What is data analytics, and how Excel is used in data analytics

What is data analytics, and how Excel is used in data analytics

What is data analysis

  • Comprehensive method of inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
  • A multifaceted process involving various techniques and methodologies to interpret data from various sources in different formats, both structured and unstructured.
  • Tool that empowers organizations to make informed decisions, predict trends, and improve operational efficiency. It’s the backbone of strategic planning in businesses, governments, and other organizations.

The data analysis process


The data analysis process

  • Define the question or goal behind the analysis: what are you trying to discover?
  • Collect the right data to help answer this question.
  • Perform data cleaning/data wrangling to improve data quality and prepare it for analysis and interpretation–getting data into the right format, getting rid of unnecessary data, correcting spelling mistakes, etc.

  • Manipulate the data. This may include plotting the data out, creating pivot tables, and so on.
  • Analyze and interpret the data using statistical tools (i.e. finding correlations, trends, outliers, etc.).
  • Present this data in meaningful ways: graphs, visualizations, charts, tables, etc.


Types of Data Analysis

  • Descriptive analysis

    • Designed to answer the question “What happened?” The goal of descriptive analytics is to summarize data in a meaningful and descriptive manner, not to make any predictions

  • Exploratory analysis

    • Dives a bit deeper than descriptive analytics, skimming for detectable patterns and trends in data. Another way to think of this is the initial investigation phase.

  • Diagnostic analysis

    • Takes the insights found from both descriptive and exploratory analytics and investigates further to find the causes.

  • Predictive analysis

    • Uses data, statistics, and machine learning algorithms and techniques to figure out the likelihood of future outcomes based on data. Examples include sales forecasting and risk assessment.

  • Prescriptive analysis

    • Takes insights found from all types of data analysis (descriptive, exploratory, diagnostic, predictive) to determine the best course of action.


Why should we learn data analysis?

  • Job growth for data professionals
  • Data analytics is in demand
  • Higher than average salaries
  • Competitive advantage
  • Universal need


What is Excel?

  • Spreadsheet program available in the Microsoft Office Package.
  • Used to create Worksheets (spreadsheets) to store and organize data in a table format.
  • One of the most used software application in the world.
  • Easy to enter the data, read and manipulate the data.
  • Excel stores the data in a table format in Rows and Columns.


What is Excel used for?

  • Enter data in Strings, Dates or Numerical type of Data in the Excel Cells and Save the Files for future reference
  • Use variety of formulas available in Excel to perform calculations
  • Develop Tools and Dashboards
  • Interact with Other Applications
  • Represent data in Charts
  • Drill down and analyze the data using Pivot Tables


Where is Excel used?

  • Almost ALL industries.
  • Some examples could be:

    • Financial sector
    • Analytical professionals
    • Retail Associates
    • Reporting Analysts
    • Healthcare Teams
    • Market Research Analysts
    • VBA Developers