and their business acumen (e.g. telling a compelling story with the data to drive business action).Once you’ve passed the EMCDS associate level exam, you can consider the Advanced Analytics Specialty. The certification training works on developing new skills in areas such as Hadoop (and Pig, Hive, HBase), Social Network Analysis, Natural Language Processing, data visualization methods and more.
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.
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 to 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.
Data Science Job Outlook
Some data scientists get their start working as low-level data analysts, extracting structured data from MySQL databases or CRM systems, developing basic visualizations 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 Google, LinkedIn and Amazon to the humble retail store – are looking for experts to help them wrestle big data into submission. There are many different types of data scientist jobs, but even as demand for data engineers surges, job postings for big data experts are expected to remain high. There are also some indications that the roles of data scientists and business analysts are beginning to merge. 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.
Professional Organizations for Data Scientists
Some technology organizations may hold conferences or workshops that focus on analytics, big data or data science. These organizations are specifically focused on data science, research, and/or machine learning.