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The world today is incomplete without data. Humongous amounts of data are generated by users every day. If this data can be somehow analyzed and interpreted to capture what the user wants and make innovations accordingly, we could bring in a revolutionary system where businesses can provide state-of-the-art solutions to the problems faced by a common man and that too at low costs. Better still, this system can improvise and improve itself to be more innovative by the day. This revolution is data science and involves data analytics, machine learning and much more.
In this article, let us explore big data, data science and then know how they are different from each other.
A common use case
Just like the name, big data means a lot of data – unstructured or raw. With increasing demands and interactive business models, the traditional way of the collection of data is not sufficient anymore. The humongous amount of data generated every day from various sources is called big data. Next, we need to have systems that can collate the data, filter it for the relevant target group, apply some statistical and machine learning models and predict future decisions based on the current data. Think of it as a feedback system. Data Analytics does a part of that – performing statistical analysis on sets of data to find answers to business problems. The rest of it – parsing the data, machine learning, predictive analysis, and visualization – in data science.
You must have seen this kind of intelligence in your Facebook feed. If you see a particular genre of videos or texts, you are shown with similar kinds of ads in the future too. On average, even if you spend about 10 minutes on Facebook, you can see a few videos of your interest and ‘like’ somebody’s posts. Well, all this data (big data) is collected by Facebook to keep track of your interests and disinterests.
Who uses this data?
Yes. Based on your selections, Facebook gives you next similar suggestions. For example, if you like Bournvita, you might get an ad about Cadbury drinking chocolate or some other similar drinks. On the other hand, if you choose not to see the bournvita ad on the first go, you will not be shown any other similar ads in the near future too.
Imagine how complex the system must be that caters to customization at such a minute level for each user!
This is the same way online shopping works too!
All of this is done through data analytics and data science.
In our article Data Analyst vs Data Scientist, we have detailed the responsibilities of these roles. You will get a fair idea of how both are related and yet different.
What is Data Analytics?
Through the above example, we see that there is a lot of raw data that is collected and can be analyzed in a proper way to get business benefits. Such an analysis of data to fetch information and get meaningful insights to solve a business problem is called data analytics.
Data analytics uses several tools and techniques to analyze the humongous big data as opposed to pure human intervention and manual organization of data. Data analytics involves the following simple steps –
- Determining the data requirements and grouping. This could be based on the target group or the business problem. Data can be grouped in any manner which is most appropriate, for example, age, location, gender, interests, lifestyle, etc…
- Collecting data from various sources online and offline – computers, physical surveys, social media, etc…
- Organizing the data for analysis. The most common method to organize data is in spreadsheet although frameworks like Apache Hadoop and Spark are picking up the pace to replace spreadsheets.
- Incomplete, inconsistent and duplicate data sets are removed and data is cleaned before analysis. In this step, any errors in the data are corrected and data becomes ready to be analyzed.
In data analytics, the data analyst already has information in hand – for example, a business problem, and works on a known set of data to provide descriptive, predictive, diagnostic or prescriptive analysis. Read more about these here.
Data analytics is becoming increasingly important in all the major domains like healthcare, finance, retail, tourism, and hospitality industries. Start your Data Analytics journey with our easy to learn tutorials.
What about data science?
Data science has a wider scope compared to data analytics. We can say that data analytics is contained in data science and is one of the phases of the data science lifecycle. What happens before and after analyzing the data is all part of data science.
In addition to the knowledge of programming languages like Python, SQL, etc like a data analyst, data science combines statistical knowledge and domain knowledge to produce insights from data that can drastically improve business. Data science experts use machine learning algorithms to any type of data – text, image, video, audio, etc… to produce AI systems capable of thinking like a human.
Data science has the following main components –
- Statistics – Statistics deals with the collection, analysis, interpretation, and presentation of data through mathematical methods.
- Data visualization – Results of data science are displayed in the form of visually appealing diagrams, charts, and graphs which makes it simple to view and understand. This also helps in quicker decision making by highlighting the key takeaways.
- Machine learning – this is an essential component where we use intelligent algorithms that learn on their own and predict human behavior as accurately as possible.
A data science expert identifies and defines potential business problems from various unrelated sources and gets data from these sources. Once data is analyzed through data analytics, a model is formed and tested for accuracy iteratively.
Data Science vs Data Analytics: Head to Head Comparison
Now that we are clear with each field, let us do a head to head comparison of data science and data analytics to get a clearer picture.
|Data Science||Data Analytics|
|Data Science is the whole multidisciplinary field that includes domain expertise, machine learning, statistical research, data analytics, mathematics, and computer science.||It is a significant part of data science where data is organized, processed and analyzed to solve business problems.|
|The scope of data science is said to be macro.||The scope of data analytics is micro.|
|One of the highest-paid fields in computer science.||It is a well-paid job, but less than that of a data scientist.|
|Requires knowledge of data modeling, advanced statistics, machine learning and basic knowledge of programming languages like SQL, Python/R, SAS.||Requires solid knowledge of database like SQL, programming skills like Python/R, Hadoop/Spark. Also requires knowledge of BI tools and medium level understanding of statistics.|
|The input is raw or unstructured data which is then cleaned and organized to be sent for analytics.||The input is mostly structured data on which design principles and data visualization techniques are applied.|
|Involves search engine exploration, artificial intelligence, and machine learning.||The scope is limited to analytical techniques mostly using statistical tools and techniques.|
|The aim of data science is to find and define new business problems that lead to innovation.||The problem is already known and with analytics, the analyst tries to find the best solutions to the problem.|
|Used for recommender systems, internet research, image recognition, speech recognition, and digital marketing.||Used in domain areas like healthcare, travel and tourism, gaming, finance and so on.|
|Involves finding solutions to new and unknown problems by discovering them and converting data into business stories and use cases.||The data only goes through thorough analysis and interpretation, however, there is no roadmap created.|
To sum up
This hierarchy diagram pretty much sums up the difference between data science and data analytics.
Image source here.
As you may have realized by now, Data science is vast and offers a more promising future. However, if you want to be closer to programming, Data analytics could be your best start. One thing is clear – both the fields are hungry for data and you need to work extensively with data to understand the whole picture. Data science includes the entire business process from involving stakeholders, storytelling, data analysis, preparation, model building, testing, and deployment. Data Analytics is one of the stages of data science – and a big one – where the big data is analyzed and insights are extracted and prepared in the form of graphs, charts, and diagrams. It is easier to move up the ladder from data analytics to data science. Read our comprehensive list of data science interview questions to grab your dream job today.
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