Data Analytics for Managerial Decision Making

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.

1. Managerial decision making and the role of data analytics in it.

2. Introduction to Data Analytics

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)

Determine how the digital transformation and disruption landscape create a competitive advantage across the enterprise.
Identify the organizational ecosystem needed for becoming a data-driven organization.
Analyze the life cycle of data and its implications for data-driven decision making.
Compare common problems and risks associated with becoming a data-driven business.
Differentiate between data analysis tools.
Identify requirements for data-driven projects.

18 to 20 Hours

Who should attend the Data Analytics for Managerial Decision Making Course?

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