Certification In Key Business Analytics And Data Analytics

Key Business Analytics 40 + concepts like AB testing, Visual, Correlation, Scenario, Forecasting, Data mining more

What you'll learn

You will 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.

You will be able to learn Sentiment analysis. Image Analytics. Video analytics. Voice analytics.

Monte Carlo simulations. Linear programming. Cohort analysis. Factor analysis. Neural network analysis. Meta analytics literature analysis.

Learn about the details related to Qualitative surveys. Focus groups (. Interviews and ethnography.

Learn 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.

Discover how to get the knowledge of Competitor analytics. Pricing analytics. Pricing analytics. Marketing channel. Brand analytics. Customer analytics.

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

DescriptionTake 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 frameworksInvest in yourself today and reap the benefits for years to comeThe Frameworks of the CourseEngaging 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 methodsThe 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 1Introduction and Study Plan· Introduction and know your Instructor· Study Plan and Structure of the Course1. Introduction1.1 Details of Introduction1.2. The raw materials -Data1.3. Data types and format1.4. How to use this 1.5. Who is this for?2. Business experiments or experimental design or AB testing2.1. What is it?2.2. What business questions is it helping me to answer2.3. Create a hypothesis2.4. Design the experiment2.5. Tips and traps3. Visual analytics4. Correlation analysis5. Scenario analysis6. Forecasting or Time7. Data mining8. Regression analysis9. Text analytics10. Sentiment analysis11. .Image Analytics12. Video analytics13. .Voice analytics14. Monte Carlo simulations15. . Linear programming16. Cohort analysis17. Factor analysis18. Neural network analysis19. Meta analytics literature analysis20. Analytics inputs tools or data collection methods21. Qualitative surveysPart 222. Focus groups23. Interviews24. Ethnography25. Test capture26. . Image capture27. Sensor date28. Machine data capture29. Financial analytics30. Customer profitability analytics31. Product Profitability32. Cash flow analysis33. Value driver analytics34. Shareholder value analytics35. Market analytics36. Market size analytics37. Demand forecasting38. Market trends analytics39. Non- customer analytics40. Competitor analytics41. Pricing analytics42. Marketing channel43. Brand analytics44. Customer analytics45. Customer lifetime

Overview

Section 1: Introduction to Key Business Analytics

Lecture 1 Introduction and Study Plan

Lecture 2 1.1. Details of Introduction

Lecture 3 1.2. The raw materials -Data

Lecture 4 1.3. Data types and format

Lecture 5 1.4. How to use this

Lecture 6 1.5. Who is this for?

Section 2: 2. Business experiments or experimental design or AB testing

Lecture 7 2.1. What is it?

Lecture 8 2.2. What business questions is it helping me to answer

Lecture 9 2.3. Create a hypothesis

Lecture 10 2.4. Design the experiment

Lecture 11 2.5. Tips and traps

Section 3: 3. Visual analytics

Lecture 12 3.1. What is it

Lecture 13 3.2. What business questions is it helping me to answer

Section 4: 4. Correlation analysis

Lecture 14 4.1 Correlation analysis

Lecture 15 4.2. What business questions is it helping me to answer

Section 5: 5. Scenario analysis

Lecture 16 5.1 Scenario analysis

Lecture 17 5.2. What business questions is it helping me to answer

Section 6: 6. Forecasting or Time

Lecture 18 6.1 Forecasting or Time

Lecture 19 6.2. What business questions is it helping me to answer

Section 7: 7. Data mining

Lecture 20 7.1 Data mining

Lecture 21 7.2. What business questions is it helping me to answer

Section 8: 8. Regression analysis

Lecture 22 8.1 Regression analysis

Lecture 23 8.2. What business questions is it helping me to answer

Section 9: 9. Text analytics

Lecture 24 9.1 Text analytics

Section 10: 10. Sentiment analysis

Lecture 25 10.1 Sentiment analysis

Section 11: 11. Image Analytics

Lecture 26 11.1 Image Analytics

Section 12: 12. Video analytics

Lecture 27 12.1 Video analytics

Lecture 28 12.2 How do I use it?

