Data Science and Big Data

    Data Analytics


    Human civilization started to curate, interpret, and summarize data since ancient times, to bring out the benefits and insights. In the 20th century, statistician William Sealy Gosset, who was working in a brewery used his statistical expertise to choose the best yielding types of barley. All these types of statistical methods stayed with the mathematicians and statisticians till the mid century. In the last few decades, software tools like MS Excel, SAS, and programming languages like R & Python have been making tremendous development allowing the data scientists of today’s time to apply mathematical algorithms utilizing ready-made libraries and the latest algorithms. Nowadays, based on the data collected machines are not only capturing logical knowledge, but they are also working & developing an intuitive knowledge.

    Data Analytics

    What is Data Science?

    During the 1980s and early 1990s, people were in a rush to apply for investment banking jobs. Then in the late 1990s and early 2000s, it became apparent that the internet will soon revolutionize the world and a lot of tech-savvy grads began concentrating on software and web development. There has been a lot of hype about data storing & processing, this hype led to the opportunity for a new door to a broad field which came into existence as “Data Science”.

    Data science means using various tools & algorithms to extract valuable information from raw data. This raw data is drawn from different channels & platforms like cell phones, search engines, surveys, e-commerce sites, and social media. This large amount of data is structured as well as unstructured, which requires interpretation for valuable purposes. This process is elaborate and time-consuming; hence there is a need for professional Data Scientists.

    What is Big Data?

    A variety of huge amounts of data is being produced at an extremely fast rate in various fields. Therefore, examining big data has become remarkably significant and unavoidable. As a result of this, big data analytics courses is being adopted all across the globe to achieve infinite advantages from the data being generated.

    Just like the big bang explosion, data has been exploding exponentially, which is leading to an accumulation of enormous amounts of data. According to studies, there is a creation of 2.5 quintillions of data every other day. This data is getting generated from various sources, whether it is social media platforms or from banking sectors or governments or various other sources. This data is generated in an unstructured format.

    Big data has three different formats – structured, semi-structured and unstructured.

    Structured data is typically in the order of relational databases which come in the form of tables that have rows & columns.

    Unstructured data is in the form of audio files, video files, images, etc.

    Semi-Structured data is in the form of JSON in XML files.

    The growth of Big data is divided into five dimensions known as the five V’s of the Big data which are volume, velocity, variety, veracity, and value.

    The term volume defines the size of the data which is moving rapidly from Gigabyte to Terabyte and so on. This huge volume of data cannot be analyzed using traditional systems.

    Velocity defines the rate at which the data is produced, collected or processed. Velocity can be calculated per hour per minute or per-second basis.

    Variety means the data that social media platforms have introduced in the form of unstructured data via SMS, images, video files, audio files that can be fit into the structured databases.

    Veracity refers to the identification of truth from all kinds of information.

    Value is simply defined as the potential benefit of the data for the organization.

    Languages & Tools used

    SAS – SAS was the earliest software to be developed for analyzing data. North Carolina University began managing the project which was named statistical analysis system. This happened in 1966 and continued until 1976, until the launch of the SAS institute happened. SAS continued to grow and mature as a product and in early 2000, several new technology areas like social media analytics were introduced.

    PYTHON – Python is a programming language that is used for general purposes. This was created by the Dutch Programmer Guido Van Rossum. 1991 was the year, which saw the birth of the first version of this language. Numpy and Pandas are the two Python libraries that are the backbone for data science courses. This popular programming language is also used as a scientific scripting language to correct the difficulties of numerical data processing and manipulation. Python has begun to become the first choice for data scientists, especially in the area of deep learning. It has been listed as the third most utilized programming language by the social programming community Github.

    R – R language was not developed from a software research lab, but rather from the need of statistics professors who needed the technology for quick statistical computing. In the year 199, the statistical professors Ross Oaxaca & Robert Gentlemen belonging to the University of Auckland started working full-time to produce a language that could meet the much-required technology for statisticians. The first version of the R language was launched in the year 1993. Since that time R has been in support and updated regularly. It is also known to be one of the most extensively utilized programming languages because its open-source contributed rich set libraries. R is currently dominating the data science industry.

    Excel – We all are very well aware of Microsoft Excel. It is a very popular tool utilized in all types of businesses that involve both small & medium organizations, skilled professionals and people, for diversified objectives. A survey was conducted by O’Reilly to find out which is the most common tool used by the data specialists. 70% of the people answered Excel. This showed the extensive usage of this tool for data analysis.

    The big social media players have pioneered the technologies that can handle big data volumes. Hadoop framework consisting of the file system HDFS and programming language MapReduce is an open-source software framework for storing and accessing big data.

    The technology has rapidly grown to make the power of data to be engineers in a friendlier mannerism. The real-time analytics gave birth to several frameworks and currently, Apache Spark is leading the space to process real-time data with a strong set of libraries including machine learning and graph processing. Data is compared to be the new gold or oil. All the different business functions must be able to access and discover the data, understand, and interpret it.

    The Need for Data Science & Big Data

    In the era of globalization, every organization is working hard enough to use the extracted and analyzed data for its decision-making goals. Analysis of data breaks down earlier reviews to form the plan for the future. Valuable extracted data has various tiers of applications which are remarkably significant for the economy, to shape up some of the most valuable outcomes and partnerships. It assists in learning the accessible primary and secondary data more distinctively, which, in turn, influences the operational effectiveness of several teams in a company.

    The fascinating technology of data science helps in producing a competing edge. It consolidates the data accessible with many thought models to improve marketing judgments.

    Profession of a Data Scientist & a Business Analyst

    The profession of a data scientist was the hottest job opportunity in the market in the year 2017 and 2018. The job of a data scientist sounds quite scary and sophisticated, but in reality, the journey of becoming a data scientist is similar to any other profession in the job market.

    Each company has its way of extracting their data, but who does all this work? A data scientist is the one who analyses a huge amount of data into valuable information. Data scientists are attentive to even small details & they love finding solutions to computer-related issues.

    A data scientist takes the data and starts developing, interpreting, and implementing data learning tools to extract valuable data from the raw data. They use advanced statistical methods to perform predictive analysis and receive essential insight from the data. Being a good data scientist doesn’t mean how advanced the tools utilized are, it means the amount of impact that a data scientist can produce with their work.

    According to a study led by the computer world, the financial sector requires 15% implantation of a business analyst, manufacturing and techno-related businesses require 13-14% and health businesses require up to 8% of business analysts. Every sector of the automation industry requires a professional business analyst to analyze their big data. The profession of a business analyst is completely a people-oriented job that provides job security as well as a lucrative salary.


    Extraction and analysis of data has made everyone’s life simpler because of automated decision-making in day-to-day life. The possibilities of utilizing big data & data science are aggregated by developing technology inventions in fields such as artificial intelligence, business analytics, data analytics, data science, robotics, autonomous transportation, 3-D printing, nanotechnology, and quantum computing.

    Big data and data science are not just buzzwords, but in reality, they have an importance in finding insights and making decisions to simplify everybody’s life.

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    Author bio: Mr Ram Tavva is a Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkatta) with over 25 years professional experience, Specialised in Data Science, Artificial Intelligence, and Machine Learning.

    PMP Certified

    ITIL Expert certified

    APMG, PEOPLE CERT and EXIN Accredited Trainer for all modules of ITIL till Expert

    Trained over 3000+ professionals across the globe

    Currently authoring book on ITIL “ITIL MADE EASY”


    Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis and Project management process definition and end to end implementation of Project management best practices.

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