The data scientist role has exploded in recent years, as smartphones, computers and IoT devices produce exponential amounts of data.
In fact, according to a study from Indeed, demand for the role has increased by 29 percent year over year. Unfortunately for businesses, job searches by aspiring data scientists only increased by 14 percent.
This rift presents a huge opportunity for IT professionals interested in learning more about data analytics. And the need for data scientists will only continue to grow as businesses strive to glean deeper insight from their growing collections of data.
No matter where you are in your IT profession, you can develop the skills to become an in-demand data scientist and drastically improve your earning potential.
This blog offers actionable tips and steps to help you understand the role and how to become a data scientist.
What Is a Data Scientist?
Data scientists pull together and analyze large amounts of information to determine actionable next steps for business decision-makers.
To discover these answers, data scientists might put the data onto a chart or other visual context to help demonstrate patterns that wouldn’t be identifiable within a spreadsheet. Or, they could create algorithms that can find the patterns within seemingly disparate numbers.
Companies like Amazon and Netflix use data analytics to determine what users would be interested in purchasing next. Using the data from your previous viewing or purchase history — based on the information they’ve gathered from customers with similar habits as your own — they’ve developed an algorithm to offer personalized recommendations.
Data science focuses on hard facts and data gathered using computers and technology. It uses digital means to manipulate and determine the outcomes of this data.
While data science is still new, the average salary for a data scientist is $117,345, according to Glassdoor. This number is expected to grow as data scientists become linchpins for organizations to compete in a more personalized world.
Career Path to Become a Data Scientist
Now that you know what a data scientist is, you might be interested in learning how to set foot in this role. Here are four steps to take to become a data scientist:
1. Understand the Required Data Scientist Skills
There are several important skills you need to obtain before you can become a data scientist.
To start, any data scientist needs to be able to communicate his or her findings to non-technical teams. Marketers, sales professionals and C-Suite executives might need to know about your findings to implement next steps within the company as quickly as possible. But they can’t perform this job function if you can’t communicate with them.
Along the same lines, data scientists should be able to work well with others. You’ll be working with everyone involved with the company — from business executives to product managers to customers — to help create use cases and solve problems with the insights gleaned from your data.
Beyond soft skills, data scientists also need hands-on technical skills in the following areas:
Hadoop: Similar to Apache Spark, Hadoop is also a data computation framework. Data scientists frequently run into situations where they have so much data that they don’t have enough room on their system or they need to send that data to another server.
Hadoop can easily transition this data or keep it on your machine without damaging your system memory. Hadoop can also help explore data sets, filter data, find data samples and craft a data summary.
SQL Database and Coding: Structured query language, or SQL, is a programming language that can help you add, remove and pull data from a database. SQL can also perform analytical functions and help you build database structures.
As a data scientist, knowing SQL is a great asset. The language was created to help with finding and assessing data, and its commands were designed to make complicated queries easy to perform.
Python Coding: Python is one of the most popular coding languages for data scientists. It’s versatile, and it can integrate with SQL to streamline your work even further. Because it can take on various data formats, Python can work within almost all steps you have to take when analyzing information.
Apache Spark: Apache Spark is a big data computation framework that caches computations in memory. It was developed to help data scientists quickly run complicated algorithms. The system can help distribute massive amounts of data, and it can handle complex, unstructured data sets — all without losing data at any point in the process.
Machine Learning and AI: The data gathered for data science is typically used to help with machine learning and artificial intelligence. But you can also use machine learning to help you compile and gain insights into the data you’re working with.
Machine learning can analyze your data and create predictions. But too few data scientists have skills in machine learning and AI. Learning techniques like decision trees, logistic regression, natural language processing and recommendation engines will help you stand out above other data scientist candidates.
2. Earn a Degree or Gain Experience Through Training
Some schools offer degrees in data science, which can teach you the skills needed to understand statistics, computers and how to gather and evaluate datasets. But data science is still a relatively new field, so you might have to look at other technical or computer-based degrees instead.
Computer science, applied math, statistics, economics and related degrees can all help you gain the range of skills needed for data science.
Non-technical degrees, such as psychology, political science and business, can also give you a boost in understanding core parts of the data science career. Look to them as a minor, rather than a major.
If you already have a degree, however, you can supplement your knowledge with IT courses that provide real-world data science experience and tactical training.
3. Obtain Entry-Level Data Science Job
Businesses need to fill their entry-level data science roles quickly. When you start searching, look for jobs as a Junior Data Scientist or Junior Data Analyst to get your foot in the door.
Some companies may still need you to take some on-the-job training courses to get a better idea of the specific systems they use, and additional training or certifications can help you even as you’re looking for beginner roles.
4. Earn Relevant Data Scientist Certifications
Certifications can help you secure an entry-level data science role, and many companies require them. Earning vendor-certified credentials can also position you to earn a higher salary and instill you with more confidence when entering the job market. And, as with any role in technology, data science is constantly evolving, so refreshing your knowledge through continued education will keep you at the top of your game in this job. Here are two certifications that will help you on your journey:
Microsoft Certified Azure Data Scientist Associate Certification: This credential will help you navigate Microsoft Azure, one of the leading cloud platforms available today. Before you gain this certification, you also have the option of taking the Microsoft Certified Azure Fundamentals certification to learn the basics of the platform. Browse relevant Azure Data Scientist Associate training here.
MCSE: Data Management and Analytics Certification: This certification will show potential employers that you're skilled in SQL administration, business intelligence data analysis and creating enterprise-sized data solutions using both on-site servers and cloud solutions.
MCSE stands for Microsoft Certified Solutions Expert. To gain access to this certification, you need to be certified as a Microsoft Certified Solutions Associate (MCSA) in a data-related course first. Browse relevant MCSE: Data Management and Analytics training here.
Prepare to Become a Data Scientist With New Horizons Training
Now that you know how to become a data scientist, all you have to do is take the first step. New Horizons can help you by providing the training and certifications that will move you toward your new career.
Ready to learn more? Check out our big data training courses to get started, or contact a training specialist to find out more!