As a data scientist you are tasked with managing and manipulating large datasets to find insights or make predictions. Data scientists use tools like statistical models, algorithms, and machine learning to make their data more digestible and easier to understand. That way others can read the data and make conclusions too. Depending on your role, you may create visualizations like charts or graphics that demonstrate the ‘story’ behind the data – that is, what does the data tell or predict?
As a data scientist you may collaborate with data analysts, software engineers, data engineers, scrum masters, designers, and product managers.
While data scientists and data analysts use many of the same skills, these roles are not quite the same. A data scientist is generally a more senior role, while data analysts can be considered the entry-point for a career in data science. Both data scientists and data analysts use statistics and programming to analyze data, but a data scientist generally has a broader and more advanced technical toolkit. Another key difference is that data scientists perform predictive analysis, which requires more advanced skills such as machine learning and deep learning, while data analysts will focus more on analyzing past or current data trends.
Notice that while specific academic requirements and hard skills are necessary for data scientists, soft skills such as leadership abilities and communication skills are imperative. In order for a data scientist to be successful, they must be able to communicate with their partners in the business world and make them understand the data and innovative problem-solving options presented.
Data scientists use a variety of tools to perform their job. First, every data scientist must be a master in at least one programming language. According to a 2020 study of over 20,000 Data Scientists by Kaggle, the most popular programming language and the language that should be learned first is Python. Data scientists also employ the use of Integrated Development Environments which are coding tools that allow data scientists to write, test, and debug their coding. According to the 2020 Kaggle study, JupyterLab and Visual Studio Code are the two most popular IDEs.
Data visualization tools are also important tools for data scientists. Below are the 4 most common data visualization tools used by data scientists. Matplotlib, Seaborn, Plotly, and Ggplot were the most used by those surveyed.
Computing platforms, presentation tools, processing units, and more are all needed for a data scientist to successfully perform all required tasks. A data scientist must be proficient in multiple types of tools which is why they are in such high demand for their wide range of skills and talents.
Data scientists can find work with any company or organization that keeps or uses data. Research facilities collect and use data for their studies; hospitals keep data about their patients; and commercial businesses keep data about their sales and production to help make informed business decisions. And these are just a few examples – Glassdoor lists opportunities for data scientists across 16 different industries, including education, aerospace, banking, and civil engineering! As more and more organizations find the value of data-driven decision-making, the demand for data scientists continues to grow. According to the Burea of Labor Statistics, the number of data scientist jobs in the U.S. is projected to increase by 15% between 2019 and 2029.
The metro areas with the highest concentrations of data scientists – also from the Bureau of Labor Statistics – are Lexington, MD; Warner-Robins, GA, and Atlantic City, NJ. If you’re looking for a data science career outside of large urban areas, Northeast Virginia also has an especially large number of data scientist roles.
The length of time it takes to become a data scientist is very unique to the individual. A bachelor’s degree usually takes at least 4 years. Master’s programs that follow are at least 1-2 years long. Certification programs can last 6-18 months and bootcamps generally last 6-18 months. Thinking of completing a PhD program? This adds approximately 4 years to the timeline.
Python.org is a community open to the public for resources to learn, practice, and get feedback about the programming language.
Khan Academy’s Introduction to SQL is a free course to learn SQL and incorporates challenges where you can apply what you learned.
Andrew Ng’s Machine Learning Course is widely regarded as the best course for machine learning. The course is free and has had millions enroll in the course.
Data Science Weekly is an email newsletter sent every Thursday and includes data science jobs, articles, and the latest news in the profession.
Discover Data Science lists multiple data science programs as well as scholarship programs and a plethora of other resources.
Elite Data Science is a library of resources for data science beginners.