Learn the foundations of business intelligence, sector trade-offs, organizational structures, and technology stacks while mastering coursework, certifications, and interview success strategies
Purchase of the print or Kindle book includes a free PDF eBook
Key Features
Identify promising job opportunities and ideal entry point into BI
Build, design, implement, and maintain BI systems successfully
Ace your BI interview with author's expert guidance on certifications, trainings, and courses
Book Description
Navigating the challenging path of a business intelligence career requires you to consider your expertise, interest, and skills. Business Intelligence Career Master Plan explores key skills like data modeling, visualization, warehousing, organizational structures, technology stacks, coursework, certifications, and interview advice, enabling you to make informed decisions about your BI journey.
You’ll start by assessing the different roles in BI and matching your skills and career with the tech stack. You’ll then learn to build taxonomy and a data story using visualization types. Additionally, you’ll explore the fundamentals of programming, frontend development, backend development, software development lifecycle, and project management, giving you a broad view of the end-to-end BI process. With the help of the author’s expert advice, you’ll be able to identify what subjects and areas of study are crucial and would add significant value to your skillset.
By the end of this book, you’ll be well-equipped to make an informed decision on which of the myriad paths to choose in your business intelligence journey based on your skillset and interests.
What you will learn
Understand BI roles, roadmap, and technology stack
Accelerate your career and land your first job in the BI industry
Build the taxonomy of various data sources for your organization
Use the AdventureWorks database and PowerBI to build a robust data model
Create compelling data stories using data visualization
Automate, templatize, standardize, and monitor systems for productivity
Who this book is for
This book is for BI developers and business analysts who are passionate about data and are looking to advance their proficiency and career in business intelligence. While foundational knowledge of tools like Microsoft Excel is required, having a working knowledge of SQL, Python, Tableau, and major cloud providers such as AWS or GCP will be beneficial.
Author(s): Eduardo Chavez, Danny Moncada
Edition: 1
Publisher: Packt Publishing
Year: 2023
Language: English
Pages: 284
Cover
Title Page
Copyright
Dedication
Foreword
Contributors
Preface
Chapter 1: Breaking into the BI World
Where to start?
BI roles
Problem solving
Specific industry knowledge and subject matter expertise
Communication skills
Statistical analysis
Technical knowledge
Business acumen
Keep up with innovation
Potential entrances to the BI world
BI roadmap
ETL developers
Data architects
Data modelers
BI developers
Data scientists
Technology solutions stack
Non-technical data analysis
Case 1
Case 2
Case 3
Case 4
Case 5
Summary
Chapter 2: How to Become Proficient in Analyzing Data
Building a taxonomy of your data sources
How to use a BI tool to explore data
Understanding your data needs
Summary
Chapter 3: How to Talk Data
Presenting data
Know your audience
Choose the right visualization
Keep it simple
Use color and formatting effectively
Provide context
Tell a story
High-level dashboards
Operational reports
Talking to stakeholders
Data visualization taxonomy
Bar charts
Line charts
Pie charts
Scatter plots
Area charts
Heat maps
Bubble charts
Gauge charts
Tree maps
Box plots
Advanced data visualizations
Sankey diagrams
Bullet charts
Taxonomy diagrams
Pareto diagrams
Decision tree for picking a visualization
Storytelling
Summary
Chapter 4: How To Crack the BI Interview Process
Finding the right interview
Building a business problem and data solutions matrix
Potential business cases and solutions
Teamwork
Customer service
Adaptability
Time management
Communication
Motivation and values
A hypothetical BI interview process and what to expect
General business intelligence interview questions
Scenario-based BI questions
Business case study questions
SQL business intelligence interview questions
Database design business intelligence questions
Python business intelligence interview questions
Summary
Chapter 5: Business Intelligence Landscape
The current landscape and the most effective technologies to study
Collecting data
Storing data
Cleaning and preparing data
Analyzing data with BI tools
Focusing on user experience
The use of AI
BI developer versus BI architect versus data engineer versus data modeler versus business analyst
Business acumen
Increased productivity
Automation
Templating
Standardization
Monitoring systems
Efficient team management
Summary
Chapter 6: Improving Data Proficiency or Subject Matter Expertise
Data proficiency
Concluding thoughts
Subject matter expertise
Concluding thoughts
Data behavior in different business units
Data behavior in marketing
Data behavior in sales
Data behavior in finance and accounting
Data behavior in operations
Data behavior in human resources
Data behavior in academia
Analytical thinking and problem-solving techniques
Problem-solving
Leveraging online tools for problem-solving
Web (Google) search
Stack Overflow
AI chatbots (ChatGPT/Bard)
Summary
Chapter 7: Business Intelligence Education
Academic programs, training courses, certifications, and books
Academic programs
Training courses and certificates
Data architecture books
Data modeling books
Data analysis and visualization books
Summary
Chapter 8: Beyond Business Intelligence
Business analytics
Cross-pillar reporting
Measuring your BI success
Executive briefing books
Customer-focused processes
Automation
BA final thoughts
Data science
Data exploration
Summary
Chapter 9: Hands-On Data Wrangling and Data Visualization
Technical requirements
Data analysis using Python
Data wrangling and visualization with Python
Step 1 – loading the dataset
Step 2 – an initial exploration of the dataset
Step 3 – remove duplicate rows
Step 4 – cleaning and transforming data
Step 5 – saving the cleaned dataset
Step 6 – load the cleaned dataset
Step 7 – creating visualizations to summarize the data
Tableau for data visualization
Tableau dashboard hands-on
Summary
Conclusion
Index
About Packt
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