Becoming a data scientist is a relatively new career trajectory that merges statistics, business logic, and programming knowledge. Given the exponential amount of data being churned out via our smartphones, desktops, and the vast array of IoT devices throughout the world, governments and private enterprises are interested in gleaning insight from their extensive data collection processes. At first glance, one may assume that data analysts and data scientists are interchangeable – meaning there is a mutual one-to-one correspondence between the two, but this is not the case.
While data scientists can (and do) perform data analysis, they do so within the realm of building and deploying predictive models which often incorporate machine learning and deep learning protocols. Data scientists must also have a meta-level understanding of which models are the best fit for the data being analyzed. Since all models are approximations of current and future environments, they require fine-tuning which, in turn, relies on the data scientists’ mathematical expertise. Although data scientists are not data engineers, they should (ideally) have some knowledge of how databases are constructed, and how to pull data from an organization’s preferred database management system (DBMS). Due to the extensive knowledge requirements, including academic and professional training and/or experience, companies, research organizations, and governmental agencies are scrambling to find qualified data scientists.
This guide provides a basic overview of some of the opportunities in this emerging field and lists the steps required to become a data scientist. For informational purposes, a detailed job description, salary information, and the data science job outlook are included.
Five steps to become a Data Scientist
Step 1: Preparation
Future data scientists can begin preparations before they even step foot on a university campus or launch themselves into an online degree program. Becoming proficient with the most widely used programming languages in data science such as Python, Java, and R — and refreshing their knowledge in applied math and statistics — will help aspiring data scientists get a head start. In fact, entering college with an already established skillset frequently improves a student’s learning rate. But, also, early exposure to data science knowledge requirements is helpful for determining whether a data science career is the right fit.
Step 2: Complete undergraduate studies
The most sought-after majors for data science are statistics, computer science, information technologies, mathematics, or data science (if available). Minoring in one of the aforementioned fields is also recommended. Continue to learn programming languages, database architecture, and add SQL/MySQL to the “data science to-do list.” Now is the time to start building professional networks by looking for connections within college communities, look for internship opportunities, and ask professors and advisors for guidance.
Step 3: Obtain an entry-level job
Companies are often eager to fill entry-level data science jobs. Search for positions such as Junior Data Analyst or Junior Data Scientist. System-specific training or certifications in data-related fields (e.g., business intelligence applications, relational database management systems, data visualization software, etc.) might help when looking for entry-level data science jobs.
Step 4: Earn a Master’s Degree or a Ph.D.
Data science is a field where career opportunities tend to be higher for those with advanced degrees. The in-demand graduate degrees for data science include the exact same specifications for an undergraduate degree: data science (if available), computer science, information technology, math, and statistics. However, many companies also accept STEM degrees such as biotechnology, engineering, and physics (among others). Also keep in mind that data scientists need to understand how to use enterprise-grade data management programs and how distributed storage and computation operate (e.g., Hadoop, MapReduce, and Spark) in relation to model building and predictive analytics.
Step 5: Get promoted
Additional education and experience are key factors that lead to being promoted or becoming a data scientist in high demand. Businesses value results. Coupling strong technical skills with project management and leadership experience will generally chart a course towards more significant opportunities and higher compensation.
Never Stop Learning
Staying relevant is crucial to the ever-evolving field of data science. In this age of constant technological innovation, continuing education is a hedge against shifts in the career market. This is also the case for data science since the field isn’t as established as other statistically and technologically focused careers. A career-oriented data scientist is always learning and evolving with the industry. Continue to network and look for educational and professional development opportunities through boot camps and conferences.
While data scientists can (and do) perform data analysis, they do so within the realm of building and deploying predictive models which often incorporate machine learning and deep learning protocols. Data scientists must also have a meta-level understanding of which models are the best fit for the data being analyzed. Since all models are approximations of current and future environments, they require fine-tuning which, in turn, relies on the data scientists’ mathematical expertise. Although data scientists are not data engineers, they should (ideally) have some knowledge of how databases are constructed, and how to pull data from an organization’s preferred database management system (DBMS). Due to the extensive knowledge requirements, including academic and professional training and/or experience, companies, research organizations, and governmental agencies are scrambling to find qualified data scientists.
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| How to Become a Data Scientist Skills Career |
This guide provides a basic overview of some of the opportunities in this emerging field and lists the steps required to become a data scientist. For informational purposes, a detailed job description, salary information, and the data science job outlook are included.
Five steps to become a Data Scientist
Step 1: Preparation
Future data scientists can begin preparations before they even step foot on a university campus or launch themselves into an online degree program. Becoming proficient with the most widely used programming languages in data science such as Python, Java, and R — and refreshing their knowledge in applied math and statistics — will help aspiring data scientists get a head start. In fact, entering college with an already established skillset frequently improves a student’s learning rate. But, also, early exposure to data science knowledge requirements is helpful for determining whether a data science career is the right fit.
Step 2: Complete undergraduate studies
The most sought-after majors for data science are statistics, computer science, information technologies, mathematics, or data science (if available). Minoring in one of the aforementioned fields is also recommended. Continue to learn programming languages, database architecture, and add SQL/MySQL to the “data science to-do list.” Now is the time to start building professional networks by looking for connections within college communities, look for internship opportunities, and ask professors and advisors for guidance.
Step 3: Obtain an entry-level job
Companies are often eager to fill entry-level data science jobs. Search for positions such as Junior Data Analyst or Junior Data Scientist. System-specific training or certifications in data-related fields (e.g., business intelligence applications, relational database management systems, data visualization software, etc.) might help when looking for entry-level data science jobs.
Step 4: Earn a Master’s Degree or a Ph.D.
Data science is a field where career opportunities tend to be higher for those with advanced degrees. The in-demand graduate degrees for data science include the exact same specifications for an undergraduate degree: data science (if available), computer science, information technology, math, and statistics. However, many companies also accept STEM degrees such as biotechnology, engineering, and physics (among others). Also keep in mind that data scientists need to understand how to use enterprise-grade data management programs and how distributed storage and computation operate (e.g., Hadoop, MapReduce, and Spark) in relation to model building and predictive analytics.
Step 5: Get promoted
Additional education and experience are key factors that lead to being promoted or becoming a data scientist in high demand. Businesses value results. Coupling strong technical skills with project management and leadership experience will generally chart a course towards more significant opportunities and higher compensation.
Never Stop Learning
Staying relevant is crucial to the ever-evolving field of data science. In this age of constant technological innovation, continuing education is a hedge against shifts in the career market. This is also the case for data science since the field isn’t as established as other statistically and technologically focused careers. A career-oriented data scientist is always learning and evolving with the industry. Continue to network and look for educational and professional development opportunities through boot camps and conferences.