What is a Data Scientist? Explore the Data Scientist Career Path

Explore the Data Scientist Career Path

Data scientists collect, organize, and analyze large sets of data, providing analysis that is key to decision making. Governments, non-profits, and businesses of all types rely on data for forecasting, risk management, and resource allocation. Data scientists discover and analyze trends in data, and report their findings to stakeholders. They will use algorithms and models to simplify and mine data sets to create data-driven recommendations. Data scientists are needed across a handful of industries, especially the ubiquity of data and the reliance on it for business decision-making.

The Day-to-Day as a Data Scientist

Data Scientists interpret the results of data that they’ve processed, modeled, and analyzed to create actionable plans for their employers. With such broad opportunities, the day-to-day life of each Data Scientist varies. Data Scientists might be cleaning data, collecting data, analyzing data, creating data visualizations, making predictions, researching, risk modeling, or testing hypotheses. These skills contribute to things like reports, predictions, risk assessments, product or service improvement, and more.

Data Scientists can work in many industries including retail, tech, medicine, and government agencies. Most Data Scientists work on a team to make data understandable to those who need to use it to inform their decisions, create reports, or assess risks. They work full-time, usually, in onsite or remote positions. They can find part-time and freelance work as well.

What Skills Should Data Scientists Have?

Data Scientists should have strong analytical and communication skills. They must be proficient in SQL, Python and R, file management, machine learning and algorithms, natural language processing, linear algebra, calculus, statistics, and probability. Most jobs will also require proficiency in data modeling and visualization tools like Tableau. Data Scientists will need to be team players, good at pattern recognition, detail-oriented, and passionate problem-solvers.

It is essential for Data Scientists to dial in their soft skills. These skills will depend on the employment environment. Some Data Scientists work for large organizations where they will have to communicate their findings to upper-level management and C-level positions, others work with doctors and writers to create reports, some work for startups and will communicate directly to a team of founders, while still others will find themselves communicating directly with a small group of clients. These communications will essentially be educating the person(s) receiving the analyzed data.

Data Science

Data science combines domain knowledge, programming skills, mathematics, and statistics to infer crucial insights from data. These insights can be used by businesses, governments, and any other data-collecting entities to inform decisions.

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Python

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It is used to write scripts, automations, algorithms, manipulate data, and create frameworks. Python prioritizes simplicity, easy to learn syntax, readability, and versatility.

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SQL

SQL stands for Structured Query Language. It is a computer language used to store, manipulate, and retrieve data which is stored in a relational database.

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Machine Learning

Machine learning is the use and study of computer algorithms that improve automatically through experience. It is a subset of artificial intelligence (AI). Machine learning is used in everything from email filtering to Netflix recommendations.

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FinTech

FinTech stands for Financial Technology. FinTech is the technology or computer programs that are used to support, improve, enable, and automate banking and financial services.

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Mathematics

Mathematics are used on a day-to-day basis by many technical positions. Subjects like linear algebra, calculus, statistics, and probability are used by Data Scientists, Cybersecurity professionals, Developers, Motion Graphics Designers, Engineers, and more.

Typical Qualifications to Become a Data Scientist

You do not need a higher education degree to become a Data Scientist but many Data Scientists have a bachelor’s degree in mathematics or data science. Some also have graduate degrees or certifications. Many employers will require at least a four-year degree for this position. Typically, it is more important for a Data Scientist to demonstrate skill proficiency and thought processes than to have a degree.

Certifications are available for Data Scientists both from the Data Science Council of America (DSCA) and various vendors such as Microsoft, Google, IBM, Cloudera, and Dell. These certifications may boost a Data Scientist’s chances of employment but they are not required.

Tips to Become a Data Scientist

Data Scientists who are consistent in their job search should be able to find a job somewhat swiftly but will need to pass rigorous technical interviews. Along with a polished résumé, a Data Scientist should have a well-rounded portfolio that showcases their thought processes and technical knowledge. Case studies that use real-world data manipulated into reports with visualizations, predictions, and inferences alongside the thought processes that lead you there are ideal for showcasing skills in this field. Posting these portfolio examples on a cleanly designed blog is common practice so that potential employers can view your work.

Data Scientists should also be utilizing LinkedIn to its fullest potential. Your LinkedIn profile should be up-to-date, include all past experience, and include keywords relating to Data Scientists’ skills and responsibilities. It should also show and tell how your past experiences offer transferable value to your position as a Data Scientist.

To get a leg up, try to connect with a point person at each company you send an application to, whether you applied via LinkedIn or not. Add a letter of introduction as a note with your connection request that includes a conversation starter. This will provide you with name recognition and sometimes first-hand advice. You should also make these connections with people who might be your manager at any company you would like to work for regardless of whether they have posted any job openings.

What Job Titles Would a Data Scientist Hold?

Data Scientists can apply for many positions that may be narrowed down based on industry, location, company size, and interest in machine learning. Data Scientists will likely start out in junior or entry-level positions but will find that rising the ranks to a Senior Data Scientist is possible within a few years and will result in a better annual salary. Here are a few options you might be qualified for as a Data Scientist:

Junior Data Scientist
Senior Data Scientist
Data Engineer
Machine Learning Engineer
Big Data Analytics Consultant
Data Science Instructor
Decision Scientist
Data Analyst

What is a Data Scientist?

Explore the Data Scientist Career Path

The Day-to-Day as a Data Scientist

What Skills Should Data Scientists Have?

Learn the Skills You Need to Become a Data Scientist

Data Science

Python

SQL

Machine Learning

FinTech

Mathematics

Data Scientist Salaries

Typical Qualifications to Become a Data Scientist

Searching for Data Scientist Jobs

Tips to Become a Data Scientist

What Job Titles Would a Data Scientist Hold?

The Day-to-Day as a Data Scientist

What Skills Should Data Scientists Have?

Learn the Skills You Need to Become a Data Scientist

Data Science

Python

SQL

Machine Learning

FinTech

Mathematics

Data Scientist Salaries

Typical Qualifications to Become a Data Scientist

Searching for Data Scientist Jobs

Tips to Become a Data Scientist

What Job Titles Would a Data Scientist Hold?

Related Careers

Salary Comparison to Data Scientist

Data Analyst

Machine Learning Engineer

Data Engineer

Python Developer