IN THIS ARTICLE
- What Is Data Science?
- What Is Data Analytics?
- What a Data Science Career Entails
- Characteristics of Data Scientists
- What a Data Analytics Career Entails
- Characteristics of Data Analysts
- Data Science vs Data Analytics: How To Choose?
- Which Data Career Is Right for You?
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If you’re confused about the difference between data science and data analytics, you aren’t alone. A lot of people aren’t sure what sets them apart. Given their similar names, it isn’t easy to discern what sets these fields apart. Even a cursory Google search might not do the trick.
Although they do overlap somewhat, data science and data analytics are two distinct fields. Those who work in these areas have different roles within organizations and are expected to work on different kinds of problems. Your career trajectory can also change significantly based on whether you choose one or the other.
So we’re here to put this discussion to rest once and for all. We’ll tell you what sets the two fields apart, aside from their names. We’ll also show you how to pick when choosing between a data science and data analytics career track.
What Is Data Science?
Data science is the discipline of designing processes to source and process the data that is available to a company. While data analysts probe data and unearth insights, data scientists think about the processes used to source and analyze data, the systems used to store data, and mechanisms to automate data analysis.
Think of data scientists as professionals who work upstream from data analysts. While analysts receive a large volume of data and study it, data scientists have to create systems to obtain that data and design models that filter out extraneous data, in the hopes of aligning with the larger business goals.
Data scientists often start with data modeling, in which they design the information system that will carry their data. Then, they build prototypes and engineer those systems. Companies value good data scientists because they can do both those things: conceptualize data systems and build them out.
Another important part of a data scientists’ job is to choose data sources and write algorithms to wrangle and clean data. While data scientists might analyze data in the same way that a data analyst would, most of a data scientist’s work involves conceptualizing systems and processes that store and manipulate datasets.
What Is Data Analytics?
Data analytics is a subset of software engineering that involves analyzing large datasets to unearth trends and produce actionable insights that can guide business decisions. Think about the troves of data companies produce—data that has to do with its product, customers, the market, and so on. Data analysts break these datasets down and see what interesting insights can be unearthed.
The datasets available to companies are often unorganized and unwieldy; it isn’t always obvious what kinds of insights they can unearth. Data analysts study the available data and decide how to uncover patterns and meaningful business insights.
Data analysts work with a range of tools. Programming languages like R are popular in the field. Python is increasingly becoming the go-to program for data analysts, thanks to its easy syntax and powerful libraries. Data analysts also need to be familiar with database technologies, like SQL, so they can run queries to clean and manipulate data.
What a Data Science Career Entails
Data scientists must be able to imagine and develop computing systems. They apply this knowledge to systems that manipulate data and channel it across different individuals in an organization.
A career as a data scientist requires analytical skills that go beyond solving the problems of how to use data. Rather, it is about being able to conceive systems and frameworks that can help achieve that goal. Data scientists also work on data and analyze it, but that is only a part of their responsibilities.
Often, data scientists are more specialized in the field of computer sciences. The profession requires a deep understanding of algorithms and systems thinking, as well as the ability to actually hack together the systems they dream up.
Read more about data science career paths and roles for you to understand the options you can choose from.
Get To Know Other Data Science Students
Characteristics of Data Scientists
Data Science Skills and Tools
Data scientists need to be competent in probability, statistical analysis, calculus (both single variable and multivariable), and linear algebra. If you’re trying to get into the field, you should focus on learning these areas of math.
Related Read: Data Science vs Statistics: Learn the Difference
Machine learning and deep learning approaches are also integral to a data scientsts’ toolkit. Both can help develop models to interpret data and automate some of the work that you do.
Data scientists need to be proficient in working on data. Data wrangling is a key skill within the discipline. Python libraries like Panda are often used in the data wrangling process.
Data Scientist: Roles and Responsibilities
The following are the roles and responsibilities of a data scientist:
- Identify sources of data and automate the process of retrieving data from those sources
- Preprocess unstructured and structured data so it can be made available to data analysts
- Develop machine learning algorithms that can identify patterns in data
- Build predictive models using statistical modeling and predictive analytics techniques to forecast data trends
- Analyze data systems for efficiency, security, and optimization opportunities
What a Data Analytics Career Entails
Data analysts need to have a solid understanding of the company they work for, in addition to understanding their company’s larger industry. This requires some research beforehand, as well as updating your knowledge as you gain experience in your industry.
Data analysts use data and their knowledge of their industry to solve problems. Data analysts need to be able to answer questions such as: Which marketing channels produced the best results? Why do certain SKUs outperform others? What pricing categories will be most attractive to a particular demographic?
