Data Analytics for UG students - What is data analytics and its importance

Data Analytics for UG students - What is data analytics and its importance

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  Skill Enhancement Course(SEC) – I Data Analytics – I Unit- I Shambhu Rout Lecturer in Economics Rayagada Autonomous College, Rayagada
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  Concept of DataAnalytics What is Data Analytics? • Data Analytics is a systematic approach that transforms raw data into valuable insights. This process encompasses a suite of technologies and tools that facilitate data collection, cleaning, transformation, and modelling, ultimately yielding actionable information. This information serves as a robust support system for decision-making. • Data analysis plays a pivotal role in business growth and performance optimization. It aids in enhancing decision-making processes, bolstering risk management strategies, and enriching customer experiences. By presenting statistical summaries, data analytics provides a concise overview of quantitative data.
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  Application of DataAnalytics: Data analytics is used across various industries to optimize performance, improve decision-making, and gain a competitive edge. For example: • Healthcare: Analyzing patient data to improve treatment outcomes and manage resources efficiently. • Finance: Detecting fraudulent activities and managing risks • Retail: Understanding customer preferences and optimizing inventory management • Agriculture: Enhancing crop yields and managing supply chains
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  Process of DataAnalytics Data analysts, data scientists, and data engineers together create data pipelines which helps to set up the model and do further analysis. Data Analytics can be done in the following steps which are mentioned below: • Data Collection : It is the first step where raw data needs to be collected for analysis purposes. It consists of two steps in which data collection can be done. If the data are from different source systems then using data integration routines the data analysts have to combine the different data whereas sometimes the data are the subset of the data set. In this case, the data analyst would perform some steps to extract the useful subset and transfer it to the other compartment in the system.
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  • Data Cleansing: After collecting the data, the next step is to clean the quality of the data as the collected data consists of a lot of quality problems such as errors, duplicate entries and white spaces which need to be corrected before moving to the next step. By running data profiling and data cleansing tasks, these errors can be corrected. These data are organised according to the needs of the analytical model by the analysts. • Data Analysis and Data Interpretation: Analytical models are created using software and other tools which interpret the data and understand it. The tools include Python, Excel, R, Scala and SQL. Lastly this model is tested again and again until the model works as it needs to be then in production mode the data set is run against the model.
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  • Data Visualisation:Data visualisation is the process of creating visual representation of data using the plots, charts and graphs which helps to analyse the patterns, trends and get the valuable insights of the data. By comparing the datasets and analysing it data analysts find the useful data from the raw data.
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  Types of DataAnalytics There are different types of data analysis in which raw data is converted into valuable insights. Some of the types of data analysis are mentioned below: • Descriptive Data Analytics : Descriptive data Analytics is a type of data analysis which summarises the data set and it is used to compare the past results, differentiate between the weakness and strength, and identify the anomalies. Descriptive data analysis is used by the companies to identify the problems in the data set as it helps in identifying the patterns. • Real-time Data Analytics: Real time data Analytics doesn’t use data from past events. It is a type of data analysis which involves using the data when the data is immediately entered in the database. This type of analysis is used by the companies to identify the trends and track the competitors’ operations.
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  • Diagnostic DataAnalytics: Diagnostic Data Analytics uses past data sets to analyse the cause of an anomaly. Some of the techniques used in diagnostic analysis are correlation analysis, regression analysis and analysis of variance. The results which are provided by diagnostic analysis help the companies to give accurate solutions to the problems. • Predictive Data Analytics: This type of Analytics is done in the current data to predict future outcomes. To build the predictive models it uses machine learning algorithms, statistical model techniques to identify the trends and patterns. Predictive data analysis is also used in sales forecasting, to estimate the risk and to predict customer behaviour.
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  • Prescriptive DataAnalytics: Prescriptive data Analytics is an analysis of selecting best solutions to problems. This type of data analysis is used in loan approval, pricing models, machine repair scheduling, analysing the decisions and so on. To automate decision making companies use prescriptive data analysis.
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  Thank You