Section 13: 13. Voice analytics

Lecture 29 13.1 Voice analytics

Section 14: 14. Monte Carlo simulations

Lecture 30 14.1 Monte Carlo simulations

Section 15: 15. Linear programming

Lecture 31 15.1 Linear programming

Lecture 32 15.2 How do I use it?

Section 16: 16. Cohort analysis

Lecture 33 16.1 Cohort analysis

Section 17: 17. Factor analysis

Lecture 34 17.1 Factor analysis

Lecture 35 17.2 Tips and Traps?

Section 18: 18. Neural network analysis

Lecture 36 18.1 Neural network analysis

Lecture 37 18.2 How do I use it?

Section 19: 19. Meta analytics literature analysis

Lecture 38 19.1 Meta analytics literature analysis

Lecture 39 19.2 Tips and Traps

Section 20: 20. Analytics inputs tools or data collection methods

Lecture 40 20.1 Analytics inputs tools or data collection methods

Section 21: 21. Qualitative surveys

Lecture 41 21.1 Qualitative surveys

Section 22: 22. Focus groups

Lecture 42 22.1 Focus groups

Section 23: 23. Interviews

Lecture 43 23.1 Interviews

Section 24: 24. Ethnography

Lecture 44 24. Ethnography

Section 25: 25. Test capture

Lecture 45 25.1 Test capture

Lecture 46 25.2 How can I use it?

Section 26: 26. Image capture

Lecture 47 26.1 Image capture

Section 27: 27. Sensor date

Lecture 48 27.1 Sensor date

Lecture 49 27.2. Possible data Sources

Section 28: 28. Machine data capture

Lecture 50 28.1 Machine data capture

Lecture 51 28.2. Why does it matter

Lecture 52 28.3. How do I get started?

Section 29: 29. Financial analytics

Lecture 53 29.1 Financial analytics

Section 30: 30. Customer profitability analytics

Lecture 54 30.1 Customer profitability analytics

Lecture 55 30.2. Why does it matter?

Section 31: 31. Product Profitability

Lecture 56 31.1 Product Profitability

Lecture 57 31.2. Tips and traps

Section 32: 32. Cash flow analysis

Lecture 58 32.1 Cash flow analysis

Lecture 59 32.2. How do I use it

Section 33: 33. Value driver analytics

Lecture 60 33. Value driver analytics

Lecture 61 33.2. Why does it matter

Section 34: 34. Shareholder value analytics

Lecture 62 34.1 Shareholder value analytics

Section 35: 35. Market analytics

Lecture 63 35.1 Market analytics - Unmet need analytics

Lecture 64 35.2. Why does it matter

Lecture 65 35.3. Tips and traps

Section 36: 36. Market size analytics

Lecture 66 36.1 Market size analytics

Section 37: 37. Demand forecasting

Lecture 67 37.1 Demand forecasting

Lecture 68 37.2. How do I use it

Section 38: 38. Market trends analytics

Lecture 69 38.1 Market trends analytics

Lecture 70 38.2. Tips and traps

Section 39: 39. Non- customer analytics

Lecture 71 39.1 Non- customer analytics

Lecture 72 39.2. Why does it matter?

Section 40: 40. Competitor analytics

Lecture 73 40.1 Competitor analytics

Section 41: 41. Pricing analytics

Lecture 74 41.1 Pricing analytics

Section 42: 42. Marketing channel

Lecture 75 42.1 Marketing channel

Section 43: 43. Brand analytics

Lecture 76 43.1 Brand analytics

Section 44: 44. Customer analytics

Lecture 77 44.1 Customer analytics

Section 45: 45. Customer lifetime

Lecture 78 45.1 Customer lifetime

Lecture 79 45.2. Why does it matter?

Section 46: Assignment Part

Lecture 80 Assignment Part

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