How to Become a Data Scientist: Skills, Requirements, and Career Guide
Data science is a rapidly growing field that is transforming the way businesses make decisions and solve complex problems. If you're interested in working with big data, machine learning, and artificial intelligence, becoming a data scientist may be the perfect career choice for you.
In this comprehensive career guide, we will explore the skills and requirements necessary to launch a successful career in data science, including the latest tools, technologies, and programming languages used by data scientists. We'll also provide valuable insights into career advancement opportunities and how to position yourself for success in this exciting and rapidly evolving field. Whether you're just starting your career or looking to take your data science skills to the next level, this guide will provide valuable insights into this in-demand field.
What is a Data Scientist?
Data scientists are usually product analysts who find themselves inclined towards coding. If you’re technically savvy or at all inclined towards numbers, read on! A data scientist is like a product analyst on steroids. Data scientists often utilize code, like python or R, to ingest large datasets to find actionable trends or themes.
The insights found by a data scientist help more mature companies make decisions about the business, often in the realm of personalization. The primary difference between data scientists and product analysts is that data scientists use code to access and manipulate their datasets, whereas often product analytics requires significantly less technical knowledge.
Similar to product analysts, data scientists also use their skills to put together “data stories,” which include recommendations for changes their team could make to show positive impacts to website or application performance. Often this is by observing cohorts of users and their various engagements with the product to personalize for similar behaviors.
Data scientists complete less measurement strategy than product analysts, but make up for the difference in time with more detailed recommendations for users.
Responsibilities of a Product Analyst
What does a Data Scientist do?
Data scientists spend a bulk of their day writing scripts to combine datasets to find trends or themes that the business can use to improve experiences. They also run scripts, pull complex datasets, and present their findings for teammates to build personalized experiences.
Often companies who employ data scientists have their data housed in complex ways, so to find relationships between sets is a meaty task.
Difference between Data Analysts & Data Scientists
Analytics is often associated with coding, including SQL or Python to access huge swaths of data stored in warehouses. However, this is incredibly company dependent, and often product analysts are not required to code.
Most popular web analytics tools -- Google & Adobe Analytics, Amplitude, Heap and others do not require programming to access data and have an intuitive user interfaces. If a company is requiring "coding" or "programming," it may be worthwhile to ask about the reason in an interview. If they have lots of data only accessible by warehouse & code, this may be more of a data scientist role!
The main distinction between product analytics and data science is the ability to code-- and data scientists are paid better for it. Don't be fooled into doing data science for lower pay!
Do I need a degree to be a Data Scientist?
While this role seems more technical, data scientists can come from all backgrounds.
Job descriptions will often mention a requirement of something STEM related, but often bootcamp graduates and product analysts are best off. This is also a popular bootcamp role.
If you're new here to bridged, we're glad to meet you! We are huge fans of alternate forms of education, and recommend specific certifications to target skills. While this job works great with degrees, you have other options. Learn more here.
Our Favorite Data Science Certifications
Data Analytics with Google
Google sponsors a data analytics certificate program through Coursera. This is one of the more coveted certificates in the industry for Google Analytics.
SQL for Data Science
UC Davis teaches a class in Tableau to manipulate and visualize data. It's included with a Coursera subscription ($49/month) and has a rating of 4.5 stars with almost 6 thousand reviews.
Data Science A-Z™:
Real-Life Data Science
This class walks through the basics of data science including cleaning, visualizing and modeling.
Salary and Career Potential
What is a Data Scientist's salary?
We've aggregated thousands of salaries across glassdoor and linkedin, and data scientists can make anywhere between 100k-120k, depending on their location and skillsets.
Career Path of a Data Scientist
While this sample career path is very common in the tech industry, data scientists can have a range of roles and responsibilities. Often data scientists transition into other product roles, and sometimes even research roles!
- Data Scientist: Spend about 2-4 years at each level here.
- Senior Data Scientist: Spend about 3-5 years here.
- Analytics Manager (or Product Manager): Spend about 5-10 years here.
- Director of Analytics / Director of Data Science: This one is tricky, but most folks spend roughly 4-6 years here.
Job Requirements and Skills
Popular Job Description of a Data Scientist
We've used AI to aggregate the top job descriptions used by hiring managers looking for data scientists. When putting your resume together, try to mimic these listings. To learn more about this process, check out our partner Jobscan for a comprehensive resume review.
- Analyze complex datasets and identify trends and patterns
- Develop and implement machine learning models to drive predictive insights
- Work closely with cross-functional teams to design and implement data solutions
- Collaborate with business stakeholders to understand requirements and provide insights
- Conduct ad-hoc analysis to support business needs
- Communicate findings and insights to non-technical stakeholders
- Stay up-to-date with the latest tools, techniques, and best practices in data science
Top Technical Skills of a Data Scientist
We've compiled thousands of job descriptions for data scientists to record the most common requirements to save you time. While preparing for interviews, keep in mind specific times you've demonstrated these skills.
