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- Data Analytics vs. Data Analysis
- Quantitative vs. Qualitative Data
- Data Analytics vs. Business Analytics
- Data Analytics vs. Data Science
- The Difference Between Data Analytics and Statistics
- The Difference Between Data Analytics and Data Visualization
- Data Analytics Lifecycle
- Data Analytics vs Business Intelligence
- What is Behavioral Analytics?
- What is Descriptive Analytics?
- What is Data Analytics?
- What is Diagnostic Analytics?
- Data Analytics Processes
- A top-level guide to data lakes
- Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
- Data Warehouse Architecture
- What Is a Data Warehouse?
- How to Create and Use Business Intelligence with a Data Warehouse
- Best Practices for Accessing Your Data Warehouse
- Data Warehouse Best Practices — preparing your data for peak performance
- How do Data Warehouses Enhance Data Mining?
- Data Warehouses versus Databases: What’s the Difference?
- What are the Benefits of a Data Warehouse?
- Key Concepts of a Data Warehouse
- Data Warehouses versus Data Lakes
- Data Warehouses versus Data Marts
- Difference Between Big Data and Data Warehouses
- How to Move Data in Data Warehouses
- What Is Customer Data?
- Customer Data Analytics
- Customer Data Management
- Collecting Customer Data
- The Importance of First-Party Customer Data After iOS Updates
- Types of Customer Data
- What Is a Customer Data Platform?
- What is an Identity Graph?
- Customer Data Protection
- A complete guide to first-party customer data
- CDPs vs. DMPs
- What is Identity Resolution?
- What is Consent Management?
- Data Access Control
- Data Sharing and Third Parties
- Cybersecurity Frameworks
- What is PII Masking and How Can You Use It?
- Data Security Strategies
- Data Security Technologies
- Data Protection Security Controls
- How to Manage Data Retention
- How To Handle Your Company’s Sensitive Data
- Data Security Best Practices For Companies
- What is Persistent Data?
- Google Analytics 4 and eCommerce Tracking
- What Is Google Analytics 4 and Why Should You Migrate?
- GA4 vs. Universal Analytics
- What are the New Features of Google Analytics 4 (GA4)?
- GA4 Migration Guide
- Benefits and Limitations of Google Analytics 4 (GA4)
- Understanding Google Analytics 4 Organization Hierarchy
- Understanding Data Streams in Google Analytics 4
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Data Analytics vs. Data Science
The terms data analytics and data science are sometimes used interchangeably by those who don’t work with big data. While there is overlap in places, there are core features unique to each field and differences in the skills expected of the practitioners within each field. Looking at job descriptions (and salaries!) for each role also shows that the two areas have quite different specialties.
This article will highlight the primary attributes of both data analytics and data science, explain what data analysts and data scientists do and the skills they should have, clarify when to use each process, and explain how to choose which role to hire for your team.
What is data analytics?
Data analytics aims to discover insights about specific areas of a business and uses basic statistics to find solutions to the questions that data scientists raise. Data analysts then communicate these solutions to their stakeholders so they can be implemented by the business. Data analytics follows a process of creating regular reports and predictions for the business, instead of just providing one-off insights. Data analysts do this by using an automated pipeline for consuming and monitoring data, which gets designed and created by data engineers. This pipeline follows all the steps of the data analytics lifecycle.
What is data science?
Data science is a field that uses rigorous experiments, computer algorithms, and statistics to find patterns in both structured and unstructured data, leading to useful business insights. It is an umbrella term that includes some parts of data analytics, as well as a combination of other disciplines such as machine learning and data mining.
The goal of data science is to apply scientific methods and predictions to business goals and discover new and unique questions to drive the business forward. Some useful predictions that data science can help with include working out how many supplies should be purchased based on expected sales volume, or answering a question like “if we raise prices by X%, what is the predicted impact on sales and revenue?”
The difference between data science and data analytics
Both data science and data analytics techniques can be applied to big data. They both involve collecting, preparing data, and analyzing data. But beyond these similarities, the two fields are quite different. The main differences between data analytics and data science are listed below.
There are four main types of data analytics: descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive and diagnostic analytics are done by data analysts, but predictive and prescriptive analytics fall under the realm of data science. This is the main difference between the two fields: data analytics looks backward and focuses on past data, aiming to identify trends (by describing the past and diagnosing why certain events happened). Data science looks forward and focuses on the future (by predicting it or prescribing what should happen).
Data science involves coming up with and answering key questions that are game-changers for driving businesses forward. Data analytics focuses on asking specific questions that are on more of a micro-scale or are specific to a particular team. Despite the smaller scale of the questions, data analytics answers very useful questions that tend to be asked on a regular basis, which is why a key part of data analytics is operationalizing–procedurally automating–analytics reports.
Both data analytics and data science make use of statistics; however, the types of statistics used in data analytics tend to be more rudimentary than those used by data science. Data analytics tends to use aggregation methods such as averages, percentiles, sums, and counts in spreadsheets, analytics tools (such as Mixpanel, Amplitude, or PostHog), or relational databases and data warehouses. Data scientists, on the other hand, use more advanced statistical methods such as regression or cluster analysis. Data scientists also commonly use machine learning models, whereas data analysts are much less likely to do so.
