Data is something that all of us use every day in different forms, whether it is email, social media websites, or even our own personal information. As quoted by eminent marketing and customer experience expert, Jay Bear, “We’re surrounded by data, but starved for insights.” – (Ref: https://buffer.com/resources/bufferchat-with-jay-baer-youtility-marketing/)
This quote clearly highlights the importance of data and the ability of an individual to use data to draw meaningful insights. It is the data alone that allows an individual (through proper analysis) to identify the cause of a certain problem, establish connections, and visualize it to serve different purposes in our day-to-day life.
Data analytics and data science as we know them today actually date back to the early 1900s. But what exactly do these terms mean? Are they different? Why should I care? How can we use this knowledge to our advantage today?
Data Analytics versus Data Science
In present times, data analytics and data science are two different terms that are often used interchangeably, but they are actually quite distinct from each other.
Data analytics may be understood as the use of data analysis techniques to make predictions about future events based on past trends. It is often used to predict customer behavior, market trends, and business performance. The main goal of data analytics is to identify patterns and relationships in data sets.
Whereas, data science may be understood as the application of data analysis techniques to extract information from data sets. It is often used to analyze big data sets that may have been collected over time and across many different locations
. The goal of data science is to find patterns and relationships in these data sets.
The fundamental goal of both data science and data analytics is to help people understand their world. Both data science and data analytics are tools that allow us to gain insight into our environment. In order to gain meaningful results or insights, we first have to go about collecting data. We then apply data analysis techniques to those data sets to learn something about our environment.
There are primarily three major differences that exist between data analytics and data science:
Scope of Work
Data science deals primarily with understanding patterns in data; whereas data analytics looks for answers and solutions in the data.
Data science uses structured data sets, while analytical methods work best with unstructured data.
Data science primarily places its emphasis on discovering relationships among variables (predictive modeling), while business goals focus on solving problems (data analysis).
However, if we go deeper, we would find that there are several other differences that also exist between data analytics and data science, taking into consideration the skills and the job role.
Let’s understand them in detail.
1. Skills and Tools
In data analytics, an individual is required to possess
- strong skills in the fields of statistics, modeling, databases, and so forth
- proficiency in using Microsoft Excel and SQL databases, and
- knowledge of major statistical tools, such as SAS, R, or Python.
In data science, an individual is required to possess
- expertise in using different tools of big data, such as Spark and Hadoop, and
- sound knowledge of programming languages, such as Scala, R, and Python.
2. Job Roles and Responsibilities
The primary responsibilities of a data analyst include
- identifying new patterns using several tools used in the field of statistics,
- performing exploratory data analysis, and
- developing ways to visualize the data and establishing KPIs.
The primary responsibilities of a data scientist include
- identifying new patterns or trends in data to draw predictions about the forthcoming period,
- drawing meaningful insights using machine learning techniques and scientific algorithms, and
- ensuring the integrity of data by properly processing, cleansing, and verifying it.
Data science and data analytics are often recognized as the most valuable skills for any individual who is willing to enhance productivity, maximize overall revenue, or reduce operational costs. In the present world, businesses ought to use these skills so as to come up with new ways to make money and also to enhance the overall experience of their customers.
For example, consumers in present times expect companies to offer personalized products and services to them. Additionally, companies these days are somewhat obligated to respond immediately to consumer reviews, complaints, or requests. There are many organizations that are looking for resourceful ways to leverage big data to boost their marketing campaigns. Therefore, if you are an individual who enjoys solving complex problems, you are likely to gain a competitive advantage by becoming a data scientist. Or maybe you would rather join a team that helps clients understand data and make smart and informed decisions. Either way, you will be facilitating your organization’s growth well.
Data Science versus Data Analytics: Which One Is Better for You?
Data is now used by every company that is expanding its business on a national, international, or global scale to provide information to potential stakeholders and to maintain critical insights into the business.It not only helps them create a positive image in the market, but it also allows them to explore new fields and areas of work. Data is mainly maintained with the help of a data scientist or data analyst.
If an individual wants to build a career associated with data, then it is quite confusing whether being a data scientist or data analyst will be more profitable or not. However, at first, it totally depends on the skills, capabilities, and educational qualifications that an individual has.
While comparing data science and data analysis, data science involves exploring innovative ideas for the betterment of existing methods or tools. On the contrary, data analytics involves critical analysis or hypothesis of the problems with the aim of making the best decision. Data science involves research and evaluation as it focuses on historical data to form results, while data analysis involves creativity, learning, or innovation as it requires machine learning and focuses on predictive modeling.
If you have experience of more than 2 to 5 years in the related field, then you can do the professional course in data analytics that can provide you with exposure to tools such as SQL, Python, Power BI, Tableau, and Microsoft Excel through which you can outshine in your career in the fields of data analysis tasks and the tasks that mainly involve creating dashboards.
