Data Analytics Tutorial

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- Data analytics is the science of analyzing raw data in order to make conclusions about that information.

- Data Analytics has a key role in improving your business as it is used to gather hidden insights, generate reports, perform market analysis, and improve business requirements.

- Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things.

Data Analytics Role

  • Gather Hidden Insights
    • Hidden insights from data are gathered and then analyzed with respect to business requirements.
  • Generate Reports
    • Reports are generated from the data and are passed on to the respective teams and individuals to deal with further actions for a high rise in business.
  • Perform Market Analysis
    • Market Analysis can be performed to understand the strengths and weaknesses of competitors.
  • Improve Business Requirement
    • Analysis of Data allows improving Business to customer requirements and experience.

Data Analytics Type

  • Descriptive Analytics
    • process of describing historical trends in data
    • often involves measuring traditional indicators such as return on investment
    • does not make predictions or directly inform decisions
    • focuses on summarizing data in a meaningful and descriptive way
    • find what happened?
  • Diagnostic Analytics
    • identify anomalies in the data (unexpected changes in a metric)
    • collected anomalies in the data
    • find relationships and trends that explain these anomalies
    • find why things happened?
  • Predictive Analytics
    • historical data to identify trends and determine
    • provide valuable insight into what may happen in the future
    • find what will happen in the future?
  • Prescriptive Analytics
    • analyzing past decisions and events, the likelihood of different outcomes can be estimated
    • insights from predictive analytics, data-driven decisions can be made
    • businesses to make informed decisions in the face of uncertainty
    • find what should be done?
  • Advanced Analytics
    • advanced tools to extract data, make predictions and discover trends
    • include statistics and machine learning (neural networks, natural language processing, sentiment analysis)
    • information provides new insight from data
    • addresses to 'what if?' questions
  • Big Data Analytics
    • enables businesses to draw meaningful conclusions from complex and varied data sources
    • machine learning techniques, massive data sets, and cheap computing power
    • made possible by advances in parallel processing

Data Analytics Applications

  • Figures and Plots
    • matplotlib
    • Histogram
    • Bar Plot
    • Line Plot
    • Scatter Plot
    • Box Plot
    • pandas
    • ggplot
    • seaborn
  • Excel workbook
  • CSV Files

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