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.
Regardless of your previous experience or skills, there exists a path for you to pursue a career in data science. We are here to help you know what skills you need to develop, and where you can learn them.
This guide provides a basic overview of some of the opportunities in this emerging field and lists the skills to become a data scientist. Let’s get started!
What is a Data Scientist?
Data Scientists must possess statistical knowledge and computer skills that are needed for solving complex problems. Using descriptive, predictive, inferential, and causal models, they can explore and anticipate problems then work to model a solution based on a multitude of factors.
Data scientists are part mathematicians and part computer scientists. Their skillset encompasses both the business and information technology sectors, which is why they are highly sought after.
Data science is a deep knowledge discovery through data exploration and inference. This discipline focuses on using mathematical and algorithmic techniques to solve some of the most analytically complex business problems. In doing so, they leverage the troves of raw data to figure out the hidden insight that lies beneath the surface. The core of the field centers around evidence-based analytical accuracy and building strong decision capabilities. However, data scientists must also verbally and visually communicate their findings to stakeholders who may or may not understand the statistical jargon. Thus, data scientists must be excellent communicators.
What is the necessary background?
The first waves of data scientists were primarily from development personnel, computer scientists, and engineers. They were the ones who created machine learning models that optimized the process and minimized the cost function. They would analyze unstructured data, create specific programs for each problem, and, due to limitations of the computational processing, do manual map/reduction.
The good news is people from different backgrounds have the opportunity. Join this field and one of the main reasons is the intense use of Python which is a high-level language acting as an API. Performative low-level languages. In other words, you don’t have to waste energy worrying about complicated syntax. Because writing in Python is like writing in English. You just need to study for a few weeks to master the basics.
Six 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, looking for internship opportunities, and asking 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.
Step 6: 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.
In general, the data science outlook continues to be on the upward trajectory as the influx of data isn’t likely to cease anytime soon and enterprises will need someone with the skills to parse through data tangle and help increase its value.