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Data Alchemy: Mining for Business Intelligence

Name: Data Alchemy: Mining for Business Intelligence
Rating: 4.4 (45 reviews)

Turning Raw Data into Gold ;Uncovering Insights with Data Mining ; The Magic of Business Intelligence From Numbers

Created byDr. John Daniel Mclellan

Last updated 4/2025

English

English [Auto],

What you'll learn

You will gain an understanding of the key concepts, tools, and processes in data analytics and business intelligence.
Understand a range of analytical techniques including descriptive, diagnostic, predictive, & prescriptive analytics, & understand their l business applications
Learn how companies use time series analysis, sensitivity analysis, and simulation models help make informed decisions and predictions based on historical data.
Learn how companies effectively visualize data and communicate findings and insights in a clear and impactful manner, using various tools and best practices.

Course content

13 sections • 35 lectures • 2h 15m total length

Introduction0:40
Explore data alchemy and management concepts through modular lectures, with quizzes, optional assignments, and real-life case studies to boost career-relevant understanding.
Overview of course content1:13
Explore big data analytics, data mining, and business intelligence; learn key terminology, the four big V's, and data types, both structured and unstructured, and supervised and unsupervised.
Creating Your Profession Development Portfolio4:59
Create and maintain a professional development portfolio (pdp) as a dynamic evidence collection. Show ongoing learning, online courses, and certificates to support career advancement and performance assessments.

Introduction to Data Analytics and Business Intelligence5:08
Explore the fundamentals of data analytics and business intelligence, including key terms, dashboards, and the four V's of big data, plus structured, semi-structured, and unstructured data.
Importance in the modern business environment4:27
Uncover how data analytics and business intelligence transform raw data into actionable insights, enabling data-driven decisions, real-time monitoring, and strategic planning for a competitive edge.
Key terms and concepts6:41
Master essential terms in data analytics and business intelligence, including big data, data mining, descriptive, predictive, and prescriptive analytics, data visualization, and data governance.
Test Your Understanding on Data Analytics and BI

Defining Big data and the four V's6:57
Explore big data through the four v's: volume, velocity, variety, and veracity, and understand opportunities and challenges of processing structured, semi-structured, and unstructured data.
Opportunities and Challenges5:09
Explore the opportunities of big data to drive data-driven decision making, enhance customer understanding, spur innovation, and improve operational efficiency, while addressing data quality, privacy, scalability, and ethics.
Structured, semi-structured, and unstructured data5:08
Explore structured, semi-structured, and unstructured data in business enterprises to drive insights and informed decisions. Understand how each data type supports financial reporting, CRM, inventory, sentiment analysis, and competitive intelligence.
Case studies of Big Data applications3:17
Explore how a health care provider leverages a big data platform to integrate electronic health records, imaging, labs, and genomics, use predictive analytics to improve patient outcomes and reduce costs.
Real-world examples and case studies3:42
Apply real-world data analytics techniques to a retail scenario, analyzing transactions, customer data, and market basket analysis to drive targeted marketing, store layout, and inventory decisions.
Test Your Understanding
Matching Terms

Data, Information, Knowledge. Wisdom and action4:27
Explore the data-information-knowledge-wisdom-action pyramid and see how raw data, such as daily sales figures, become actionable insights for business intelligence.
Case studies illustrating the DIKW Pyramid in action2:50
This case study shows applying the DIKW pyramid—data, information, knowledge, wisdom—to retail strategy. Converting raw data into insights enhances customer experience, inventory management, staffing during peak times, and sales.
Progression3:58
Explore the data to information, knowledge, wisdom, and action progression and apply it to strategic planning, operational efficiency, customer understanding, risk management, and innovation.

Definition and components of BI5:12
Explore business intelligence tools that analyze data and present actionable insights. Discover components like data warehousing, data mining, reporting, analytics, and visualization to drive strategic and operational decisions.
BI tools and applications in business5:18
Discover how business intelligence tools, including Tableau, Power BI, SAP, Oracle BI, and IBM Cognos Analytics, enable performance monitoring, customer analysis, and strategic planning.
Hands-on BI tool demonstrations2:45
Use Tableau to import sales data, build bar and line visualizations, create interactive dashboards with filters, and tell a data story highlighting trends, categories, regional insights, and actionable business actions.
Matching Terms

Definition and process of data mining3:01
Extract patterns from data using statistics and machine learning to support decision making. Follow a data mining process from collection and cleaning to exploration, modeling, evaluation, and deployment.
Challenges and iterative nature of data mining3:02
Explore data mining's key challenges, from data quality and big data volumes to privacy and GDPR, and its iterative cycle of refinement, experimentation, validation, and adaptation.
Practical applications in various industries3:33
Data mining unlocks patterns from large data and applies across retail, banking, health care, telecom, e-commerce, manufacturing, and education for segmentation, forecasting, risk management, fraud detection, recommendations, and predictive maintenance.

