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Certification in Key Business Analytics and Data Analytics

Name: Certification in Key Business Analytics and Data Analytics
Rating: 4.7 (7 reviews)

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

Created byHuman and Emotion: CHRMI

Last updated 9/2024

English

English [Auto],

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.

Course content

46 sections • 80 lectures • 12h 8m total length

Introduction and Study Plan9:11
Explore the introduction to key business analytics and data analytics, including data collection, experimental design, ab testing, visual analytics, forecasting, text and sentiment analysis, image, video, and voice analytics.
1.1. Details of Introduction11:28
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.
1.2. The raw materials -Data10:58
Explore the raw materials of data—volume, velocity, variety, veracity, and value—and how data types from transactional to social media and machine data enable real-time analytics and informed decision making.
1.3. Data types and format9:52
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.
1.4. How to use this10:29
Learn to use this book to prioritize key analytics, including customer analytics, descriptive, predictive, and prescriptive analytics, while exploring data types, collection, analysis, and visualization to drive business decisions.
1.5. Who is this for?5:34
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.

2.1. What is it?7:49
Explore business experiments, experimental design, and A/B testing to test hypotheses and drive data-driven decisions with metrics, randomization, and control and experimental groups.
2.2. What business questions is it helping me to answer8:51
Explore how business experiments, including A/B testing, answer key questions across product development, marketing, pricing, and operations to optimize customer experience, growth, and revenue.
2.3. Create a hypothesis6:15
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.
2.4. Design the experiment9:34
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.
2.5. Tips and traps8:13
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.

3.1. What is it7:38
Explore visual analytics, the science of analytical reasoning supported by interactive visuals that combine data visualization, automated analysis, and human insight to reveal patterns and support decision making.
3.2. What business questions is it helping me to answer8:55
Identify where best customers are located, their profile, and market share trends using visual analytics and data visualization, guided by performance metrics, customer insights, and strategic planning.

4.1 Correlation analysis9:51
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.
4.2. What business questions is it helping me to answer8:48
Identify how loyalty, profitability, price, tenure, and absenteeism relate through correlation analysis, guiding decisions on marketing spend, product quality, customer service, and forecasting future sales.

5.1 Scenario analysis7:33
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.
5.2. What business questions is it helping me to answer9:51
Using scenario analysis, you answer essential business questions about strategic direction and expansion by evaluating economic, technological, regulatory, and marketing conditions and assessing risks and contingencies.

6.1 Forecasting or Time8:09
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.
6.2. What business questions is it helping me to answer8:21
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.

7.1 Data mining11:27
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.
7.2. What business questions is it helping me to answer8:17
Data mining helps decision makers predict the future by uncovering patterns, correlations, trends, and insights to answer key business questions across customer segments, fraud, and marketing.

8.1 Regression analysis9:08
Explore regression analysis as a statistical method to model relationships between dependent and independent variables, predict outcomes, and drive data-driven decisions in sales, marketing, finance, and operations.
8.2. What business questions is it helping me to answer11:42
Regression analysis helps answer key business questions by revealing relationships among variables and forecasting outcomes from historical data, guiding sales and marketing decisions, advertising spend, and 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

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

Certification in Key Business Analytics and Data Analytics

What you'll learn

Explore related topics

Course content

Introduction to Key Business Analytics6 lectures • 58min

2. Business experiments or experimental design or AB testing5 lectures • 41min

3. Visual analytics2 lectures • 17min

4. Correlation analysis2 lectures • 19min

5. Scenario analysis2 lectures • 17min

6. Forecasting or Time2 lectures • 17min

7. Data mining2 lectures • 20min

8. Regression analysis2 lectures • 21min

9. Text analytics1 lecture • 10min

10. Sentiment analysis1 lecture • 9min

Requirements

Description

Who this course is for: