“Big Data Analytics, Data Science and Big Data classes soon! Sign up to learn the next big technologies! “Does this ad seem to be familiar to you? Whether you’re a techie or not, don’t you come across these terms quite often? On the surface, Big Data Analytics and Data Science looks the same and are used interchangeably. A quick Google result probably didn’t help, it may have just confused you further.
This blog covers what these technologies are, how different they are, and how much they overlap.
Data v/s Big Data
What makes data truly Big in nature isn’t just the huge volumes being produced every day but also the Variety and the Velocity at which it is being produced. Read more here.
Analysis and Analytics – the same concept?
Data Analysis is a broad spectrum that includes Analysis of all kinds, on data sets of all sizes. At a basic level, working with functions and formatting data in Microsoft Excel is an example of Analysis.
Businesses used excel for a long time. With the increase in the volume of data, Excel could not remain the does it all tool. Analysis tools had to fit “bigger” data as well. Therefore, development of new tools became a necessity to deal with Big Data. This led to the birth of Hadoop.
Analysis largely deals with analysing past data and understanding the data. Analytics deals with using these insights to make smart business decisions in the future.
What is Big Data Analytics?
Big Data becomes an asset when it becomes possible to mine analysis and insights from it. This is where Big Data Analytics comes into the picture. The process of mining useful information (i.e. relevant and useful insights from raw data) from the plethora of data being generated to make smart business decisions, is Big Data Analytics. (This is how the word “information” differs from the word “data”- other pair of words that are used interchangeably.)
What is Data Science?
Data Science is the science that uses smart mathematical and statistical models to mine information from data. It is a multidisciplinary field that involves Statistics, Programming and Domain Knowledge. Data Science uses a host of smart Machine Learning algorithms to make smart and informed decisions about data.
Michael E. Driscoll, the CEO of Metamarkets, said “Data scientists: better statisticians than most programmers & better programmers than most statisticians”.
This basically sums the entire field up!
If you are a newbie to Data Science and want to understand what it really is read this very informative blog on Data Science!
Big Data Analytics and Data Science – Is there a winner?
Broadly speaking, Big Data Analytics can be called Data Science, but Data Science cannot be called Big Data Analytics. On the surface, they perform the same operation – i.e. mining useful information from data. So, the two fields overlap significantly and often work hand in hand. Big Data Analytics involves mining useful information from raw data. Data Science uses Machine Learning to make future predictions. This is done by training the computer to learn without being explicitly programmed. Machine Learning is what makes it different from Analytics. The Machine Learning Algorithms used are – Decision Tree Learning, Artificial Neural Networks, Deep Learning, Clustering, Random Forest Classifiers, Naïve Bayes, Regression and more. The use of Machine Learning makes computers even smarter – which makes Data Science so sought after these days.
Do Big Data Analysts and Data Scientists differ?
Data Science has entered the realm of Big Data recently. Big Data Analytics has been around for a little over twenty years. Experts are still trying to develop a clear definition about the differences between Big Data Analytics and Data Science. However, as we delve deeper, we notice discernible differences. Here are some of them –
Are there any stark similarities between the two fields?
There seem to be some significant differences between the two fields. Does this mean that they don’t coincide at all? No. There are quite a few similarities –
- Develop useful insights from raw Data
- Work on Big Data
- Attempt to use the insights achieved to make smart business decisions
- Applied in similar fields, like healthcare, finance, social media and sports.
Did you know? The Harvard Business Review named Data Scientist as the sexiest job of the 21st century!
Can you become a Data scientist and Analyst?
Are you fed up with your current job and want to shift to the hottest new profession on the block? Here are some of the requirements the fields demand –
- Good Statistical and Mathematical skills
- Good programming skills (Python, R, Java)
- The ability to ask the right questions (given a data set)
- Knowledge in Machine Learning
- A fast learner
- Good Analytical skills
- A keen business mind
- The ability to analyse the results after the application Analytics tools to data
- Knowledge in dealing with Analytics tools like Hadoop and Hive
- A fast learner
If you think that, you have (or can acquire) these abilities, then these fields are for you!
Which industries use Big Data Analytics and Data Science?
While there is a major overlap between Data Science and Data Analytics and essentially, as essentially they perform the same operation, the two fields have some concrete differences.
Either way, they are making our lives easier every day, usually without us even realizing it! But, if data amazes you and you’re smitten with this Big Data revolution, then hop on to the bandwagon and join us! Read more articles on Big Data Analytics, Data Science, Machine Learning and much more!
Analytics or Data Science? We’d love to know your thoughts on the two and whether you’ve ever used these words interchangeably.