Data science jobs are plentiful in today’s job market. Read on to learn about what data scientists do, what kinds of data science-related jobs are available, and how to become a data scientist.
What is data science?
The purpose of data science is to create an impact by solving business problems that can increase profits, reduce costs, provide insights and others. The data science process includes collecting, cleaning, analyzing, and communicating data. Data is used as a tool that provides insights and helps guide the decision making process. Over the last several decades, companies have realized the importance of utilizing data to inform business decisions. This led to the rise of data science as an industry and data scientists as key players in today’s successful businesses.
What is a data scientist? And what does a data scientist do?
Data scientists help companies and organizations increase revenue, reduce costs, provide insights, and boost productivity through data analysis. They’ll take a question, such as how certain customer demographics translate to the purchase or renewal of a product or service, gather and analyze the available data in order to create actionable insights into how the company can improve. A data scientist is part mathematician, part computer scientist, and part subject-matter domain expert.
Data Scientist Skills
Critical thinking, communication, and problem solving skills, as well as math, computer science, and subject matter expertise skills, are core to a successful career in data science. These hard and soft skills work hand in hand to help you take raw data, purge it of impurities, extract meaning from it, and be able to communicate that meaning to key stakeholders. Having the technical skills to understand and manipulate the data and being able to communicate your findings to those invested in the project (who often don’t have the technical knowledge to understand the data on their own) will empower you to have a successful career in data science.
Your required level of expertise in each area depends on what specific type of data science you’re interested in. You should learn all the basics, but don’t worry about learning particulars until you’ve narrowed down your career goals.
You should be comfortable in the following data science subject areas:
- - Mathematics
- - Calculus
- - Linear algebra
- - Statistics
- - Probability
- - Programming Skills (e.g. Python, SQL, Tableau)
- - Machine learning
- - Data visualization
- - Communication
- - Subject matter expertise
Here are a few examples of tasks that data scientists do:
- - Create data visualizations and presentations
- - Have sufficient programming skills in at least one programming language for manipulating and summarizing data
- - Develop models using machine learning algorithms
- - Know how to access and clean data
- - Understand how various machine learning models work, the merits and drawbacks of each model, and how to interpret their outputs
- - Conduct a business intelligence report using data analysis
- - Clearly and succinctly communicate findings to stakeholders.
Data Science Careers
Careers in data science vary greatly. The “data scientist” job title exists in many different fields, and is creating an impact in the following industries:
- - Technology
- - Financial
- - Healthcare
- - Marketing
- - Retail
- - Education
- - Journalism
- - Research and Academia
- - Law and Policy
- - Cybersecurity
- - Government agencies and non-profit organizations
- - And others
If you’re looking for a career that incorporates working with data as one of many job responsibilities, consider one of the following job titles:
- - Data Scientist
- - Machine Learning Engineer
- - Data Analyst
- - Data Engineer/Software Engineer
- - Data Miner
- - Analyst
- - Product Analyst
- - Research Analyst
- - Marketing Analyst
- - Data Journalist
- - Business Analyst
- - Business Intelligence Analyst
- - Database Administrator
- - Artificial Intelligence Engineer
- - Statistician
- - And more
How to Become a Data Scientist
If you’re considering becoming a data scientist, ask yourself the following questions to gauge your readiness:
- - Do you have a solid grasp of basic math concepts in Linear Algebra, Calculus, Probability and Statistics?
- - Do you have experience programming?
Once you’ve established a baseline for yourself, take the following steps to get started in data science:
- - Set some flexible goals that will guide—but not restrict—your data science journey. Why do you want to work in data science? What types of data problems do you want to solve? Use your goals to inform your path, but don’t get discouraged if you hit a roadblock or your interests change.
- Take a course or work through a textbook in one of your weaker areas. Metis offers a Beginner Python and Math for Data Science course that covers the fundamentals to get you started in a career in the Data Science and Analytics field.
- - Find encouraging mentors. These could be people you already know who work in the field, or you can reach out to someone new whose work you admire and develop a relationship with them.
- - Pick a project that excites you and start collecting and analyzing data! For example, you could create a report (basic data analysis or summarization of information that answers a “business” question in an area that interests you) on how your favorite sports team did last season, or a visualization of your exercise and eating habits, or find open source data for a scientific study and determine the correlations between the different variables.
- If you need a more structured environment, you can join a program such as the Metis Data Science and Analytics Bootcamps or Short Immersive Courses.
Data Scientist Resume
Writing a resume when you’re new to the field of data science might seem daunting, but rest assured you can make a compelling case for yourself even without years of data science experience under your belt.
