Increasing Role of Data Science in the BPS Domain

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Data Science Lecture Report
The data science market analyzes vast volumes of information using contemporary techniques and tools to identify unseen patterns, derive meaningful data, and guide organizational decisions. This data analysis usually involves using sophisticated machine learning algorithms to generate predictive models and actionable insights. The sub-space covered in the lecture comprises the software hub upon which all types of data science are founded, including incorporating and exploring data from different sources, creating models, and coding. The global data science market was valued at $95.3 billion in 2021 and is expected to expand at a compound annual growth of 27.7%, reaching a $322.9 billion valuation in 2026 (ReportLinker). The high growth rate is driven by the rapid increase in big data, increasing adoption of cloud-based solutions, growing need among companies to extract actionable insights from voluminous data, and increasing application of data science platforms in numerous industries. The data science-related skills and technologies commonly used in this sector include statistics, programming and coding, multivariable calculus and linear algebra, predictive modeling, machine learning, deep learning, data wrangling and preparation, model deployment and production, and data visualization.
Data science involves regularly employing statistical concepts, and analysts must be familiar with various elements of statistics to collect, organize, analyze, interpret, and present data. It also requires the application of multivariable calculus and linear algebra to understand and optimize fitting conditions, such as when aligning a model to a data set, training an artificial neural network on big data, or simplifying intricate analysis problems, including high-dimensional data. Data science also involves programming and coding, and therefore a keen understanding of programming languages is necessary. Moreover, the sub-space involves predictive analytics to identify patterns, make predictions, and model various situations and outcomes. Another helpful technique is machine learning and deep learning: data science increasingly applies AI technologies to execute machine learning applications. Therefore, data scientists must know how to train machine learning algorithms to analyze data sets (Walch). Data wrangling and preparation is another critical skill in data analysis, particularly concerning profiling, cleansing, and modeling data for analytics applications.
Model deployment and production are also essential in building and deploying models, as is data visualization in effectively visualizing voluminous data sets with unstructured data types. Data and computing-related methods used in typical workflows in this sector include four main phases: preparation, analysis, reflection, and dissemination. The first phase is data preparation and involves acquiring data for analysis and reformatting and cleaning the same for analysis. Before any data analysis can occur, the data scientist must first acquire the data: this is usually from various sources such as data downloaded or streamed on-demand from online repositories, data generated by computer software, or data manually generated into a spreadsheet. This process of acquiring data includes keeping track of each data source and its viability. The next step after acquiring data is reformatting and cleaning gathered information to ensure that it is in a convenient format for analysis. This process is tedious as it involves removing semantic errors, inconsistent formatting, and missing errors, among other data munging and organization tasks.
The analysis phase is the core activity of data science. It entails an iterative process of editing scripts, implementing data analysis platforms to produce output files, evaluating the output files to derive in-depth insights, identifying mistakes in the analysis process, debugging, and re-editing. This process generates as much insight as possible from the collected data. Therefore such issues as absolute running times, incremental running times, and crashes from errors are common (Stoudt et al.). Data management issues tied to the production of multiple output files during repeatedly editing and implementing scripts is another challenge encountered by data scientists. Data scientists usually alternate between data analysis and reflection phases: the reflection phase involves deliberating on the analysis outputs. Sometimes additional data analysis is necessary, especially if the output files are inadequate. The reflection phase involves t...