A data scientist uses algorithms, data mining, and other processes to extract patterns and knowledge from large amounts of data. By analyzing the information, they can use their insight to optimize process performance, gather intelligence, and solve any problems that may arise. Essentially, data scientists apply statistical models to make recommendations for optimal business decisions.
With an average salary of $117,345, data science is a highly sought-after career path. If this career path seems appealing to you, we’ll answer all of your questions related to “how to become a data scientist” below.
Check out our guide of the general skills, educational background, and responsibilities of a data scientist.
The Breakdown Of How To Become A Data Scientist
Who Is A Good Candidate To Become A Data Scientist?
Excellent candidates for the field of data science should be first and foremost inquisitive learners. The constant thirst for knowledge and curiosity to find answers in thousands of lines of data is a must-have. In a line of work that can be extremely data heavy, it’s crucial that each scientist can maintain long-term interest in finding optimal solutions. In addition, candidates should be detail-oriented and persistent in their work. Complications and road bumps can be common when mining data. Can they catch small details that will change the entire project? Will they persevere through frustrating times where the answer doesn’t appear to exist? Lastly, candidates should be creative problem solvers. The ideal individual should be able to approach the problem in different ways, by reorganizing and reanalyzing the data until the answer can be found.
It’s important to remember that data science can also be pursued without any prior background in coding and analysis.
In an interview with Yohan Obadia, a senior consultant at Ernst & Young, he initially believed that coding “was something stratospheric that only geniuses that started when they were 15 years old could do.” However, his marketing analytics class during his master’s program quickly changed this erroneous opinion. For candidates interested in this career path, the willingness to learn and solve problems is ultimately the most important factor of success.
What Educational Requirements Are Needed For The Job?
Without a bachelor’s degree, it can be virtually impossible to land a job in data science. For undergraduate students, a data science degree is an obvious choice if your school offers a specific program. However, if your school does not have a designated major, related degrees in computer science, IT, and statistics will also be great options.
In addition to a strong educational background, make sure to supplement your resume with relevant internships, research programs, and work experience. These can vary from previous positions directly related to the field you plan on pursuing (i.e. healthcare or marketing) and should demonstrate your analytical capabilities. It’s also a good idea to have a portfolio of previous school work and projects as well, which can be used in interviews for research, internships, and future job applications.
Also, having a fundamental knowledge of commonly used programming languages like R and Python, Excel, and Tableau is a great starting point before pursuing a Master’s program.
What Do A Data Scientist’s Daily Responsibilities And Tasks Entail?
One of the biggest misconceptions is the time allocated to different assignments. As Yohan Obadia mentioned, modeling actually represents is a small portion of time spent on projects. In fact, the majority of time is dedicated to “identifying the needs of the client to make sure that what we deliver solves the problem…and delivering the result in an intuitive way, like an interface.” With a large portion of the tasks actually allocated in clientele interface, a data scientist should be able to explain and pinpoint what data is needed for success. Additionally, data scientists spend the majority of their time pre-processing data and transforming business problems to read as a data mining problem instead.
Other daily responsibilities include optimizing classifiers with machine learning techniques, as well as extending company data when working with third-party information sources. Other complicated tasks can include ad-hoc analysis and developing different systems to track for anomalies or common occurrences. They may also be responsible for presenting their findings to stakeholders or business executives. For a full scope of a data scientist’s responsibilities, check out this resource here.
What Tools Do Data Scientists Use?
Most data scientists are extremely well-versed in Python and R. Python is typically the main programming language used, and also includes its entire PyData packages, including “Pandas, Jupyter Notebook, Sci-kit, Matplotlib, Weka, and NetworkX.” Isac Artzi, an associate professor at GCU, mentions that he does most of his Python work in PyCharm, and for R he uses programs like RStudio and Shiny. For databases, MySQL and SQLite are commonly used.
It’s important to note that different programs, programming languages, and tools can be used at any time. As Dat Tran, a data scientist and AI engineer, explains, “There are many tools that I use, but I am also quite tool-agnostic. I don’t really care about the tool itself but want to use the right tool for the problem and every problem is different.” Each data science problem will require different tools for a solution, so scientists may have to test out several options before finding the right one for the problem.
Explore The Best Graduate Programs For Data Science
We hope we’ve answered your questions related to how to become a data scientist. A career in data science can be both incredibly rewarding and challenging. With all of the undergraduate and Masters programs available, there is a school with the perfect curriculum for your specific needs.