Requirements

• You should have an interest in Key Business Analytics and data driven management
• Basic understanding of business and different requirements to run an organization
• Basic communication skill and proficiency in office package

Description

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

Who this course is for:

• Existing executive board directors
• managing directors who is looking to get more engagement and innovation from their teams and organizations
• Any managers or aspiring managers wants to understand business with different data analysis
• Any professional wants to be a Business Analyst or Data Analyst