Data analytics can empower the following individuals:
- QA engineers
- data warehouse professionals
- individuals who are interested in the domains of marketing, sales, or finance
On the other hand, if you are a professional with experience of 1 to 10 years, then data science course can be a great fit which will give you intensive learning of Python. For the following individuals, data science can be a great option:
- IT application engineers
- BI engineers
- data analysts who want to polish their skills
Although both careers have their own challenges, surprises, and exposure, each demands dedication to grow in any of these fields where you need to embrace your skills technically, professionally, and personally.
Tools used in Data Analytics and Data Science
If an individual opts to pursue data analytics or data science as a profession for the career, the following are the major tools that will be predominantly used by them:
- SQL: It is the abbreviated form for “structured query language.” It can be understood as a programming language that is useful for creating databases.
- R: It is a statistical computing environment and a programming language that is primarily used for statistical analysis.
- SAS: It is a business analytics platform and is primarily used to analyze data from different sources and perform predictive modeling.
- Hadoop: It may be understood as a framework or structure that is primarily used for storing (repositing) and processing big data sets across clusters (groups) of commodity hardware.
- Spark: It is a cluster computing system based on the Apache Spark project. It provides a unified abstraction layer for both batch jobs and interactive workloads.
- Tableau: It is a business intelligence tool that helps in the visualization of the given data.
Terminologies Related to Data Analytics and Data Science
The following are the common terms that have been discussed so that you may not have to specifically browse them on the Internet during a conversation.
- Data mining may be understood as the process that primarily involves discovering patterns in data sets. A data set could refer to any collection of records. The goal of data mining is to find useful information in these collections.
- Big data is recognized as a subset of data analytics. It signifies the vast amounts of data and is often characterized by high velocity, variety, and volume.
- Exploratory data analysis, or EDA, is a popular term commonly used in the field of statistics in order to describe any type of statistical analysis performed on a data set before performing any kind of statistical tests on it.
- Data cleansing may be understood as the act that primarily involves removal of errors from data. This can be done manually or automatically.
- Business intelligence may be understood as the use of data analysis tools with the aim of improving the decision-making process in the businesses.
Learning Tips to Master Data Science and Data Analytics:
If an individual wants to learn about how to master data science and analytics, here are some useful tips that can be followed:
- Learn how to program: Programming is a skill that many people do not even know how to do yet. There are plenty of free online courses out there to help teach you the basics of coding.
- Get comfortable with Microsoft Excel: Microsoft Excel is identified as a useful tool that is often considered the database of choice for data science. An individual should become at ease using it before moving on to more complex databases like SQL Server.
- Learn how to read data: Data scientists or data analysts should have the ability to suitably analyze and interpret data. It means knowing they should know the manner or approach to read different types of files and CSVs.
- Learn how to write code: If the individual wants to get technical about it, the individual should learn how to write the programs. An individual will be able to work directly with algorithms and create unique solutions to problems.
- Learn statistics: Statistics are what makes data science possible. An individual needs to learn how to use basic statistical software to perform calculations and make predictions.
- Learn how to visualize data: Visualization is a critical element that helps in understanding the complex data sets. An individual needs to develop skills in creating graphs, charts, and maps with a view to effectively communicate ideas and findings.
- Apply machine learning: Learn the ways to suitably apply machine learning.
There are various good colleges that offer data science degrees. As well, there are colleges that simply focus on teaching basic computer programming skills. An individual has a plethora of options available when it comes to pursuing courses related to data analytics and data science with minimal investment. By doing such courses or certifications, an individual can precisely understand how to perform basic data analysis tasks like pulling data from databases and working with Microsoft Excel spreadsheets.
An individual can also get exposure to advanced subjects also like artificial intelligence, and deep learning. In addition, an individual may also wish to earn a certification in specific domains like cyber-security, healthcare, finance, marketing, and retail management.
There are several online platforms that provide courses that can surely help an individual in evaluating the difference between these two fields where you will get professional exposure that will help you to choose the best field, learn and grow together. To nurture yourself in this field, you will get to use the various tools which will help you to understand the field of data science and data analytics in a better manner.
They along with these courses provide insights into the practical demand of the job where you can learn to run programs like SQL, Python, and Microsoft Excel specifically for data analytics and learn majorly about Python as it is used in machine learning, statistical programs, and analytical applications in the course of data science.
Make the right choice and start your journey of learning and creating a positive change in your career.
Anjani is a technical as well as creative content writer at Thinkful, a Chegg service. She is an outgoing person, and you will find her near books, arts and explore the miraculous world of technology. Connect with her on LinkedIn or Twitter.
Anjani is a technical as well as creative content writer at Thinkful, a Chegg service. She is an outgoing
person, and you will find her near books, arts and explore the miraculous world of technology. Connect
with her on LinkedIn or Twitter.