
Introduces key business analytics and its procedures across descriptive, diagnostic, predictive, prescriptive, and cognitive analytics. Explains how data-driven insights guide informed decisions, performance optimization, and competitive advantage.
Explore data types and formats, including structured, unstructured, and semi-structured data, and learn how formats like JSON, XML, HTML, text, and images support storage, search, and analysis.
This book is for anyone in business who wants to understand what analytics can offer, including executives, managers, decision makers, and analysts, with a general, nontechnical overview for applying analytics.
Create a measurable, strategy-aligned hypothesis using independent and dependent variables, a 6 to 7 month time frame, and a 25% increase expectation; illustrate with loyalty rewards and a control group.
Design the experiment to test real time analytics in supply chain, comparing pre- and post-implementation with centers in control and treatment groups on inventory holding cost and order fulfillment accuracy.
Identify tips and traps in business experiments by testing one variable at a time, keeping other factors constant, while using high-quality prototypes and representative samples for reliable results.
Explore correlation analysis, a numeric method that measures the strength and direction of relationships between two quantified variables, including Pearson and Spearman forms for business forecasting.
Explore scenario analysis, a horizon-based projection method, to evaluate futures, assess implementation viability, and examine how economic conditions, market trends, regulatory changes, and technological advancements shape outcomes.
Explore how time series data enables forecasting future trends by defining objectives, preparing historical data, training and evaluating models like ARIMA or exponential smoothing, and monitoring forecasts for data-driven decisions.
Explore how forecasting answers key business questions—future economic performance, inventory needs, demand, cash flow, and job prospects—through defining objectives, gathering data, choosing time series or regression models, and forecasting.
Uncover insights from large business data by data mining, an analytic process that extracts patterns, dependencies, and anomalies through AI, statistics, databases, and machine learning.
Description
Take the next step in your career! Whether you’re an up-and-coming professional, an experienced executive, aspiring manager, budding Professional. This course is an opportunity to sharpen your Sentiment analysis. Image Analytics. Video analytics. Voice analytics. Monte Carlo simulations., increase your efficiency for professional growth and make a positive and lasting impact in the business or organization.
With this course as your guide, you learn how to:
All the basic functions and skills required key business analytics.
Transform the Key Business Analytics including the raw material – data. Business experiments/experimental design/AB testing. Visual analytics. Correlation analysis. Scenario analysis. Forecasting or Time. Data mining. Regression analysis. Text analytics. Text analytics.
Get access to recommended templates and formats for the detail’s information related to key business analytics.
Learn to Qualitative surveys. Focus groups (. Interviews and ethnography. Test capture. Image capture. Sensor date. Machine data capture. Financial analytics. Customer profitability analytics. Product Profitability. are presented as with useful forms and frameworks
Invest in yourself today and reap the benefits for years to come
The Frameworks of the Course
Engaging video lectures, case studies, assessment, downloadable resources and interactive exercises. This course is created to learn the Introduction to the Key Business Analytics including the raw material – data. Business experiments/experimental design/AB testing. Visual analytics. Correlation analysis. Scenario analysis. Forecasting or Time. Data mining. Regression analysis. Text analytics. Text analytics. Sentiment analysis. Image Analytics. Video analytics. Voice analytics. Monte Carlo simulations. Linear programming. Cohort analysis. Factor analysis. Neural network analysis. Meta analytics literature analysis. Analytics inputs tools or data collection methods
The details Test capture. Image capture. Sensor date. Machine data capture. Financial analytics. Customer profitability analytics. Product Profitability. Cash flow analysis. Value driver analytics. Shareholder value analytics. Market analytics. Market size analytics. Demand forecasting. Market trends analytics. Non- customer analytics.
The course includes multiple Case studies, resources like formats-templates-worksheets-reading materials, quizzes, self-assessment, film study and assignments to nurture and upgrade your of Competitor analytics. Pricing analytics. Pricing analytics. Marketing channel. Brand analytics. Customer analytics in details.
In the first part of the course, you’ll learn the details of Introduction to the Key Business Analytics including the raw material – data. Business experiments/experimental design/AB testing. Visual analytics. Correlation analysis. Scenario analysis. Forecasting or Time. Data mining. Regression analysis. Text analytics. Text analytics. Sentiment analysis. Image Analytics. Video analytics. Voice analytics. Monte Carlo simulations. Linear programming.
In the middle part of the course, you’ll learn how to develop a knowledge of The , Test capture. Image capture. Sensor date. Machine data capture. Financial analytics. Customer profitability analytics. Product Profitability. Cash flow analysis. Value driver analytics. Shareholder value analytics. Market analytics. Market size analytics. Demand forecasting. Market trends analytics. Non- customer analytics.
In the final part of the course, you’ll develop the Competitor analytics. Pricing analytics. Pricing analytics. Marketing channel. Brand analytics. Customer analytics.
Course Content:
Part 1
Introduction and Study Plan
· Introduction and know your Instructor
· Study Plan and Structure of the Course
1. Introduction
1.1 Details of Introduction
1.2. The raw materials -Data
1.3. Data types and format
1.4. How to use this
1.5. Who is this for?
2. Business experiments or experimental design or AB testing
2.1. What is it?
2.2. What business questions is it helping me to answer
2.3. Create a hypothesis
2.4. Design the experiment
2.5. Tips and traps
3. Visual analytics
4. Correlation analysis
5. Scenario analysis
6. Forecasting or Time
7. Data mining
8. Regression analysis
9. Text analytics
10. Sentiment analysis
11. .Image Analytics
12. Video analytics
13. .Voice analytics
14. Monte Carlo simulations
15. . Linear programming
16. Cohort analysis
17. Factor analysis
18. Neural network analysis
19. Meta analytics literature analysis
20. Analytics inputs tools or data collection methods
21. Qualitative surveys
Part 2
22. Focus groups
23. Interviews
24. Ethnography
25. Test capture
26. . Image capture
27. Sensor date
28. Machine data capture
29. Financial analytics
30. Customer profitability analytics
31. Product Profitability
32. Cash flow analysis
33. Value driver analytics
34. Shareholder value analytics
35. Market analytics
36. Market size analytics
37. Demand forecasting
38. Market trends analytics
39. Non- customer analytics
40. Competitor analytics
41. Pricing analytics
42. Marketing channel
43. Brand analytics
44. Customer analytics
45. Customer lifetime