If you are transitioning to the Data Science field without having a lot of prior experience, building out and showcasing a portfolio that contains data related projects you’ve worked on will be an important part of your resume.
Second, you’ll want to include an “Experience” section on your resume that should include any data science-related jobs or experiences you’ve had, but if you’re at the beginning of your career these may be lacking. You should address this in a few ways:
- List your prior work experiences (whether paid or unpaid) even if they are not data related. Employers like to see that you are employable, so highlighting your work or volunteer history is important. Find connections between your previous employment and other experiences that indicate a propensity for the type of work you’d be doing as a data scientist. For instance, if you worked for several years as an investment banker, you could highlight that your responsibilities included analyzing a high volume of financial data and making financial recommendations to your clients. Remember, not all important data science skills are technical, so if you worked in a non-technical role before, think about what other functional skills you practiced that are transferable to data science.
- List projects you’ve worked on. Projects may not be what you’re used to seeing under the “experience” section of a resume, but it’s a perfectly acceptable way to show how you’ve gained experience in data science. If you’ve taken courses that required you to complete significant data projects, include information about them. If you’ve worked on projects individually that you feel confident including, don't hesitate to do so.
- Demonstrate your curiosity and ability to problem solve. Talk about experiences where you uncovered a challenge and had a methodological approach to solving it.
Finally, make sure to include your education on your resume. While employers like to see some education in STEM, there are many companies out there that are open to considering candidates of all backgrounds and educations. If you do not have formal technical education, it is recommended that you supplement the education you do have with a licensed bootcamp like Metis. Programs like Metis can help to give you the technical foundation needed for data science roles but also help you build some hands-on experience by allowing you to build out a data portfolio.
Data scientist job description
If you’re interested in pursuing a career in data science, you might come across job descriptions that look like this:
Data Scientist Job Responsibilities
- - Work collaboratively to identify how we can leverage company data to create business solutions.
- - Gather and analyze data from databases to optimize and improve processes such as product development and marketing automation.
- - Analyze new data-gathering efforts and sources for accuracy and effectiveness.
- - Create custom data models and develop algorithms for data sets.
- - Leverage predictive modeling to grow revenue, enhance customer experience, and optimize ad targeting.
- - Develop and execute A/B tests including designing the test, gathering relevant data, and analyzing the results.
- - Implement processes to track and analyze model performance and accuracy.
Data Scientist Job Skills and Qualifications
- - Strong product development problem solving skills.
- - Statistical computer language expertise for manipulating data and drawing insights from large data sets including, but not limited to: R, Python, SQL
- - Ability to create and craft data architectures.
- - Comfortable with many machine learning techniques Skilled in applying advanced statistical concepts and techniques A genuine interest in learning and utilizing new techniques and technologies.
- - Experience building statistical models and working with data sets, and is familiar with the following software/tools:
- - High level of familiarity with the following tools and software:
- - Knowledge and experience in statistical concepts and data handling : distributions, correlations, exploratory data analysis, hypothesis testing, etc.
- - Experience creating and using advanced machine learning algorithms: regression, tree-based models, clustering, neural networks, etc.
- - Experience with computing tools such as Spark, SQL, Cloud Computing, and more.
- - Experience visualizing and presenting data with BI tools such as Tableau
Data Scientist Salary
Glassdoor reports the average base pay for data scientists as just over $113,000/year. Keep in mind that this number will vary significantly depending on where you work, the size of your company, and your level of experience and education.
Data Analytics vs. Data Science
Both data analysts and data scientists deal with collecting and analyzing data. Data analysts often focus on building reports and dashboards, whereas data scientists tend to focus more on data modeling. The roles are similar, but data science jobs are generally more advanced and higher-paid. If you’re passionate about the field of data science but have little experience, data analytics is a good place to start.
Business Intelligence vs. Data Science
Business Intelligence experts work with data focused on exploratory data analysis, visualizations and communication. They spend most of their time using tools such as Excel, Tableau, and others.
Data Scientist vs. Machine Learning Engineer
Machine Learning Engineers in addition to having the capabilities of many Data Scientists they can work with big data, more advanced algorithms such as Deep Learning, and can even develop new algorithms.
Data Science vs. Data Engineering & Software Engineering
Data Engineers focus more on the data pipeline where they are responsible for collecting and storing data with high accuracy and quality, find ways of extracting the data and feature engineer new data. Feature engineering consists of creating new data from the collected data; for example, if you have the date a customer made a purchase, you may want to create a new feature called "day of the week", which indicates if the purchase occurred on Monday, Tuesday, etc.
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