Business Data Analytics – Certificate Program

Business Data Analytics – Certificate Program


Course Features

  • Certificate: Yes
  • Course Capacity: 10
  • Skill Level: Advanced
  • CPE Hours: 16

Descriptions

Course Overview

“Business analytics trains you to confidently translate insights into impact – an increasingly in-demand skill both in Australia and globally".

The objectives of this course are to introduce Business Data Analytics and give participants a complete overview of the modern trends and practices as well as taking them along with a practical case study with a real life business scenario with Power Bi and Python in particular so that they become aware of the theoretical as well as practical aspects of the domain.

Participants will be given a complete understanding of the various mathematical, statistical, technical and visualization tools which can be used to analyze data with Power BI and Python, draw insights from data and how to best communicate results to the stakeholders to create an influence in the business decision making process. This will help contributing the organizations’ current decision-making process which might be based on a personal intuition to one that is more robust and based on data-driven informed decision making practices.

Participants will also gain a thorough understanding of current practices and future growth trends of the domain, so they could tap the business opportunities in one of the fastest growing fields in modern business world along with designed case studies. 

Recorded Videos of the Course will be available for the participants after the Live Interactive session for limited time period.

Key Takeaways for Participants:

  • Understand the best practices of carrying out a business data analytics exercise with Power BI
  • Discover the latest tools available to perform the data analytics with Power Bi & Python
  • Learn about major systems and practices that form a complete data analytics life cycle and domain
  • Improve your understanding of various tasks and practices involved in carrying out any analytics tasks
  • Explore practical aspects of a real-life case study to learn how to draw insights from data and how to effectively let the stakeholders know and appreciate your findings
  • Learn the latest trends and prospects of this filed and resources to keep the journey going with the industry specific case studies.

Harness the power of data-driven decisions to drive business value and increase your Employability within the Industry. 

Who should attend? (Participants):

This important course is designed for middle and senior professionals who are part of the business decision making process of any organization and who are either supporting the higher corporate management in their decision making process or are themselves are the decision makers and are interested to explore the data in a meaningful way for getting insights or/and want to improve the current process of decision making and, including:

  • Finance Managers and Controllers
  • Financial Accountants/Analysts
  • Management Accountants/ FP&A managers
  • Internal Auditors
  • Budget, Corporate, Business and Financial Analysts
  • Project Managers and Risk Analysts
  • Business Analysts / Data analysts
  • Financial / Commercial Analysts
  • Investment and Management Accountants
  • Heads of Business Units and Business Planners
  • Financial Advisors and Corporate Analysts

Program Outline:

Module 1 (2-Hours Session)

  1. Course Introduction.
  2. What is Business Data Analytics?
  3.  Emerging trends and technologies in Data Analytics.
  4. Various Types of Analytics
    a. Customer Analytics
    b. Industry-focused Analytics
    c. Financial Analytics
    d. Performance Analytics
    e. Risk Analytics
  5. How Business Analytics differ from Business Analysis.

Module 2 (2-Hours Session)

  1. Business Data Analytics Life Cycle
  2. Business Data Analytics Domain and Tasks
    a. Formulating Analytics question
    b. Sourcing data
    c. Analyzing data
    d. Interpret and communicating results
    e. Influence business decision making
    f. Guide Company level Strategy

Module 3 (2-Hours Session)

  1. Formulating Research Question and Getting data
  2. Identifying business problem / Opportunity
  3. Identifying stakeholders and their business needs
  4. Cleaning the sourced data and making the data ready for analysis.
  5. Overview of the ETL (Extract, Transform & Load ) data using Excel and Power Query
  6. Introduction of various descriptive and inferential statistical techniques Formulating Analytics question
  7. Commonly used Statistical and Mathematical Functions used for data analytics
    a. Types of data like Categorical, numerical their further sub-categories and relevant data analysis techniques
    b. Validating data sets and normalizing data for analysis
    c. Relationship between data tables and directionality of data filters
    d. Rank, Quartile, Decile, Percentile
    e. Standard Deviation, Correlation, Mean, Median, Sum, Count, Sumifs, Countifs.
    f. Outlier detection and data profiling
    g. Analyzing variance and distribution of data

Module 4 (2-Hours Session)

  1. Performing Analysis of Data and technical knowledge
  2. Converting business question into mathematical/ statistical equation
  3. Analytical Tools Available
    a. Power BI
    b. Tableau
    c. Python
  4. Analyzing Data with Excel & Power BI Visualization tools
  5. Initial exploration of data using visual tools to see trends and getting initial insights
  6. Various techniques for Supervised and unsupervised data analysis methods
  7. Getting into analysis using Power pivot and Data Models created in Excel
    a. Pivot Table Creation and power pivot data model maintenance
    b. Show and Summarize Values
    c. Calculated Field and Calculated columns
    d. Linking Slicers
    e. Interactive Charts with Slicer Controls
    f. Analyzing relationships between variables using correlation coefficients and other analytical and statistical techniques

Module 5 (2-Hours Session)

  1. Data Analysis Using Statistics/ Descriptive and inferential statistical techniques
  2. Descriptive/Inferential Statistics
    a. ANOVA
    b. Causality Analysis
    c. Moving Averages
    d. Measuring Covariance and Correlation
    e. Distribution/pareto
    f. Regression
    g. Introduction to Probability and its distributions
    h. Regression analysis and determining line of best fit

Module 6 (2-Hours Session)

  1. Interpreting and Reporting the results of the Analysis
  2. Deriving insights from the data and choosing the right visual for an effective Storytelling.
  3. Various visual and technical tools used to communicate analytical findings
  4. Power Bi Dashboards and various reporting techniques
  5. Presenting analysis results by way of Power Bi Dashboards
    a. Selecting Correct Chart to present results
    b. Interactive Charts with Slicer Controls
    c. Combo box, Check Box, Scroll Bar and Radio Button/slicers
    d. Interactive Dashboard with Form Controls

Module 7 (2-Hours Session) – Case Study

  1. Practical Case study with real life business data
    a. Analyzing data.
    b. Performing statistical and mathematical analysis techniques
    c. Converting analysis into visualizations
    d. Interpreting and communicating results through dashboard
    e. Communicating the results to management for recommended actions
    f. Quantifying the financial impact of the proposed recommendations

Module 8 (2-Hours Session) – Case Study

  1. Continuing with the case study started in module 7
  2. Emerging trends/new technologies in data analytics;
    a. Data Analytics with Python and introduction to various statistical and data analysis libraries
    b. Various Python Libraries for EDA (Exploratory Data Analysis & Visualizations (Seaborn, Matplotlib & Plotly)
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