Here’s what we’ll cover:
Data science is an extremely broad field, entailing everything from cleaning data to deploying predictive models. It’s rare for a data scientist to do it all; most specialize in a specific function within the data processing cycle.
While data science is often used as an umbrella term to refer to the entire data function within an organization, in reality, data scientists work in tandem with data analysts, data engineers, business intelligence specialists, and data architects to create and maintain databases, analyze data and communicate business insights.
According to LinkedIn, data science jobs grew 37% from 2016-2019. Advances in open-source and commercial data analysis tools also means that drudgeries like data cleaning and preparation are often automated, leading to more specialized roles in data science focused on analysis, designing predictive algorithms, and decision science.
Average data scientist salaries vary widely by location, topping out at $166,519 in the San Francisco Bay Area, according to data from Indeed.
Statistics from Glassdoor put the average data scientist base pay at $113,309.
Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals.
Data scientists design data modeling processes and create algorithms and predictive models to extract the data the business needs. They also analyze the data and share these insights with the right teams in the form of reports, dashboards, graphs, and charts.
Data scientists may also perform the job roles of a data analyst, data engineer, data architect, and business intelligence specialist—since all of these areas fall under the discipline of data science.
Data analysts use business intelligence and analytics tools to sift through the data and identify trends, answering key questions like, “What stories do the numbers tell?” and “What business decisions can be made based on these insights?
It could mean figuring out how to price a new product, reduce supply chain costs, or workforce optimization (maximizing output relative to labor). A data analyst’s job roles vary depending on the type of data they work with, such as sales data, social media analytics, market research, and inventory data.
The key difference between a data scientist and a data analyst is what each does with the data collected.
Data engineers are responsible for creating and maintaining the analytics infrastructure that enables almost every other data function. This includes architectures such as databases, servers, and large-scale processing systems.
Most importantly, data engineers are in charge of ETL (Extract, Transform, Load) processes in data warehouses. This involves extracting data from various data source systems, transforming it into the staging area, and loading it into the data warehouse system.
To do this, data engineers need an in-depth knowledge of SQL and other database solutions such as Cassandra and Bigtable. If a company starts generating large amounts of data from different sources, a data engineer’s job is to organize the collection, process it, and store the information.
If you’re unclear on the distinction between a data scientist and a data engineer, remember this:
Business intelligence (BI) specialists transform real-time data into actionable insights. While data analytics consists of analyzing trends and predicting the future, business intelligence gives a readout on the current state of the business by tracking key operations metrics in real-time. For example, a BI dashboard could show how many customers are buying a particular item during a promotion, or how many engagements a social media campaign is garnering.
Business intelligence specialists design and implement BI software and systems, while integrating them with databases and data warehouses. Modern BI tools enable users to interact with agile, intuitive systems to analyze data farther, while classic BI centers around using data to generate reports.
Even so, a key part of a business intelligence analyst’s job is to use BI tools to access and analyze datasets and present analytical findings in reports, summaries, dashboards, charts, and graphs.
Data architects work in tandem with data engineers to conceptualize, visualize, and build enterprise data management systems (DBMS). DBMS refers to the technology solutions used to manage the storage and retrieval of data from databases. Data architects are also often responsible for creating and maintaining the databases themselves.
They work with database administrators and data analysts to ensure they can easily use and access the data, and work with management to figure out user needs and create solutions.
Finally, data architects oversee database integrity to ensure the security of the database in the event of a natural disaster or cyberattack.
Machine learning engineering combines data science with software engineering. A machine learning engineer creates programs that enable machines to perform specific tasks without human intervention, using artificial intelligence. These include functions like decision-making, speech recognition, translation, visual perception or making analytical predictions.
Machine learning engineers develop algorithms that can receive input data and use statistical models to predict an output, while updating outputs as new data becomes available.
Machine learning engineers and data scientists often work together, so there is a lot of overlap in their roles:
Learn more about the differences and similarities between machine learning engineers and data scientists in this short video.
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