Data science plays a vital role in virtually every industry — marketing, retail, finance, health care, technology, social media, travel and more. Using data, a company can strategize marketing campaigns and gather valuable customer insights. Data science can even be used to predict trends and consumer demand for specific products and services.
This multidisciplinary field aims to harness critical knowledge by organizing and analyzing large data sets. Blending science, mathematics and technology, data scientists create data analytics-driven information that can be applied to business decisions and projections.
Not surprisingly — considering the immense value and competitive advantage that data science gives companies — data science careers are some of the most in-demand in the country right now. Companies of all sizes, in every sector, have a growing need for data scientists to join their ranks.
For those wondering how to become a data scientist, the best place to start is to explore an advanced education in data science or business analytics. This can supply the necessary foundational knowledge in big data, applied data science and other critical areas to enter the field.
What Does a Data Scientist Do?
In a broad sense, data scientists find valuable and actionable insights contained in large sets of data. However, how they go about finding those insights is a complex process that calls on technology, math and science to work in concert. To capture the valuable information they’re looking for, data scientists develop, create and oversee systems and algorithms designed to arrange data in meaningful ways and extract useful insights.
According to IBM, the process of turning raw data into actionable insights can be boiled down to five key steps, known as the data science life cycle.
- Capture the data: All data (structured or unstructured) is captured from all relevant sources.
- Prepare and maintain the data: During this step, all the captured data is cleaned and formatted. This is the most time-consuming portion of the process. It involves removing duplicates, correcting misspelled attributes and identifying missing values. Once the data is clean, it is put into a format that is consistent with machine learning, analytics or deep learning models.
- Process the data: The cleaned data is then carefully examined to identify ranges, patterns, biases and distributions in values within the data. This step determines the data’s sustainability regarding predictive analytics, deep learning algorithms and/or machine learning.
- Analyze the data: The data is analyzed using predictive analytics, statistical analysis, machine learning, regression and deep learning algorithms. This step is where the data scientist or analyst will be able to extract valuable insights.
- Communicate findings: The insights gleaned from the data are translated into reports and/or charts that are easily digestible. They are then passed along to key decision-makers for review.
To perform their jobs, data scientists use a host of tools including dashboards, data visualization programs and other software. Typical data science programming languages include Python, R and Java. Additionally, data scientists use big data processing platforms like Apache Hadoop and Apache Spark. For visualization, they use tools including Microsoft Power BI and Tableau.
Difference Between Data Scientists vs. Data Analysts
Although data science and data analytics are closely related, data scientists and analysts fill different roles. Data scientists are focused on finding meaningful insights or correlations in large data sets. Data analysts, on the other hand, focus on the specifics of what’s been extracted by data scientists.
Steps to Become a Data Scientist
With so many companies relying more and more on big data, many job seekers in the analytics field are wondering how to become data scientists. According to SimpliLearn, the digital economy skills company, all data scientists need to achieve the following:
- Learn the databases that store and analyze data. Applicable tools include SQL, Oracle and Teradata.
- Learn probability, statistics and mathematical analysis. Many data scientists have a degree in one of these fields.
- Master at least one programming language, such as R or Python.
- Learn how to clean, manipulate and organize data.
- Become proficient in machine learning and using algorithms.
- Become proficient in using big data tools. Examples include Hadoop, Apache Spark and Tableau.
- Learn to effectively communicate findings by turning data insights into charts, graphs and diagrams.
Since probability, statistics, and mathematics are a big part of data science, it’s recommended that job seekers pursue an undergraduate degree in a related field. According to the data science information source Towards Data Science, the top undergraduate degrees for aspiring data scientists are:
- Computer science
These degree programs teach the analytical and problem-solving skills that are necessary to break into the field of data science. Once a student earns an undergraduate degree, they should further their education with an advanced degree, such as a Master of Science in Business Analytics. The advantage of a degree program like this is that it covers both the data science side and the business side of the profession, providing candidates with a dual skill set that employers value in hiring data scientists.
Data Scientist Salary and Job Outlook
The future for data scientists in the job market looks incredibly bright. Employment opportunities for computer and information research scientists are projected to grow by 22% between 2020 and 2030, which is much faster than the national job growth average, according to the U.S. Bureau of Labor Statistics (BLS).
Additionally, the BLS reports the median annual wage for computer and information research scientists was approximately $131,500 in 2021. This salary figure represents a baseline. Education, experience, job location and the type of employer are all factors that can influence the precise salary a candidate may earn.
Pursue an Increasingly Critical Career
With the growing relevance of data science across different industries, the BLS is anticipating tremendous job growth for the role of data scientists. Data scientists who understand both the analytical and business elements of the job are poised to have the most successful careers. This is why the University of Nevada, Reno’s online Master of Science in Business Analytics program is an ideal fit for those aiming to become data scientists.
The program offers a comprehensive curriculum that includes courses such as Leadership Strategies, Big Data, Predictive Modeling and Data Mining, Applied Data Science and more.
Begin your journey to becoming a data scientist by acquiring the business analysis expertise to jump-start your career success.
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