Beginner’s Guide: Data Science vs Big Data vs Data Analytics

In today’s day and age, dominated by technology and mass consumerism, data is almost literally everywhere.

Just to give you an idea of the amount of data out there, IBM reports that in 2012, 2.5 billion GBs of data were generated on average every day. And that was 7 years ago, which in technology years is practically a whole era. According to Forbes, at this wild, constantly increasing pace of Data growth, by 2020, there will be 1.7 megabytes of new information generated every second per each human being.

 

 

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And it’s not just the amount of data that’s out there, but what it stands for – money, lots of it.

This is why JCU Online has compiled some essential data on the three main fields that together make up the professional Data landscape – Data Science, Big Data, and Data Analytics, so you can decide if you’d be interested in pursuing a career in any of them and how you’d approach it.

Data Science Data Analytics Big Data
Skills required
  • Business
  • Data mining
  • Machine learning
  • Analytical
  • Programming
  • Statistical
  • Mathematical
  • Machine learning
  • Communication
  • Data visualization
  • Analytical
  • Mathematical
  • Statistical
  • Business
  • Computer
  • Data visualization
Average Salary in the US() $117,345 $67,377 $116, 591
Qualification SAS and/or R; Python, C/C++, R, Pearl, SAS, Java, SQL SQL, Python, Matlab, R, SAS, and Excel Database development and management; some programming helps but not required
Education background Master Degree, PhD Bachelors in math, statistics, computer science, and/or something else closely related Bachelors in math, statistics, computer science, information management, finance, and/or economics

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Image Credits: mohamed_hassan/Pixabay

Data Science

The term “science” is more than befitting. Data Science is the most refined field of the three and requires the tightest niche expertise to process and sift through raw data in order to identify highly specific and valuable insight amidst all the background noise. Data Science is concerned with highly individual data.

In short, Data Science encompasses all the complex processes, tools, techniques, and mechanisms that go into the cleansing, analysis, and preparation of data, which ultimately results in clear, pinpoint estimates and highly proactive directions that help businesses drive growth.

These techniques and tools include statistics, problem-solving, mathematics, creativity, predictive analysis, machine learning, and sentiment analysis.

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Application of Data Science

Internet Search

This is perhaps the most important and ubiquitous application of Data Science, which ironically, tends to be the most inconspicuous as well. Have you ever wondered how Google can produce a plethora of accurate results of a search in the matter of milliseconds? Through Data Science.

Digital Advertisements

This is another big one. You’ve probably noticed and perhaps even feel watched when a weirdly relevant to you digital ad pops up in your feed or search screen. Something that specific can’t be a coincidence, right? Indeed, it isn’t – it’s the result of the algorithms that are Data Science’s bread and butter. Digital marketing altogether relies heavily on Data Science, which is why it’s become so successful over the last few years.

Recommender Systems

Recommendations are a huge part of the user experience, especially in fields like e-commerce. Those recommendations aren’t just some half-random, rounded up suggestions – they are strategically based on your search history and demands and have been selected amidst a billion other options.

 

Big Data

While Data Science drives business growth by focusing on one individual at a time, Big Data is concerned with the masses. It deals with unimaginably large data that certain businesses drown in on a daily basis. If it wasn’t for Big Data, all this information would go unprocessed and unutilized, resulting in loss of insight and opportunities.

Big Data examines both structured and unstructured information, the first coming from transaction data, Relational Database Management Systems, etc.,  while the latter is derived from emails, blogs, social media activity, etc. There’s also semi-structured data that is obtained from the likes of text files and system logs.
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Applications of Big Data

Financial Services

All institutions that provide financial services deal with endless amounts of data. They employ Big Data to create a structure to the chaos and identify patterns in key areas such as customer, compliance, fraud, and operational analytics.

Communications

The communication industry is booming, and the competition for subscribers – whether it’s about gaining or retaining them and/or upgrading their worth, is fierce. The key to success is streamlining the analysis of all this customer and machine-generated data and pinpointing strategies that work on large scales.

Retail

Again, brick and mortar stores can focus on every single individual too much as it wouldn’t be cost-efficient. Instead, it needs to pin down more universal strategies that would draw in large groups of consumers. Big Data helps retail businesses stay on top of all the data that comes from from customer transactions, social media, loyalty programs, etc.

 

Data Analytics

Between the three, data analytics is probably the most trivial, but utilizing it to its full potential can tremendously increase profits.

Data Analytics employs algorithmic and mechanical processes to juxtapose a number of different data sets in order to spot connections and patterns, drawing conclusions on consumer behavior and companies’ efficiency.
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Applications of Data Analytics

Healthcare

Healthcare institutions are usually torn between providing quality care and providing time and cost-efficient care.. Data Analytics help establishments gain a more in-depth understanding of patient flow, equipment use and treatment from a business standpoint in order to help them optimize their operation and reach a happy medium.

Travel

The user and buying experience determine the winning and losing companies in the travel industry. Through Data Analytics, companies can delve into consumer psychology and preferences and exploit them.

Energy Management

This is another field where Data Analytics is a game-changer, helping companies optimize their energy use and distribution, achieve greater automation, and respectfully cut down operational costs.

The Data business is showing no signs of slowing down, and with a career in any one of these three fields, you’re equipped to ride its momentum in full force.

-This is a guest post by Kevin McGowan from James Cook University.-

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