MASTER’S IN DATA SCIENCE
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.
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 typically have over ten years of experience and 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 steps to consider if you’re interested in pursuing a career in data science:
- Pursue an undergraduate degree in data science or a closely related field
- Learn required skills to become a data scientist
- Consider a specialization
- Get your first entry-level data scientist job
- Review additional data scientist certifications and post-graduate learning
- Earn a master’s degree in data science
How to Become a Data Scientist in 2019 - Pursue an undergraduate degree in data science or a closely related field
You will need at least a bachelor’s degree in data science, mathematics, statistics, computer science to get your foot in the door as an entry level data scientist. 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. - 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
This list is always subject to change. As Anmol Rajpurohit suggests in his guide to becoming a data scientist, “generic programming skills are a lot more important than being the expert of any particular programming language. - Consider a specialization
In demand data scientists typically 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. According to the Burtchworks Study, entry data scientists working in the tech industry earn an average salary of $85,143 and senior data scientists working for consulting firms earn an average salary of $158,462.
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.
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.
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. However, as Burtchworks notes, data scientists typically have a graduate or advanced degree in a quantitative discipline. The Burtch Works study also shares that most data scientists have an advanced degree, either a master’s or Ph.D.
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 the difference between data scientists and statisticians
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.
“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
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 by 2028 is 16%, over three times faster than the national average. 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:
• Data/Big Data Engineer
• Data/Big Data Architect
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. 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
A 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. West coast Data scientists earn the highest average salary and entry level data scientists can expect to earn at least $90,000. The BLS claims skilled computer research and information scientists, which include data scientists, enjoy excellent job prospects because of high demand.
Data Scientist
Average Data Scientist Salary: $118,370 per year
Lowest 10%: $69,230
Highest 10%: $183,820
Senior Data Scientist
Median Sr. Data Scientist Salary: $171,755
Total Pay Range: $147,000 – $200,000
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