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Data analytics is the process of analyzing, interpreting, and transforming data into meaningful insights, often using statistical and computational methods. This involves collecting, processing, cleaning, and modeling large datasets to identify patterns, trends, and correlations that can be used to inform business decisions or answer research questions.

Data analytics is important because it helps organizations gain valuable insights and make data-driven decisions that can increase efficiency, reduce costs, and drive growth. By analyzing data, businesses can better understand customer behavior, identify market trends, optimize their operations, and improve their products and services. Data analytics can also help organizations detect and prevent fraud, reduce risk, and improve overall performance. In short, data analytics provides organizations with a competitive edge in today's data-driven world.

Data analytics has become a popular career choice for several reasons:

  • High Demand: With the increasing amount of data being generated by businesses, there is a growing demand for professionals who can analyze and make sense of this data. This demand is expected to continue to grow as companies realize the importance of data-driven decision-making.
  • Career Growth: Data analytics is a dynamic field that provides many opportunities for career growth. As you gain experience and skills, you can progress to more senior roles and take on more responsibility.
  • Competitive Salaries: Data analytics is a well-paying profession, with competitive salaries and benefits packages. According to Glassdoor, the average salary for a data analyst in the US is around $70,000 per year, while senior data analysts can earn upwards of $100,000
  • Versatile Skills: Data analytics requires a combination of technical and analytical skills, making it a versatile career choice. These skills are transferable across industries, allowing you to work in a variety of settings.
  • Impactful Work: Data analytics can have a significant impact on businesses, helping them make more informed decisions, identify new opportunities, and optimize their operations. As a data analyst, you can play a crucial role in driving the success of a business

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    CURRICULUM

    • Introduction to Excel
    • What is Analytics
    • Why Analytics?
    • Data Analytics vs Business Analytics
    • Types of Analytics: Descriptive, Diagnostic, Predictive and Prescriptive
    • Data and Data Sources for Analytics Small Data, Big Data, Traditional Data, and Non-Traditional data Sources
    • Analytics in Business
    • Introduction to problem Solving using data
    • Analytics tools: What tools to use for which type of Problems?
    • Types of Error Data Analysis


    Data analytics can be used to answer a wide variety of questions related to various types of data. The types of questions you can ask will depend on the type of data you are analysing and what insights you hope to gain from it. Here are a few examples of the types of questions you might ask for different types of data:

    Customer data:

    • What are the demographics of our customers?
    • What products or services are our customers most interested in?
    • How frequently do our customers make purchases?
    • What factors influence customer satisfaction or loyalty?

    Financial data:

    • What are our company's top revenue-generating products or services?
    • How much revenue is generated by each business unit or department?
    • What are our company's biggest expenses and how can we reduce them?
    • What financial trends or patterns can we identify?

    Web data:

    • How many visitors does our website receive each day?
    • Which pages on our website are most popular?
    • Where are our website visitors located geographically?
    • What keywords or search terms are bringing people to our website?

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