Data Analytics

Data analytics can be defined as the science of analyzing raw data in order to make conclusions about that information. This information can then be used to optimize processes to increase the overall efficiency of a business or system. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analysis is important in business to understand problems faced by an organisation and to explore data in meaningful ways.

Data in itself is merely facts and figures. Data analysis organizes, interprets, structures and presents the data into useful information that provides context for the data. Accountants and Finance Professionals are more and more expected to deliver information from the accounting statements that are useful for strategic and other decisions of the management in different functions. The programme on Data Analytics will give an exposure to the mechanisms for using Data Analytics by Accounting and Finance Professionals.

Data analytics Outline:

  1. Managerial decision making and the role of data analytics in it.
  2. Managerial decision making and the role of data analytics in it.
    • About Data, Data Analytics, ML.
    • Why we need to learn Applications Tools Hardware and software specification
    • How can we approach
    • The raw materials of data analytics (types, quality, and data preparation)
  3. Exploratory data analysis
  4. Using summary tables and visual displays to profile sample data
  5. Introduction to Python- Basics, Loops, Functions
  6. Python Packages
    • Python Pandas
    • Python Numpy
    • Data Visualization with Matplotlib, seaborn
  7. EDA and Machine Learning
    • Exploratory Data Analysis (in detailS)
    • Machine Learning
    • Model Building (Linear Regression AND amp; Decision Tree)
  8. Graph Analytics with Tableau
    • Data Visualization
    • Model Deployment (Product)

Who should attend?

  • Accounting and finance professionals, especially those interested in learning and applying data analysis techniques to help their organizations’ make informed, data-driven business decisions.

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