Data Science Impact from a Global Standpoint: A Study

Table of Contents

Data science is an evolutionary extension of statistics capable of dealing with the massive amounts of data produced today. It adds methods from computer science to the repertoire of statistics. In a research note from Laney and Kart, Emerging Role of the Data Scientist and the Art of Data Science, the authors sifted through hundreds of job descriptions for data scientist, statistician, and BI (Business Intelligence) analysts to detect the differences between those titles.

The main things that set a data scientist courses in India apart from a statistician are the ability to work with big data and experience in machine learning, computing, and algorithm building. Their tools tend to differ too, with data scientist job descriptions more frequently mentioning the ability to use Hadoop, Pig, Spark, R, Python, and Java, among others. For instance, almost every popular NoSQL database has a Python-specific API. Because of these features and the ability to prototype quickly with Python while keeping acceptable performance, its influence is steadily growing in the data science world. As the amount of data continues to grow and the need to leverage it becomes more important, every data scientist will come across big data projects throughout their career.

Benefits and Uses of Data Science and Big Data

Data science and big data are used almost everywhere in both commercial and noncommercial settings. The number of use cases is vast, and the examples we’ll provide throughout this book only scratch the surface of the possibilities. Commercial companies in almost every industry use data science and big data to gain insights into their customers, processes, staff, completion, and products. Many companies hire graduates from data scientist courses in India to offer customers a better user experience, as well as to cross-sell, up-sell, and personalize their offerings. A good example of this is Google AdSense, which collects data from internet users so relevant commercial messages can be matched to the person browsing the internet. Human resource professionals use people analytics and text mining to screen candidates, monitor the mood of employees, and study informal networks among coworkers.

Financial institutions use data science to predict stock markets, determine the risk of lending money, and learn how to attract new clients for their services.  Governmental organizations are also aware of data’s value. Many governmental organizations not only rely on internal data scientists to discover valuable information but also share their data with the public. You can use this data to gain insights or build data-driven applications.  A data scientist from data scientist courses in India in a governmental organization gets to work on diverse projects such as detecting fraud and other criminal activity or optimizing project funding.  Nongovernmental organizations (NGOs) are also no strangers to using data. They use it to raise money and defend their causes. The World Wildlife Fund (WWF), for instance, employs data scientists to increase the effectiveness of their fundraising efforts. Many data scientists devote part of their time to helping NGOs because NGOs often lack the resources to collect data and employ data scientists. DataKind is one such data scientist group that devotes its time to the benefit of mankind. Universities offer data scientist courses in India to accelerate their research but also to enhance the study experience of their students.

The Data Science Process

The data science process typically consists of six steps,

  • Setting the Research Goal

Data science is mostly applied in the context of an organization. When the business asks humans to perform a data science project, they will first prepare a project charter. This charter contains information such as what they are going to research, how the company benefits from that, what data and resources they need, a timetable, and deliverables.

  • Retrieving Data

The second step is to collect data. Practicing data scientists stated in the project charter which data they need and where they can find it. In this step, they will ensure that they can use the data in their program, which means checking the existence of, quality, and access to the data. Data can also be delivered by third-party companies and takes many forms ranging from Excel spreadsheets to different types of databases.

  • Data Preparation

Data collection is an error-prone process; in this phase, data scientists enhance the quality of the data and prepare it for use in subsequent steps. This phase consists of three subphases: data cleansing removes false values from a data source and inconsistencies across data sources, data integration enriches data sources by combining information from multiple data sources, and data transformation ensures that the data is in a suitable format for use in the models.

  • Data Exploration

Data exploration is concerned with building a deeper understanding of your data. Users can try to understand how variables interact with each other, the distribution of the data, and whether there are outliers. To achieve this they mainly use descriptive statistics, visual techniques, and simple modeling. This step often goes by the abbreviation EDA, for Exploratory Data Analysis.

  • Data Modeling or Model Building

In this phase, data scientists use models, domain knowledge, and insights about the data you found in the previous steps to answer the research question. They can select a technique from the fields of statistics, machine learning, operations research, and so on. Building a model is an iterative process that involves selecting the variables for the model, executing the model, and model diagnostics. 

  • Presentation and Automation

Finally, data scientists present the results to their businesses. These results can take many forms, ranging from presentations to research reports. Sometimes they will need to automate the execution of the process because the business will want to use the insights they have gained in another project or enable an operational process to use the outcome from their models.

Conclusion

Big data is a blanket term for any collection of data sets so large or complex that it becomes difficult to process them using traditional data management techniques such as, for example, the RDBMS (relational database management systems). The widely adopted RDBMS has long been regarded as a one-size-fits-all solution, but the demands of handling big data have shown otherwise. Data scientist courses in India involve using methods to analyze massive amounts of data and extract the knowledge it contains.

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