How to Become a Data Scientist
I do not have a computer science or engineering form of academic background but i have an undiluted passion for working or playing around the computer. I studied Business Administration B.Sc but i dont see myself filing documents, overseeing business protocols or sort of office administration. I asked myself this questions am I looking for a career which can be applied to multiple industries and can lead to a vast array of interesting and unique projects, the one that wouldnt make my Business Admin B.sc irrelevant and then i got an answer "yes". Sooner i realised i need look no further than data science.
Harvard Business Review dubbed data science as “the sexiest job of the 21st century”, data science is a field that drives innovation, feeds your creative spark, and has the ability to illuminate the world around us. These characteristics, plus the above-average compensation the job provides, are likely the main contributing factors that make data science rank highly on the 25 Best Jobs for Work-Life Balance each year.
Jobs Data Scientists Actually Do
There are so many definition about and debate about the job of a data scientist, largely because the requirements for data scientists vary greatly depending upon their chosen industry focus. The guardian answers the question in their article What's a data scientist and how to become one?:
“More than anything, Data scientists utilize their knowledge of statistics and modeling to convert data into actionable insights about everything from product development to customer retention to new business opportunities. In a competitive terrain where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.”
Some jobs a data scientist might be asked to perform include:
Data Scientists
There are data scientists who fine-tune the statistical and mathematical models that are applied onto data. When somebody is applying their theoretical knowledge of statistics and algorithms to find the best way to solve a data science problem, they are filling the role of data scientist.When somebody builds a model to predict the number of credit card defaults in the next month, they are wearing the data scientist hat.A data scientist will be able to take a business problem and translate it to a data question, create predictive models to answer the question and storytell about the findings.
Statisticians that focus on implementing statistical approaches to data, and data managers who focus on running data science teams tend to fall in the data scientist role.
Data scientists are the bridge between the programming and implementation of data science, the theory of data science, and the business implications of data.
Skills You’ll Need: Knowledge of algorithms, statistics, mathematics, and broad knowledge of programming languages such as R and Python. Broad knowledge of how to structure a data problem, from framing the right questions to ask, to communicating the results effectively.
Salaries: Data scientists need to have a broad set of skills that covers the theory, implementation and communication of data science.
Inventing new algorithms to solve problems and new tools to automate work.Data scientists, then, are more than simplynumbers crunchers. Forbes explains:
“They understand statistics and applied mathematics. They can test hypotheses with experiments they design. They know enough programming to engineer methods for sourcing, processing, and storing their data. And they communicate their findings through data visualizations and stories.”
Different Types of Data Scientists
The designation “data scientist” is actually an umbrella term under which exist a variety of different, specialized descriptive types. SaaS guru Tom Tunguz divides data scientists into a few recognizable categories. Some of them are:
Quantitative, exploratory data scientists: These data scientists combine theory and exploratory research to improve products. Typically, data scientists of this type have PhDs and may have strong backgrounds in physics or machine learning.
Operational data scientists: Working in fields like finance, sales, or operations, these data scientists have a strong background in analytics and statistics. They may concentrate in areas such as business intelligence, defining patterns and trends and using predictive analytics to produce actionable insights.
Product data scientists: These professionals focus on understanding the ways users interact with a product and finding ways to improve or enhance the product accordingly. They work closely with or act as product managers and engineers.
The field of data science, then, covers a huge amount of ground, running the gamut from analysts who use business intelligence tools to physicists writing code for innovative technologies such as self-driving cars and the like.
In this video, a Facebook data scientist describes his job:
Common Personality Traits among Data Scientists
Albert Einstein said two things that epitomize the personality traits needed to become a successful data scientist. First, he said: “It’s not that I’m so smart. It’s just that I stay with problems longer.” In a similar vein, he observed: “I have no special talent. I am only passionately curious.”
Successful data scientists are master problem solvers. Their curiosity to know, to explore, and to get to the bottom of an issue are character traits that define the best data scientists, no matter the industry in which they work. Sean McClure, Director, Data Science at Space-Time Insight, makes this observation:
“Instead of listing languages and tools in an attempt to engineer your future go solve a problem. Go solve a hundred problems. Then take a look at the list of skills you have; the languages you know, the technologies you’ve mastered, and the approaches you take. Your career will always be a byproduct of the challenges you’ve tried to solve.”
Data scientists also need to be good communicators. They must be able to take highly complex information and communicate it in a way that is easy both for technically-savvy and technically-challenged audiences.
Common Skills and Educational Requirements for Data Scientists
Skills you may need for becoming a data scientist include:
- Math skills such as linear algebra, calculus, probability, and statistics
- Machine learning tools and techniques
- Software engineering skills
- Database management skills
- Languages and applications such as Python, R, SQL, Java, C, C++, SPSS, Tableau, and Hadoop
Paysa examined job postings for data scientists across multiple industries. Below is a quick chart of the top skill prerequisites found among those job listings:
There are 3 common educational paths for data scientists:
Degrees and graduate certificates provide structure, internships, networking and recognized academic qualifications for your résumé. Majors that dovetail nicely into common data science careers include: statistics, mathematics, economics, operations research, and computer science.
MOOCs and self-guided learning courses allow you to complete projects on your own time, but they require you to structure your own academic path. Choosing this method of learning requires you to do your own networking when it is time to find a job.
Bootcamps may be taught by practicing data scientists and may be a quick way to acquire some of the skills you need. The bootcamp model is based on experiential learning, and it does present some opportunities to network to help you with job placement.
Generally, to get the kind of position you want as a data scientist, having a degree is the preferred course. Paysa compiled hiring information for top data scientist positions. Here are the results of Paysa’s research regarding educational requirements:
Professional Associations and Organizations for Data Scientists
Professional organizations for data scientists include:
- Data Science Association
- International Institute for Analytics (IIA)
- International Machine Learning Society (IMLS)
- Institute for Operations Research and the Management Sciences (INFORMS)
- SIGKDD
Rock Stars of the Data Science World
Here are a couple of well-known names in data science today:
Hilary Mason: Founder of Fast Forward Labs, Data Scientist in Residence at Accel Partners, and former Chief Scientist at bitly, Mason is known for turning “big data into plain English”. Check out this short video of Mason in action.
Peter Norvig: Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google’s core search algorithms group, and of NASA Ames’s Computational Sciences Division, making him NASA’s senior computer scientist. Check out this TED Talk with Norvig as speaker.
Data Scientist Salaries
Just as the job description of data scientists changes according to industry, so too the compensation for data scientist jobs changes. Based on 2,740 profiles gathered by Paysa, the average base salary for data scientists is $116,550 per year, ranging from $84,642 to $150,707. The average market salary for data scientists is $161K per year, ranging from $89.2K to $242K, which includes $117K base salary, $24.5K annual bonus, $15.1K signing bonus and $53.4K annual equity.
Top locations for data scientists, along with base and market salaries, are:
How to Make Wise Career Decisions with Data
As all these stats indicate, it pays to do some research when considering a data scientist job. Paysa is a great resource because it can be personalized to give you specific skills and job recommendations, as well as salary data to help you negotiate a job offer or promotion with confidence. You can check out information on current data scientist positions here.
One happy data scientist and Paysa user from Atlanta, GA reports: “Paysa helped me understand my market value by supplying real data. There are hundreds of conflicting posts about the salaries of data scientists and this platform helped me out through the noise. It helped me realize that the first salary I took in the field was about 10 percent lower than market, and it helped me negotiate a 13.5 percent raise after my first year!”
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