What is a Data Scientist?
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.
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.
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
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
This is a great certification from UC Davis that focuses on SQL principles and syntax. There are units on AB Testing, data structures and distributed computing.
Data Science A-Z™:
Real-Life Data Science
This class walks through the basics of data science including cleaning, visualizing and modeling.
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!
*Or Associate
Director of DS
Entry Level Data Scientist 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.
Top 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
- Amplitude
- Heap
- Mixpanel
For Querying Data
- SQL
- Python
- R
- Java
For Visualizing Data
- Tableau
- Lookr
- Mode
- Excel / Google Sheets
- PowerBI
For Task Management
- ASANA
- Clickup
- Jira
- Notion
- Trello
Key Traits & Competencies of a Successful Data Scientist
As a Data Scientists, you'll work most with:
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!
Conclusion
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?
Let us know at hello@getbridged.co
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