Today, we are all surrounded by the data, stats, and information. Still, a lot of people are unaware of the term ‘Data’ and always looking for answers to questions like “What is Data Science and Analytics? What is data science used for? What is Data Analytics used for? What is big data analytics? What is big data and how is it used?” etc. Well, so many big questions and so, let’s find the answer of all these questions and understand stepwise, what are these and what skill sets are needed to specialize in them.
Data is ubiquitous and is proliferating at a pace that no human or technology can control its rapid growth. As per the statement released by the American multinational technological giant IBM, 2.5 billion Gigabyte (GB) of data has been generated each day as on 2012. While an article posted on the globally renowned business magazine Forbes predicted that 1.7 Megabyte (MB) of new data would be produced per second by 2020 or even before. This immensely expanding data storage can be explained with an example. We make nearly 40,0000 search queries per second only on Google search engine, other search engine excluded, which makes the number of annual search queries to more than 1.2 trillion. Isn’t it unbelievable and breathtaking?
Seeing such a huge set of data expansion, it is quite evident that the field of Data is going to witness no dearth of career opportunities in near future. However, a lot of people get confused between the terms Data Science, Big Data and Data Analytics. Here, we are sharing some piece of information that will resolve this dilemma.
Data Science, a field that came into the existence in this very decade, is a multidisciplinary field and an amalgamation of data mining, technology, algorithm development and software engineering. In a nutshell, it is the field that not only analyses and observes the given raw data and discovers insights, but also find the reoccurrence of the specific events or actions and helps decide what is best for the business. Further, Data Science helps improve business growth via its ability to mine customers’ data and transactions and create the suitable campaigns accordingly. With the technological advancement, artificial intelligence and devastating flood of information, the need for analytical data experts has emerged to rescue us from the colossal structured and unstructured data.
Many large scale companies, nowadays, hire Data Scientists to find out the valuable piece of information from the clustered data. These experts primarily understand the pattern, behaviors, trends or characteristics of the given data and investigate leads and insights required for making strategies to achieve the business goals.
Skills Required to Become a Data Scientist
- Programming: An aspirant applying for data scientist jobs must have a good knowledge of programming languages such as Python, R, C++, Java, SQL, and SAS etc. These programming skills help you analyze large databases more efficiently.
- Statistics and Machine Learning: Data Scientists must have an expertise in statistics and machine learning as it is the basic requirement for data scientist jobs. As a Data Scientist, you are required to run algorithms in order to understand the basics of statistical analysis.
- Strong Business Intelligence: Along with the technical skills, a data scientist must have a great hold of the business insights as it helps you understand your business model and seek new business opportunities.
Big Data, as the name suggests, is the data with an enormous volume that the existing obsolete applications are incapable of processing and dealing out. Usually, Big Data can be defined by the three V’s viz. Volume, Velocity and Variety.
- Volume: A company that owns the Big Data has a variety of sources it collects the data from, including the business transactions, social media approaches, machine-to-machine communication and many more.
- Velocity: Velocity of data refers to the speed at which the data is streaming. For an instance, the social media giant, Facebook has to deal with as many as 900 million photos in a single day. Hence, you can imagine how such a big data is managed and processed on a daily basis with failures.
- Variety: The data captured by the companies are not necessarily in a structured form, it can be taken in the variety of formats such as the numeric data, text documents, financial transactions, emails, photographs, videos and lot more.
Skills Required to Become a Big Data Specialist
- Analytical Skills: Analytical skills are one the most important skills one should possess in order to become a Big Data Specialist.
- Creativity: Since the Big Data is large in volume, one should have a creative mindset to play around with such a humongous volume of data.
- Computer Science: Owing to its gigantic size, the Big Data cannot be processed without heavy algorithms, and therefore proficiency with Computer Science is very useful.
- Business Skills: Big Data is all about analyzing insights from the large volume of data that can be used to make the great business decisions and strategies.
Data Analytics, on the other hand, is the process to use the qualitative and quantitative techniques to scrutinise the raw data to get the conclusions about the information that can be used to enhance the productivity of the business. In a nutshell, Data Analytics is about the extracting the unstructured data and classifying the piece of information that can be helpful to grow the business.
Data Analytics is the process that serves the Business-to-Consumer (B2C) applications largely. The renowned international associations and businesses tend to collect and analyse large data that can be linked to consumers, businesses and market finances etc. It predominantly analyses the consumers’ interests, demographics, purchasing patterns and trends.
Skills Required to Become a Data Analytics
- Programming Skills: One should have a sound knowledge of programming languages as the computer languages help analyse and categorise clustered data smoothly.
- Statistical Skills: Since the Data Analytics is all about analyzing the data, information, facts, and figures, one should have statistical abilities to handle the data analytics.
- Mathematics Skills: Data Analytics is about playing with numbers and people who are good with crunching numbers are able to do this job efficiently.
- Machine Learning Skills: As mentioned above, Data Analytics serves the B2C process primarily, one must have the machine learning skills in order to work with the data.
Data Science vs. Big Data vs. Data Analytics
Comparing these three fields is a bit tricky as all of them look the same for many people. Hence, to break it down, here we are sharing some facts and concepts by which these fields can be differentiated.
Data Science is the technique or practice to process large data and utilize such data to make the decision to enhance the business growth while Big Data is the huge volume of data that is captured from the multiple sources and in numerous formats. Data Analytics, in contrast, is somewhat similar to Data Science and is considered the basic level of Data Science. Data Analytics is largely the science to get the insights, trends and metrics from the raw data. Further, we can differentiate the Data Science and Data Analytics by defining the roles of these fields. While Data Science predicts the future actions and occurrences on the basis of the existing patterns and behaviors, Data Analytics captures information available in the existing data.
Here, we found out the differences between these parallel yet diverse fields of Data, Facts and Information. Today, not only the big businesses and giant companies but also the small ventures focus on the data in order to grow their businesses. Gone are the days when a company used to emphasize on its product only, nowadays, the key to the business growth is to analyze the data and derive even a small piece of information that can benefit the business growth. Hence, seeing the immense competition in the market, the need for the Data Science specialists, Big Data professionals and Data Analytics experts have been increasing by leaps and bounds all over the world.