Data Science vs Data Analytics: An Easy 3 Step Beginner’s Guide

Introduction

Today, in this data-dependent world, Big Data technologies are highly beneficial for organizations. The fast rise of Internet-reliant businesses has led to a huge spike in demand and need for data storage, analysis. Managing huge data sets is a challenging task; thus, it has become every organization’s necessity to hire skilled Data experts.

The motive for companies to collect and store user data is because their business depends on it. Moreover, information nowadays is not collected or stored in the traditional way; more than 90% of the company generated data is unstructured and is commonly known as Big Data. In this article let us look at Data Science vs Data Analytics.

For people who love to decode and play with numbers, it is the right time to start considering a career as a Data Analyst or Data Scientist, as are two of the hottest fields in tech (and high paid, too). 

Most people that have a basic understanding of Data Science also have confusion about the Data Scientist and Data Analyst roles. Data Science vs Data Analytics has always been a topic of discussion among learners. Before starting a career, it’s very important to understand what both fields offer and what the key difference between Data Science and Data Analytics is. 

So, what is Data Science vs Data Analytics, and how do they both differ? Are Data Science and Data Analytics the same? Let’s jump right into it!

In this article let us look at:

1. What is Data Science?
2. What is Data Analytics?
3. What is the difference between Data Analytics and Data Science?

1) What is Data Science?

In this data-concentrated world, business data is a serious asset, and Data Science is one of such interdisciplinary fundamentals that plays a huge role in helping businesses utilize these data sets to generate revenue. 

Data Science is a grouping of various disciplines – Mathematics, Computer Science, Statistics, Information Science (IS), Machine Learning (ML), Artificial Intelligence (AI). It comprises concepts like data scraping, data interpretation, analytical modeling and uses Machine Learning algorithms to extract valuable patterns using the complex datasets and later transforms them into valuable business strategies.

Besides the definitions and interpretations provided, Data Science can be defined as a scientific field which develops appropriate methods, concepts, technologies, and applications for data.  These methods range from the development, representation, storage of data to its search, allotment, privacy, security, analysis, knowledge, exhibition, visualization, and more. 

2) What is Data Analytics?

Three fundamentals construct Data Analytics: Mathematics, Statistics, and Statistical Analysis.

In general, Data Analytics is referred to the process involving dataset investigation to yield inferences about the evidence that data contain. Data Analytics procedures provide you with insightful rare data and expose patterns to extract valuable business information from it.

Various Data Analytics techniques generally involve dedicated systems and analytical software that participate with Artificial Intelligence algorithms, automation, and many other capabilities. Data Analysts work with Data Analytics methods and techniques in their data research, and companies make use of it to uniform their decisions. Data Analysis can aid companies in better management of their stakeholders, assess their ad campaigns, brand contents, generate content strategies, and increase product sales. Eventually, companies can use Data Analytics to increase business ventures and improve their stock. 

The data generated by companies include historical data or new information. They store it for future decision-making. The data is collected primarily from users, site visitors, and viewers of their potential business competitors. First-party data is collected from the customers. The data generated or collected from competitors is called second-party data. Also, the collective data purchased by the company from a selling-company is called third-party data. This data is used by companies to provide the audience’s demographics, picture interests, understand behaviors, and more.

So, are Data Analytics and Data Science the same? If they are, then which is better Data Science or Data Analytics?

3) What is the difference between Data Analytics and Data Science?

Let’s consider the roles handled by a Data Scientist (expert in Data Science), a Data Analyst (expert in Data Analytics), to provide an insight into the key difference between Data Science and Data Analytics with examples.

Data Analysts

Data Analysts assemble data collected by their organizations from various sources. They perform probing on data to present it in a better way.

• Data Analysts use SQL for the data query
• Data is analyzed using Tableau, Excel, and other tools
• Processed data is then used for business forecasting 
• Business intelligence software is used to create dashboards
• Graphical Analytics, Statistical Analytics, and Predictive Analytics are applied to make data presentable

Data Scientists 

Data Scientists are accountable for the detection of facts unseen in the compound network of unstructured data. Data Scientists are mainly responsible for the following tasks.

• Using APIs for data mining and various web scraping tools
• Cleaning data and making it usable with R or Python language
• Performing statistical analysis involving Machine Learning, Logistic Regression, Random Forest, Natural Language Processing, etc
• Programming and automating techniques involving libraries, which simplify companies’ day-to-day operations

Conclusion

There’s a very fine line when it comes to Data Science vs Data Analytics since both of them fall on similar scales. But, there’s a reasonable share of differences between Data Analyst and Data Scientist job roles.

Becoming a Data Scientist requires more effort when compared to a Data Analyst. If you are good at statistics, mathematics, and programming, then Data Science is the best fit for you.

For an early start and to have room for practising programming, it’s a wise decision to work towards becoming a Data Analyst. Learners and professionals need to choose the right path, which fits their interests. Only theoretical knowledge is not enough; hands-on programming with tools is also essential to catch hold of a good opportunity. Our Integrated Program In Business Analytics is one of the best choices available in the market. It is a 10-month online live program, designed by highly experienced experts, which prepares learners from diverse professional backgrounds to become data-smart Future Leaders.

Also, Read

Data Science vs Machine Learning: Everything to Know in 7 Easy Points

Importance of Data Science: A Simple Guide in 9 Steps