Data Analytics vs. Data Science: What’s the Difference? While the terms data science and data analytics may seem to be similar by appearances alone, they actually are quite different in their definitions and practice. First of all, data science is an umbrella term for all the different methods used to extract and analyze big data. Secondly, Data analytics, on the other hand, is the specific practice of analyzing raw data to draft up actionable insights and conclusions about it. Read up the Big Data Trends Analytics 2020/2021 (& Years to come).

Now, the growth of digital data is projected to increase drastically–Forbes speculates 1.7mb of data will be created every second for each human on the planet by 2020–meaning it’s pretty important to have a basic understanding of big data’s contributions to business intelligence. When it comes to data science vs. data analytics, think of it this way: data science is the future, and data analysis is the future in action.

Data Science vs. Data Analytics: At a Glance

Since their functions are interconnected, data science and data analytics can be thought of as two sides of the same coin. They work together to reach a common goal, and one would be fruitless without the other. Here’s a chart that can help you better understand data science vs. data analytics, and how the two work together:

Data Science Data Analytics
Core FocusThe collection of large amounts of raw data.Finding smaller patterns in raw data, with the intention of using those patterns to improve certain processes.
Goal“What data should we collect?”“What can we conclude from this data?”
Applicable Fields Search engines, online shopping, digital marketingTravel, healthcare, energy
Typical Background & Skill Set of SpecialistsMathematical and statistical knowledge and hacking skills. Skills include machine learning, software development, data analysis, and object-oriented programming.Mathematics and statistics. Skills include data mining, data modeling, R or SAS, SQL, statistical analysis, database management and reporting.

What Is Data Science?

Data science relates to everything involving structured and unstructured data: sanitation, development, and analysis. It’s a combination of programming, statistics, mathematics, machine learning, problem-solving, data collection, predictive and statistical analysis, and more. Data science determines what data is gathered and stored, as well as how that data is organized and used to identify patterns.

Search engines may use data science to create the algorithms used to produce the best results for a specific search query within a few seconds. Marketers also use data science to improve their CTR ratios, and data science is frequently used throughout the digital marketing spectrum to create better-targeted audiences. Shopping websites utilize the data science to create recommendations and improve the relevancy of future search results based on a customer’s previous search data.

In recent years, artificial intelligence has started to show a lot of promise in the world of data science through machine learning. Machine learning has been able to create new algorithms and processes simply by learning from the behavior of the data system’s users. Data science is then able to use machine learning to gain new, unbiased insights into the patterns that raw data forms, allowing data scientists to create more innovative algorithms with broader spectrums than ever before.

What is Data Analysis?

Data analysis involves the examination of raw data for the purpose of gaining actionable insights and conclusions based on the available information. Algorithms or mechanical processes are used to find meaningful correlations between different data sets. Since raw data has quickly transitioned into being big data, this process is often too complex for any human data analyst to complete by hand.

Artificial intelligence with machine learning is now being utilized to process these algorithms. Many times, these systems are also able to pick up on data set patterns that a data analyst would normally miss on their own, allowing the data analyst to create more accurate actionable insights. These actionable insights assist companies in making decisions as well as proving or disproving pre-existing models and theories. Data analysis primarily relies on the data analyst’s ability to infer conclusions based upon the observational patterns they find in raw data.

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Also, the medical industry has been using data analysis to treat as many patients as efficiently possible. Their machine and instrument data is being used increasingly more to help track and optimize equipment, treatment, and workflow. In the travel industry, data analysis is utilized to provide actionable insights into the customer’s preferences and desires. With data analysis, travel websites can create personalized recommendations based on customer’s social media, as well as up-sell additions to a current purchase by referring to previous search data.

Furthermore, the most significant way in which data analysis benefits any business is in energy management. Energy optimization, smart-grid management, building automation, and energy distribution services help monitor and control work teams, service outages, and network devices, allowing these processes to be more efficient than ever before.

Data Science vs. Data Analytics: Which One Should A Company Use?

Where data science can be defined as the collection of raw data and the creation of new algorithms to be applied to it, data analysis can be defined as the conclusive research of data science’s results. If a company wishes to start making use of their big data, they will need to first turn to data science to determine what data should be collected and what algorithms are best to use for organizing that data into data sets.

Once the company has developed these systems, it is time to start data analysis, or analyzing the data sets produced by these processes. In other words, data science will create a broad spectrum of data that data analysis requires for research. Once the data analyst has presented their conclusions, the company can use these insights to create better plans for implementing new offerings and maintaining business.

It Varies Depending on Company

Technically, there is no battle between data science vs. data analytics. It all comes down to what the company is seeking to do. If the company already has a large collection of raw data and algorithms for processing it, they may want to skip ahead to data analytics to learn how they can use that data to create well-informed business plans and decisions. However, if the company hasn’t gotten this far yet, they will need to start out with data science so they can produce the raw data that a data analyst needs in order to create actionable insights.

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