How is Big Data Different from Data Science?

How is Big Data Different from Data Science?

Keywords: Data Science, Big Data, Data science skills, Big Data skills Data Science has been making waves ever since digitization and disruptive technologies were evolved. So has been Big Data. In fact, Big data has been making noise even before data science came into being, or is it?

But what are they?

How do you define Data Science and what does Big Data mean?

Can we use them interchangeably or are they two different identities? Keep reading to know more. But before do the detailed comparison between the two, let’s have a look at the individual terms, their definition and is there any relation between the two.

Data Science & Big Data – the Definitions, Comparison, & the Relation!

According to the definition data science is nothing but a field of study, which blends programming skills, domain expertise, and in-depth knowledge of statistics and mathematics that helps you to deduct meaningful insights from the available data.

In another words, data science is a domain that not only includes but also involves working with a humongous chunk of data and use it for building both predictive and prescriptive models along with prescriptive analytical models.

Data Science is all about building a model, validating it and using the right data to deploy the best model created.

Data Science professionals apply numbers, images, videos, text, audio and most importantly machine learning algorithms to produce AI-based systems for the tasks that would usually require human intelligence. Result: Valuable insights, which business leaders and analysts can translate into business values that are tangible.

On the other hand – Big Data is nothing but a data set that is not only huge in volume but also exponentially growing with time. Big Data refers to the data sets that are not only large but are also complex in nature, which can’t be stored or processed using the traditional data management tools. Big data comprises huge amount of information that is volume; speed at which this data is being generated that is ‘velocity;’ scope of the data points that are covered that is ‘variety;’ and together this is known as Big Data’s ‘Three Vs.’

In another words, Big Data is nothing but the voluminous data or you can say it is the information or even the relevant statistics, which the businesses across the globe acquire to find solutions for their business problems. Big Data is huge enough to be processed using the traditional methods hence there are different software and tools to create and prepare big data.

Big data helps in discovering trends, patterns, thus helps in making decisions that are closely related to both human behavior as well as interaction technology.

Now that we know what each of the term individually means and stands let’s see how each of them fair against each other.

Points of Comparison Data Science Big Data Data Science is a data 1. Meaning Big Data on the other hand is a huge focused scientific volume activity. It is used to of data set that can’t be process Big Data and managed using the is like data mining. traditional data processing tools.

2. Concept Data Science is a Big Data comprises specialized area that miscellaneous data involves types that are generated techniques, from scientific programming various sources of data. tools, and techniques Big Data includes all to process Big Data. It formats and all types of data. is applied to extract meaningful insights from Big Data; and helps businesses in making data-driven decisions.

3. Foundation Basis

Data Science applies different methodologies to extract information from Big Data. Data Science also helps in extracting complex patterns from Big Data to create models.

Big Data is received from various sources like traffic from internet; online discussion forums; transactions, spreadsheets, and emails; it is also generated from the system logs.

4. Where it is Applied

You can apply data science for your internet

Big Data can be applied to performance

search, digital advertisements, search recommenders, amid other areas.

5. Approach Followed

Data Science uses statistics, mathematics, and other tools extensively. It also uses programming tools like SQL or NoSQL data visualization amid other things

optimization, health and sports, bettering business, and telecommunications amid others.

Big data is used for agility in business, gain edge over competitors, and to achieve sustainability in the business amid other things.

After comparing the two, you will see that both data science and big data are related.

In simple words, data science is an umbrella term, which encompasses entire tools and techniques that are used during the life cycle stages of the data which is useful.

In comparison, Big Data is nothing but huge data sets, which need specialized and most often than not innovative techniques as well as technologies for an efficient use of available data.

Data Science Trends in 2021 While data science was being written off in 2020, there has been a different wave this year. According to the experts, data provenance will gain prominence. As per the experts, the industry has not yet taken the data provenance assessment as seriously.

Citing some of the bad examples where the use of some unchecked data that has led to more discrimination and misinformation.

The latest trend in Data Science usage has been the applications of Generative Adversarial Networks aka GANs. For instance, the Deepfake of the British queen Elizabeth had stirred the masses and there were mixed reactions to the video. However, the purpose of the video was to bring the ‘trust’ factor in the discussion.

There will be varied uses of data science in the coming years, as experts predict.

And aspiring professionals in data science field will need to constantly upskill or reskill their data science skills to stay relevant in the industry.

Why Industry Chooses Data Science Over Big Data There is no debate about which is better, however, industries; organization; and even businesses choose Data Science professionals over Big Data professionals because data science makes data better.

1. Data Science is more versatile as compared to Big Data.

2. Data Science is always evolving while Big Data will continue to grow, however, it will be data science that will give that data some meaning.

3. Data Science skills are continuously evolving whereas Big Data still uses old skills.

While Big Data is growing by leaps and bounds, so is Data Science evolving at a faster pace and will continue to do so. Reason: Businesses are becoming data driven and innovative, and to keep up with the tremendous growth, organizations need professionals with latest data science skills.

Are you ready to become the most sought-after data science professional? The path to success is keep upskilling, reskilling, and upgrading your knowledge.