Everyone has heard of the buzzwords of the millennium – data science, data analytics, machine learning, big data, etc. But how many actually know what these terms mean? What their applications are? And how these terms are different from one another?

 

For a layman, there may not be a lot of difference, but when it comes to striving professionals looking to build a career in this domain, it’s key to understand the difference between big data and data science. This understanding will determine which courses they will pursue, a big data analytics online course or data science certification and what kind of jobs they’ll land.

 

So let’s help you discover the difference between these two domains so you can make the right career choice.

 

What Is Data Science?

 

It is a field that deals with structured and unstructured data and comprises everything ranging from data cleansing, preparation to analysis.

 

Data science combines mathematics, statistics, programming, problem-solving, and collating data to look at things differently by aligning and analysing said data. Furthermore, data science is an umbrella term that encompasses various techniques used while extracting insights and information from data.

 

What Is Big Data?

 

Big data deals with massive volumes of data that cannot be processed effectively using traditional techniques. Big data processing begins when raw data is collated from multiple sources and is then stored in a single computer’s memory. Furthermore, it is used to analyse insights, leading to better, more efficient business decisions.

 

Now you know what these two domains entail. Let’s understand the fundamental differences between the two worlds.

 

Key differences – Big Data vs Data Science

 

  1. Usage

 

Organisations use big data to enhance efficiency, understand their customer better, and increase competitiveness in the market. On the other hand, data science is used to provide modelling techniques and methods to evaluate the potential of big data in a precise manner.

 

Furthermore, data science uses theoretical and practical approaches to dig information from big data. Contrarily, big data is considered as a pool of data with no credibility unless analysed using data science techniques.

 

If you aspire to learn in-depth about big data and analytics, consider enrolling for a credible big data analytics online course. A data analytics course can help you understand the A-Z of the domain. However, if you aspire to build a career in the data science domain, you may enrol for business analytics courses online from a reputed institution like XLRI. The business analytics courses online from XLRI aims to provide a 360-degree understanding of business analytics, data analytics, and data science. What’s more, business analytics courses online from XLRI can also help you understand the difference between big and data analytics so you’ll have a better view of the domains.

 

  1. Applications

 

Common Applications of Data Science

 

  • Internet Search

 

Google, Bing, and other such search engines leverage data science algorithms to provide the best results for queries in a matter of seconds.

 

  • Digital Advertisements

 

The digital space runs on data science algorithms ranging from display banners to billboards. In fact, data science is the main reason that digital ads have higher click-through rates comapred to traditional advertisements.

 

  • Recommender Systems

 

Do you know the recommendation based systems most e-Commerce website are leveraging today? Amazon, for instance, provides users recommendations and product suggestions based on the search query and relevance of information. Data science is the secret behind these recommender systems.

 

Common Applications of Big Data

 

  • Communications

 

The telecommunication industry survives on the engine known as big data. Big data solutions help these companies combine and analyse massive amounts of customer-generated data, which is used to target them to gain new subscribers, retain customers, and expand their current subscriber base.

 

  • Financial Services

 

Financial services are big on big data. Credit card companies, retail banks, insurance firms, private wealth management advisories, and investment banks all use big data for customer analytics, compliance analytics, fraud analytics, and operational analytics.

 

  • Retail

 

Whether it is a brick and mortar store or an online retailer, the answer to staying in the game and remaining competitive is to understand the customer better. And this requires the ability to extract and analyse customer data from all sources, including web blogs, transaction data, social media, etc.

 

  1. Focus

 

Data science focuses more on decision-making areas of businesses, whereas the cornerstone of big data is technology, computer tools, and software. Big data analysis caters to a massive amount of data set, which is also known as data mining. However, data science uses ML algorithms to design and develop statistical models to generate information from the massive big data sets.

 

Fascinated by data science or big data? Not sure yet? Well, whichever domain you choose to build a career in, the pathway is clear. For a career in big data, a big data analytics online course is the ideal choice, and if you go down the data science path, you can consider enrolling in a credible data science course, or even a business analytics course can do the trick. For an in-depth understanding of the advantages and disadvantages of a business analytics course for your career, read here.

 

Read on to find out the skillset you’ll need to thrive in these domains.

 

  1. Skills Required to Become a Data Scientist

 

  • Fundamentals of Data Science
  • Statistics
  • Programming knowledge
  • Data Manipulation and Analysis
  • Data Visualization
  • Machine Learning
  • Deep Learning
  • Big Data
  • Software Engineering
  • Model Deployment
  • Communication Skills
  • Storytelling Skills
  • Structured Thinking
  • Curiosity

 

Skills Required to Become a Thriving Big Data Engineer/Specialist

 

  • Programming Expertise
  • Data Warehousing
  • Computational frameworks
  • Quantitative Aptitude and Statistics
  • Business Acumen
  • Analytical Skills
  • Data Visualization Skills
  • Familiarity with Big Data Tools
  • Skills of Data Mining

 

  1. Salary Outlook

 

Every professional earns a varied salary in both the domains. However, the pay scale is more or less the same. The average base salary of an early level data scientist is approximately ₹698,412. On the other hand, the average base salary of a big data engineer is around ₹ 8,56,643.

 

Over to You…

 

Now you know the difference between data science and big data; you know what skills you need, the salary you can earn, and even the application of the domain and the path you need to take for each field.

 

It’s your call now whether you choose to enrol for a big data analytics online course or a business analytics/data science certification like the business analytics courses online from XLRI.

 

 

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