- Assist in the scoping and development of new analytical initiatives
- Collect, connect, and preprocess relevant structured and unstructured data from disparate sources
- Conduct exploratory data analysis to uncover interesting trends and relationships in user data
- Develop statistical analyses and build machine learning models that help explain and predict trends
- Apply statistical techniques to develop and evaluate product tests
- Design and integrate visualizations in comprehensive dashboards that enable key stakeholders to make decisions
- Craft and tell compelling data stories
- Experience with Data Science and Machine Learning (aka A.I.) methods including Regression, Classification, Clustering, Ensemble Methods, Deep Learning, Active Learning, Reinforcement Learning, etc
- Assisting team's prioritization with data-driven methods
- Developing hypotheses for testing prioritization based on findings
- Use Python, Java, C++, etc. to build models to inform company decisions
Top Tools of a Data Scientist
We've also compiled the most common tools listed in job description. If you're serious about becoming a data scientist, get familiar with these and be ready to talk about them.
For Gathering Quantitative Data
- Google Analytics
- Adobe Analytics
For Querying Data
For Visualizing & Aggregating Data
- Excel / Google Sheets
For Task Management
Key Traits of a Successful Data Scientist
Programming - Data Scientists are expected to have strong programming skills, especially in at least one of the popular programming languages such as SQL, R, or Python. They should also be well-versed in statistical methods to identify trends and extract insights from large datasets.
Qualitative & Quantitative Research - Data Scientists should have expertise in both qualitative and quantitative research methods. They must be proficient in data collection, organization, and analysis. Gathering and presenting data is incredibly important for Product Analysts to provide insights that drive informed decision-making.
Basic Mathematics & Statistics - Data Scientists need to have a deep understanding of statistical methods and concepts to be confident in the trends and themes they're identifying. They should be comfortable working with large datasets and have a strong foundation in mathematics and statistics to analyze and synthesize complex information.
Curiosity - Data Scientists should have a natural curiosity and a passion for data. They should enjoy sifting through structured and unstructured data sources to identify friction points and trends that can inform product decisions. They should possess a desire to constantly learn and improve their skills.
Data Storytelling - Data Scientists should have excellent communication and presentation skills to craft a compelling story with their data. They should be able to tell the when/what/where/how about the users on their site and communicate complex data insights in a clear and concise manner to both technical and non-technical audiences.
Data Visualization - Data Scientists need to know the best methods for communicating data stories via charts, graphs, and tables. They should have expertise in data visualization tools and techniques such as Tableau, Power BI, or Google Data Studio to create effective visualizations that communicate complex data insights in a clear and concise manner.
Get Data Science Experience
Data Science Experience
There are a couple ways we'd recommend getting data science experience for your portfolio that we'll outline below. Here at Bridged we are huge fans of using a mix of free resources and affordable certifications to achieve the same results as an expensive bootcamp. Try our Skills Tracker today to see what we're about! Other than that, here are some attainable options:
- Build Your Own Projects: Building your own projects is an excellent way to practice your data science skills. You can create a project based on a topic that interests you, gather and clean the data, and then apply data science techniques to extract insights and tell a data-driven story. Platforms such as Kaggle provide datasets and competitions for data scientists to practice their skills. We also love using Google Trends data for personal projects.
- Join Online Communities: Joining online communities such as Reddit's Data Science community, LinkedIn groups, and Data Science Central can provide valuable resources, tips, and networking opportunities for people looking to break into data science. These communities also provide access to mentorship and job opportunities.
- Attend Meetups and Conferences: Attending data science meetups and conferences can provide opportunities to learn from experts in the field, network with like-minded individuals, and get exposure to the latest tools and techniques in data science. We personally love Measure Camp and have attended for years (and still are in contact with a lot of the connections we made there!).
- Volunteer for Data Science Projects: Volunteering for data science projects can provide hands-on experience and allow you to apply your skills in a real-world setting. Platforms such as DataKind connect data scientists with non-profit organizations that need help with data-driven projects.
Get Started with a Bridged Recommendation
Review: Applied Data Science with Python Specialization by Michigan
Data science is a high paying career with some hard skills as a barrier to entry, making it less competitive but longer to learn.
Here at Bridged we are huge fans of stacking micro-certifications to achieve desired career results. We're building a product to make your career planning fun and affordable, and we'd love to talk to YOU! Was this article helpful? Did you land an interview for a data science role?
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Check out our sources!
Glassdoor Team. “Salary: Data Scientist (February, 2023) | Glassdoor.” Glassdoor, Glassdoor, 1 Feb. 2023, https://www.glassdoor.com/Salaries/product-analyst-salary-SRCH_KO0,15.htm