Basics of SQL4:09
Learn the basics of SQL, including data definition, manipulation, control, and transaction commands, plus queries, joins, subqueries, aggregates, constraints, indexes, and views for relational databases.
Using SQL for data retrieval and management4:40
Master SQL for data retrieval and management in relational database systems. Use select statements, joins, aggregate functions, and group by having, plus insert, update, and delete operations.
Practical exercises3:45
Engage in practical exercises across SQL data analytics and business intelligence, from creating databases and tables to writing queries, performing data exploration, predictive modeling, and BI dashboards.

Overview of descriptive, diagnostic, predictive, and prescriptive analytics4:01
Explore descriptive, diagnostic, predictive, and prescriptive analytics to understand past performance and forecast future outcomes. Use aggregation, reporting, visualization, and modeling to identify trends, root causes, and optimization opportunities.
Application in business scenarios3:20
Apply time series analysis to forecast trends, seasonal patterns, demand planning, sales, inventory, cash flow, and marketing performance for strategic business planning.

Google3:51
Learn how Google uses AI, data analytics, and business intelligence to enhance products and empower other businesses. Explore tools like TensorFlow, BigQuery, Looker, Data Studio, and Google Analytics for insights.
Facebook3:35
Facebook uses artificial intelligence, data analytics, and BI tools to personalize the newsfeed, moderate content, optimize ads, test features, analyze sentiment, and visualize trends for data-driven decisions.
Tic Tok3:44
Explore how TikTok uses ai, data analytics, and bi tools to personalize recommendations, moderate content with computer vision and nlp, and drive growth through dashboards and market insights.

Requirements

Analytical skills

Description

In the digital age, the ability to analyze and interpret data has become a crucial skill for success in the business world. This comprehensive course, "Data Analytics and Business Intelligence," is specifically designed to equip professionals, students, and business leaders with the expertise needed to navigate the data-driven landscape effectively. This course delves into the realms of data analytics, data mining, and BI, offering a blend of theoretical knowledge and practical application.

Students will learn the essentials of data analytics, starting with an introduction to its importance and key concepts, and progressing to more advanced topics such as big data, data mining, and various analytical models. The course is meticulously structured into modules, each focused on a vital aspect of data analytics and BI.

This course begins by explaining the complexities of big data, exploring the four Vs (volume, velocity, variety, and veracity), and delving into the opportunities and challenges presented by this data revolution. Participants will gain a deep understanding of how structured, semi-structured, and unstructured data are leveraged by businesses to create value.

Through a combination of theoretical foundations and hands-on exercises, learners will progress from data to information to knowledge to insight to action. They will learn data management practices, recognize data as a strategic asset, and develop competencies in data governance and quality assurance.

Data mining, a pivotal component of BI, will be thoroughly explained, and participants will grasp the challenges, iterative nature, and artistic-science blend that characterize data mining. They will also discover how query tools like Structured Query Language (SQL) are used for efficient data retrieval.

Predictive analytics will be another focal point, with participants learning various analytic models such as clustering, classification, and regression. They will develop the capability to analyze data, reveal patterns, and provide actionable insights.

Time series analysis, sensitivity analysis, and simulation models will be demystified, empowering learners to make accurate predictions and sound decisions based on historical data.

This course is constructed using the Learning Outcomes to be tested in the Certified Management Accounting Part 1 Exam - specifically Section F4 Data Analysis. The course also serves to provide you with a Professional Development Certificate upon completion.

Who this course is for:

This course is of interest to those seeking a professional development credits.

Data Alchemy: Mining for Business Intelligence

What you'll learn

Explore related topics

Course content

Introduction to this course3 lectures • 7min

Module 1. Introduction to Data Analytics and Business Intelligence3 lectures • 16min

Module 2: Introduction to Big Data and Data Types5 lectures • 24min

Module 3: From Data to Action - The DIKW Pyramid3 lectures • 11min

Module 4 Business Intelligence (BI)3 lectures • 13min

Module 5 - Data Mining3 lectures • 10min

Module 6 - SQL and Query Tools3 lectures • 13min

Module 7 Overview of Analytical Types2 lectures • 7min

Module 8 -How these tools are Used in the retail Industry2 lectures • 8min

Module 9 - How these tools are Used in Social Media3 lectures • 11min

Requirements

Description

Who this course is for: