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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.
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 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.
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.
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.
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.
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.
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.
A Data Scientist in the United States makes, on average, $141,449 annually, according to Indeed.com.
Salaries for Data Scientists vary by region within the the United States. Listed below are some Data Scientist salaries for specific areas with the United States compared with the average national salary:
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.
Data Scientists work in a variety of environments including corporate companies, retail conglomerates, medicine, academia, start-ups, or for a government entity. They can find jobs in nearly any industry making predictions, solving problems, and presenting data in a consumable way.
You can find Data Scientist jobs on sites like:
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.
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:
Many people confuse Data Scientists and Data Analysts. They both work with data and the biggest difference between the two is what they do with that data. Data Analysts identify trends and create visualizations with those patterns. Data Scientists also interpret data but also have coding and mathematical expertise. They can do the work of a Data Analyst but usually work on algorithms, predictive models, and creating processes for data.
Data Scientists might find related careers like Business Analyst, Business Intelligence Analyst, Data Journalist, Financial Analyst, Product Analyst, or Database Administrator enticing. Most of these positions pay similar or slightly higher salaries to a Data Scientist and have similar day-to-day operations but use additional technologies or use basic technologies in a different way. Some ways to upskill into these positions would be to learn financial and business theories; sharpen writing skills for journalism; learn networking; learn back-end web development.
If you’re a Data Scientist you might find that pivoting toward the world of business or financial analysis enticing! This would include more communication with upper-level management and c-level positions, making more predictions, using math as much as technology, and understanding business principles. These positions might provide more satisfaction as the results of your work can be seen relatively quickly. If you like the learning, writing, experimenting, and inference aspects of data science the best, you should look into Data Journalism!
Data analysts review large amounts of data to summarize, analyze, and visualize it and provide insights. Working from data from multiple, relevant sources, they create and maintain databases, and use statistical techniques to analyze the collected data. Data analysts must be able to communicate with others about what the data shows and to be able to provide realistic recommendations based on their analysis. Many industries such as healthcare, advertising, and retail rely on the work of data analysts to inform their business decisions and strategy.Learn about becoming a Data Analyst
Machine Learning Engineers create computer programs that enable machines to take actions without being specifically directed to perform those tasks. This job combines computer programming and data science to enable systems to learn and improve from experience automatically by using machine learning, a subset of artificial intelligence.Learn about becoming a Machine Learning Engineer
Data Engineers create the infrastructure for data and format data into a useful system which Data Scientists use to analyze large amounts of data. Data Engineers can specialize in pipelines, databases or platforms, warehouses or infrastructure, or be generalists.Learn about becoming a Data Engineer
Python Developers typically choose to focus on back end web development, data science or analysis, scripting, or product development. They build the server side of websites, processes for data analysis, and create automation scripts.Learn about becoming a Python Developer