Data analysts answer these kinds of questions by examining patterns in the data. This requires skills in statistics (including statistical models) and probability, in addition to the programming chops needed to create scalable data analysis processes.
Presenting data is also part of a data analysts’ job. They study data visualization techniques for this and use visualization tools like Tableau to produce dashboards and reports. Soft skills like communication also come into play when talking about presenting data. Some data analysts have to prepare presentations and be able to answer doubts that other team members or management might have about their work.
Characteristics of Data Analysts
Data Analytics Skills and Tools
Critical thinking is a foundational skill for data analysts. They need to assess the most pressing business goals, then discern how their data can be most helpful in accomplishing that. A data analyst might also have to define the questions that need to be answered and set organizational goals from a data analysis perspective.
Compared to the job of a data scientist, data analysis is not as heavy on math. However, it helps to have at least a rudimentary grasp of statistics and probability.
Surprisingly, a lot of data analysts work in Microsoft Excel. It’s a piece of software that offers a lot of the tools and features needed to store and manipulate data. Data analysts can rely on programming languages like R when Excel isn’t sufficient.
Data analysts need to be able to quickly access and manipulate databases. Being highly proficient with a query language like SQL will help you both land a job, and also expedite your workflow.
Data Analyst: Roles & Responsibilities
Here are the main roles and responsibilities of a data analyst:
- Collaborate with management and other teams to establish immediate business goals
- Analyze datasets and produce meaningful insights to take key business decisions
- Implement data collection processes and acquire data from primary and secondary sources
- Visualize data and present it in a manner that makes it easy for non-technical team members to comprehend insights
Data Science vs Data Analytics: How To Choose?
Education and Background
A career as a data scientist entails a different education path than that of a data analyst.
Most data analyst roles require a bachelor’s degree, usually in either computer science or mathematics. If you have a degree in math, you will need to work on your programming chops to land a data analyst job.
Many data analyst roles don’t require a degree. In that case, completing a data science certification and working on personal projects can help you land a job.
Data scientist roles often require more advanced college degrees. Preference is often given to candidates who have a master’s degree or higher in areas like mathematics or computer sciences.
One way to decide on data science vs data analytics is by reflecting on your own interests.
Data scientists spend most of their time building systems for data analysis and using knowledge of machine learning to build predictive analytic models. They’re in charge of designing a company’s data collection system and defining processes from a computing perspective. It’s a role that suits people who enjoy systems thinking and mathematical computing.
Data analysts work more closely with the realities and challenges of their employers. They are given datasets to work with, then have to answer or solve specific questions or problems. Programming skills or tools like Excel can be used to surface data insights and solve those problems. Data analyst roles are perfect for people who like to contribute directly to organizational goals using data.
So you can make the choice between data analysts and data science easier by reflecting on your interests. Are you someone who likes to go big picture and come up with systems that can work on data? In that case, you should work as a data scientist. But if you like to go deep into datasets and work on them to tease out interesting patterns, then you’re more likely to enjoy working as a data analyst.
Career Path and Aspirations
Data scientists are usually considered more senior than data analysts. However, you do need to spend more time getting an advanced degree if you want to work as a data scientist. Choosing between a career in data science versus data analysis will depend on that trade-off.
The average salary of a data scientist in the US is $110,000 annually. Meanwhile, data analyst roles command $65,300 on average. Data analyst job opportunities are usually more plentiful in companies compared to data science roles.
Take all of those factors into consideration when making a choice between a career in data science and data analysis. Data scientists spend more time on average in an academic setting getting advanced degrees. Data analysts have an easier path into organizations and you can even land entry-level roles without a college degree. Although data science is more lucrative, data analysis roles can also pay well as you gather experience in the field.
Which Data Career Is Right for You?
Now that we’ve differentiated between data science and data analytics, it’s time to decide which path you should take. This isn’t about choosing sides in the data science vs data analytics debate. Rather, the aim is to pick the field that’s best suited to your skills and aspirations.
Before you decide, let’s reflect on the differences between the two. Exploratory data analysis places more emphasis on analyzing existing data and using these insights to answer business questions. You become a problem solver and work with other teams to unearth insights that can grow the business.
A data scientist, on the other hand, thinks more about where to source data from, how it should be stored, and how to optimize its movement through a system. If you’re fascinated by things like machine learning and data modeling, then you’ll likely enjoy a career in data science.
There are also important differences between the two disciplines based on the investment of effort that you need to put in beforehand. Most data scientists have at least a master’s degree and some experience with research. Data analysts, on the other hand, can self-learn and make inroads professionally by working on personal projects.
Whichever way you go, remember that businesses prize both data scientists and data analysts. So make a choice based on your interests and know that the job market is great for anyone who’s skilled in working with data.
Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!