Introduction

- 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