Data analysts will always be provided with a question that needs answering and will usually have access to structured data to help them with their analysis. Structured data is data that is highly organized in its structure: for example, data that is stored in a spreadsheet or relational database. Data scientists, by contrast, often have to wade through large amounts of unstructured data (for example, image data, social media posts, or large amounts of free text) and use data mining techniques to find useful insights from it. They may also have to come up with their own questions, and they must be able to justify why answering these questions adds value to the business.
One of the areas of confusion in comparing data analytics and data science is that predictive and prescriptive analytics are sometimes viewed as part of data analytics (because they are two of the four main types of data analytics), but they are also viewed as part of data science because they tend to be done by data scientists. This Venn diagram shows which activities are considered part of data science or data analytics (some things are done in both fields), as well as using a color-coded key for which of these tasks are done by data scientists, data analysts, or both.
What does a data analyst do?
Data analysts examine structured data sets using SQL, spot trends in the data, and draw conclusions from this by using simple statistical methods. Some data analysts may also be familiar with advanced statistics–this is one place where the job role overlaps with that of a data scientist. A data analyst generally uses spreadsheets, relational databases (SQL), and analytics tools such as Power BI. They also may have some basic programming knowledge of languages like Python, SAS, or R, especially if they are the kinds of data analysts that do advanced statistical techniques.
Data analysts tend to have a closer understanding of the particular business area they work on as their role is less spread across different areas of a business than that of a data scientist or business intelligence analyst. Analysts are subject matter experts when it comes to the data of their area of the business, so their job is to answer specific data-driven questions about that area, which the business can then use to inform decisions. Like data scientists, data analysts are responsible for collecting, organizing, and analyzing data to find relevant patterns, but the scope and focus of what they do along with as well the skills they need is different.
Storytelling, and the ability to see the big picture, are essential skills for a data analyst. They are responsible for clearly communicating their findings with the rest of the business, and demonstrating the business value of their analysis. Data analysts present their findings using data visualization methods such as charts and graphs, which both technical company employees and external stakeholders can understand.
What does a data scientist do?
Data scientists use various methods including mathematical, statistical, and machine learning techniques to clean, process, and model data to extract their insights. While both data scientists and data analysts pre-process and clean their data before analyzing it, the analysis that a data scientist does is more complex than that of a data analyst: they do predictive analytics and complex statistics, and use more advanced statistics and even machine learning algorithms to model and test their hypotheses.
Data scientists are also able to discover patterns in data that lead to new and fruitful questions that help drive a business forward, and they can calculate the answer to more complex questions such as the optimum price at which to buy a product if the expected selling price is $X.
They are also expected to be able to look for insights in all different kinds of data, including unstructured data such as plain text or images, where they can use data mining techniques to extract useful information. Sometimes this data mining work is done to turn raw unstructured data into workable datasets for data analysts. For example, data scientists might be tasked with looking through vast amounts of feedback or comment data to synthesize user attributes from them. These can then be used by data analysts on an ongoing basis to provide reporting and dashboarding to business users.
Data science vs. data analytics: which should I use?
Whether you should use data science or data analytics techniques depends on the types of questions you want to ask. If you have a lot of specific, straightforward questions that need answering on a regular basis, such as “How many women bought product X in the last month?”, you need descriptive analytics, and you should get a data analyst to answer this question.
If you have a large amount of data that you would like to explore to try to find new untapped revenue streams, then a data scientist can help. Data science techniques are also what you need to use if any of your questions are focused on the future–for example, if you want to use algorithms to predict the future or to tell you what the best course of action is. One exception to this is if you are happy for simple inferences to be made about the future by someone knowledgeable about the business, based on their interpretation of historical data. In this case, a data analyst can do descriptive or diagnostic analytics on the historical data, and a domain expert can use the results of this to make their own predictions about the future.
If you don’t know what you need, a data scientist is likely to have a more holistic understanding of the area, and will be able to help you decide. However, if you are looking to hire only one of these roles to get started, you should hire a data analyst, as they can get a good idea of the data landscape and prepare it for the more targeted work of a data scientist. If you have access to a data scientist contractor or one based in another team in your organization, you should definitely make use of any advice they can give you. It’s also worth noting the differences in salary: data scientists command much higher salaries on average due to their extra technical abilities in machine learning, data mining, and advanced statistics, so if you’re on a budget, make sure you plan to make use of their extra skills–there is no point in hiring a data scientist to do mostly SQL analysis.
If you have the budget, employ both and have them work together, as data science is best used to initially analyze macro sets of data, and data analytics is best used to draw conclusions about data on a micro-scale.
Data science focuses on the future, whereas data analytics focuses on past data. Data science also involves more technical statistical techniques and programming skills such as machine learning. Data analytics focuses on answering specific questions on a micro-scale, whereas data science is about asking macro questions (and also coming up with these questions in the first place). The two terms have often been confused in the past because two of the four main types of data analytics (predictive and prescriptive analytics) actually tend to be done by data scientists.
There are situations that call for either a data scientist or a data analyst; however, combining the two often will provide the best analysis and strongest understanding of a business, which can be used to make concrete decisions about the future. While they both have different focuses, data analytics and data science are often implemented in tandem.
There are many different understandings of what the data scientist and data analyst job roles entail, so make sure that your job description clearly details what you need, and check that candidates have the required skills before offering a job.
In this article, we defined data analytics and data science and looked at the differences between them. If you’d like to discover other areas related to data analytics, take a look at the other articles in our learning center.
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