How to Become a Data Scientist
Big Data keeps getting bigger. There will be 200 zettabytes of data worldwide by 2025, according to estimates from cyber economy expert Cybersecurity Ventures.
To put that in perspective, most smartphones have a minimum storage capacity of 128 gigabytes of data. It would take more than 7.8 million smartphones with 128 GB of storage capacity to store a single zettabyte of data.
The variety of data-generating devices is vast―from smartphones and smart home devices to infrastructure and satellite systems. For those interested in how to become a data scientist, the explosion of Big Data means ample career opportunities in an exciting and growing field. The U.S. Bureau of Labor Statistics (BLS) projects a 31.4% growth rate for data scientist positions by 2031. With a median annual salary of $100,910 in 2021, according to the BLS, it’s no surprise that people are interested in data science as a career.
It’s important to note that data science encompasses a wide variety of disciplines, such as data analytics, data preparation, data mining, artificial intelligence (AI), machine learning, and predictive analytics. This means that multiple career paths can lead to being a data scientist.
What Does a Data Scientist Do?
Data scientists find insights in data to reveal business opportunities. This means the role encompasses a mixture of competencies, including that of a businessperson, mathematician, and computer scientist. Take, for example, how data scientists can use Big Data to help solve big business problems. By looking at historical information, a data scientist can find gaps in business processes and uncover solutions that can lead to revenue gains.
While responsibilities and daily activities vary by industry and focus, the ability to collect data is foundational for data scientist roles. A closer look into how to become a data scientist reveals that beyond data collection, another critical skill is processing data into a usable form.
Unstructured data that hasn’t been organized into a usable format offers little value. Data scientists work to structure data to look for patterns and trends. Here, they use data analytics to identify the data’s value in driving decisions to optimize business strategies, uncover and fill knowledge gaps, and generate increased revenue.
Data scientists also apply mathematics, build statistical models, and run simulations to make forecasts based on insights from past data. These insights often require artificial intelligence and machine learning applications, as sorting through volumes of raw data using manual techniques can be intensely time consuming.
In addition to their math and computer science expertise, data scientists must be able to communicate what data insights mean to a business. Because data can be difficult for non-technical individuals to understand, data scientists must have strong communication and presentation skills to effectively share with business leaders what the data means and how it may inform business success.
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What Skills Does a Data Scientist Need?
Every data scientist’s toolbox combines technical, analytical, and business competencies. However, data scientists also need foundational skills such as programming and analytical skills, knowledge of statistics, and access to communication tools.
One of the most common programming languages for data scientists is Python, which is used for quickly creating and managing data structures. Additional programming skills include knowledge of Perl, C/C++, SQL, and Java.
To discover trends or patterns in data, data scientists rely on analytical techniques like machine learning and text analytics. They also use statistics, an interdisciplinary approach to collecting and analyzing large volumes of data.
Data scientists should also know how to use data visualization tools to help identify patterns and communicate insights to decision-makers.
How to Become a Data Scientist: Career Paths
It is common for data scientists to have at least a master’s degree, often in computer science. However, everyone has a different career journey. Some may pursue education in computer science, while others might begin in a field such as business or economics.
Three common career paths in data science include the following.
Data analytics focuses on collecting, analyzing, and reporting data. A good analytics bootcamp can open the door to an entry-level position as a junior analyst. That can lead to a role as a business data analyst, which with experience can be a gateway to a role as a senior data analyst. After about a decade of experience, some individuals can move on to become a director of data analytics or chief data officer.
Business analysts guide strategic company decisions using insights from data. The role of the business analyst is centered on leveraging data to promote sound business decision-making. A common next step in the career journey is senior operations/finance analyst. Ultimately, a business analyst with about 10 years of experience can acquire the high-level strategic skills to become a data scientist or chief data officer.
Students with a business or economics degree can begin their careers as business consultants and later acquire data analytics skills through a specialized bootcamp. Combining a business and economic background with analytics skills can position them to move onto the role of risk management analyst. With experience, this path can lead to a role as a data modeler architect and even a chief data scientist.
Find Your Own Path to Becoming a Data Scientist
If you’re keen to find out how to become a data scientist, finding a path that works best for you is the first step. An intensive program like the online Fullstack Academy Data Analytics Bootcamp can be a launchpad toward a career as a data scientist.
Find out more about how Fullstack Academy can help you pursue a career in data.