Data Science vs Data Analytics – What is the Difference?

Data Science vs Data Analytics

Let’s see the differences between data science vs data analytics. Beyond differences there are some similarities between data science and data analytics.

The tech world has evolved tremendously from the early 2000s, giving the world access to actionable insights and results in businesses to obtain. The big data is a major component of the tech world today to obtain several data sets.

Creation of big data requires proper understanding and tools to uncover the correct information. Big data technology is a software utility that analysis, processes, and extracts the right information from the exceptionally complex and large data sets.

Big data is analyzed for insights to lead for better decisions and strategic business moves. Big data and data science are the two largely used in literature to discuss potential benefits for better data-driven decision making.

Although these fall under the broad category of data analytics filed where data science explains the involvement of execution of different phases of analytics such as data manipulation, visualization, predictive model building. Whereas big data deals in analyzing and processing a large number of data sets.

So, it is confusing to differentiate between data science and data analytics, despite these two are interconnected they pursue different approaches. Let’s move on and know how they are different in terms of category and their result deliveries.

What is Data Science?

Data science is a broad term that includes a variety of models and methods to obtain the right information which is used to manipulate and analyze data. It a multidisciplinary field that focuses on finding actionable insights from large data sets of structured and unstructured data.

To obtain the correct information experts have used several techniques and methods which incorporates computer science, predictive analytics, statistics, and machine learning to resolve the large data sets to obtain a solution to the problem.

The career skills that are needed to become a data scientist should be deeper in three of the departments i.e., analytics, programming, and domain knowledge.

What is Data Analytics?

Now where data science is a house, data analytics is a room in it. It is related to data science but more specific and concentrated.

Data analytics focus on processing and performing statistical analytics on existing data sets. It is the science that examines raw data to conclude information. It involves applying algorithms or mechanical processes to derive insights and run data sets for meaningful correlations.

It involves creating methods to capture, process, and organize data to unveil actionable insights for problems and establish the best way to represent the data. It simply means data analytics is more about solving problems and generating results that can lead to improvement action.

Data analytics also includes different branches of statistics and analysis that helps to combine diverse sources of data and locate the connections while simplifying the results.

Skill sets required being Data Scientist and Data Analyst

Following are the skills required to be a data scientist.

  • Strong knowledge of Python, SAS, R, Scala
  • Experience in SQL database coding
  • Ability to work with unstructured data from various sources like video and social media
  • Understand multiple analytical functions
  • Knowledge of machine learning
  • Hadoop platform
  • Working with unstructured data

Following are the skills required to be a data analyst.

  • Knowledge of mathematical and statistics skills
  • Machine learning skills
  • Programming skills
  • Communication and data visualization skills
  • Fluent understanding of R and Python
  • Data wrangling
  • Data intuition
  • Understand PIG/ HIVE

The Difference Between Data Science and Data Analytics

Some of the main differences in data science and data analytics revolve around the automation of the analysis where data scientists focus on automatic analysis and productions with algorithms using programming languages like Python, whereas data analysts use stationary, or past data, and in some cases, will create predicted scenarios with tools like Tableau and SQL.

Data Science Data Analytics
  • Design and create new procedures for data modelling and production with the use of prototypes, algorithms, predictive models, and custom analysis.
  • Examine massive data sets and identify trends, develop charts, and create visual presentations to help in better and strategic decision making.
  • Analyze massive data sets in unstructured ways to reveal insights.
  • More focused on specific part of a larger process, involve actionable insights to be applied immediately on the problems.
  • Not concerned about answering a specific question.
  • Focused to answer questions already in mind with existing data.
  • Have broader insights to concentrate on questions to be asked.
  • Emphasize on discovering the answer to the asked question.
  • Broader term to include data analytics, data mining, machine learning, and other disciplines.
  • Focused to extract meaningful insights from different data sources.
  • A data scientist creates a question.
  • A data analyst finds an answer to the exiting questions.
  • Scope of data science is more.
  • Scope of data science is less.

Similarities in Data Science and Data Analytics

Beyond differences there are some similarities between data science and data analytics which are outlined above but to talk again, they both share common coding languages, platforms/tools to work with, and are problem-solving in nature.

Common tools include SQL, Tableau, but there are more to work on specifically.

Applications of Data Science

  • Internet Search

Through data science, search engines use algorithms to find and deliver the best result for the searches (or queries) in a few seconds.

  • Digital Advertisements

The whole digital marketing world uses data science algorithms whether it about displaying banners or digital billboards. As through digital advertisements, one can receive higher CTR as compared to that of traditional ads.

  • Recommendation Systems

The recommendation systems or platforms make it easy for users to find relevant products out of available billions of them which add a lot to user-experience. It is used for promoting products, user demands and relevance of information. It is also based on the history of search results of the user.

Applications of Data Analytics

  • Healthcare

Instrument and machine data are used in healthcare centres to track as well as optimize patient flow, treatment, and equipment used. Because the major challenge faced by hospitals is cost pressures to treat patients while proving the efficient quality of care with a scope of improvement always.

  • Travel

Data analytics optimizes the purchase experience through mobile or weblog and social media analysis. Travelling sights can gather insights through customer’s preference and wishes. The product is sold by comparing the current sales and increasing browse-to-buy conversations with the help of packages and offers (customized).

  • Gaming

Data Analytics helps to gather data to optimize and spend within and across the games. Gaming companies gain insights into the likes, dislikes, and the relationships of the users.

  • Energy Management

Many of the firms use data analytics for energy management which includes smart-grid management, energy distribution, and for automating the utility companies. It is more focused on controlling and monitoring of network devices, dispatch of crews, and management of service outages. Utilities are able to integrate with millions of data points in network performance to monitor the network.

We are surrounded by data from everywhere. The amount of data which is shared worldwide per minute is extreme nowadays. According to Forbes, data is growing faster than ever before. It will be 1.7 megabytes of new information that will be created every second for every human being in the world by 2020. So, our future lies between data which makes it important to at least grab the knowledge about the field.

Data science and data analytics share more than just their name and nature i.e., data, they also include some important differences. Whether it is data science or data analytics, I hope you found this article beneficial for their key differences and also useful similarities.

Mukta is a content writer at Asset Infinity, the provider of asset management software in every field and industry. She is a tech nerd by passion and a writer by interest. She writes a creative content from digital marketing aspect to provide high social media attention and increase search engine visibility.