Business Intelligence Foundations: Understanding Data

Explore the data ecosystem, including data collection, storage, ETL, analysis, modeling, dashboards, and how insights drive decision making, while uncovering roles and opportunities across the data lifecycle.

Explore the data ecosystem by examining data collection, storage, ETL, analysis, modeling, dashboards, and application use, highlighting how these stages form a continuous data cycle.

Examine data roles across the ecosystem: data engineers and architects design pipelines for data flow and accessibility. Cloud data engineers and ETL developers secure, cleanse, and prepare data for analysis.

Explore data roles from data analysts to machine learning engineers, BI developers, and data visualization specialists, showing how they turn data into insights that inform business decisions.

Discover how to choose a data role that fits your strengths—no single best option—and start with beginner-friendly paths like data analyst or BI developer for strong growth.

Explore in-demand data roles: data analyst, Pi developer, data engineer, data scientist, machine learning engineer, and data product manager. Learn to choose a role that fits your interests and goals.

Learn the essential data skills to start a career, including SQL querying, Excel data cleaning, BI dashboards with Power BI or Tableau, Python basics, ETL, and domain knowledge.

AI enhances data work and creates new opportunities, but you guide it, ask the right questions, and make final decisions—start small and learn to use AI.

Learn how ai supports every phase of the data workflow—from data collection and storage to etl, analysis, modeling, and dashboards—personalizing experiences and speeding clean data and insights for better decisions.

Discover how AI speeds up day-to-day data tasks like data cleaning and chart creation. Realize AI generates reports, SQL queries, and insights quickly, freeing you for strategy and decisions.

Explore how ai analyzes data at scale and generates reports, while humans shape questions, context, and ethics to drive smarter decisions and faster, responsible insights in business intelligence.

Think of data as a simple table, with rows representing records and columns detailing attributes like name, date, price, or quantity. Rows and columns together organize data for easy reading.

Identify the common data types in real datasets, including numerical data, integers and decimals, text data, date and time data, and boolean data, to read, analyze, and work with data.

Explore how customer actions on Amazon create data, logging clicks, views, and purchases, which are stored, processed, and analyzed to generate dashboards that inform business decisions.

Explore industry-specific data sources—from retail point of sale and loyalty programs to banking transactions, healthcare records, GPS and sensors, learning platforms, and manufacturing logs—and how they drive insights and efficiency.

Observe a dataset first to understand its structure and columns, then ask smart questions about who buys most, regional revenue, and price relationships, letting the data tell its story.

learn to read a real world sales data set by examining its structure and columns, where each row is a sale, observe patterns, and ask simple questions before analysis.

Explore finance data from a dataset organized in rows and columns, where each transaction includes the transaction id, date, department, vendor, amount, and status to support cost control and budgeting.

Explore an employee dataset organized in rows, where each row represents an employee and reveals id, name, department and job roles, joining date, employment type, location, salary, performance, and status.

Compare clean data and messy data to highlight accuracy, completeness, consistency, lack of duplicates, and correct data types, and see how clean data yields reliable insights and better decisions.

Identify the most common data mistakes, including missing values, duplicate records, inconsistent text, wrong data types, outliers, and date issues, to improve early data quality.

Understand why data errors occur, from manual entry and mismatched formats across systems to human mistakes and missing validations, and how evolving rules and unclear ownership degrade data quality.

Quickly spot errors in real data by scanning for issues, checking missing values, duplicates, and data types, spotting outliers, using sort and filter, and applying simple logic checks.

spot and fix common data issues in real-world excel data through quick scans, missing values, duplicates, data types, outliers, and consistency checks.

Spot and fix common data quality issues in Excel, including missing values, duplicates, data types, inconsistent text, outliers, and date formats, to ensure reliable data for analysis and dashboards.

Explore how tables store information with rows as records and columns as data types, like sale id, date, customer, product, quantity, and price, the foundation of databases, reports, and dashboards.

Compare one big table to multiple tables containing customer, product, and sales data. Use IDs to connect tables, reduce duplication, and support data modeling and analytics.

Learn how primary keys uniquely identify records and foreign keys link tables to create relationships. See how these keys enable joins, data modeling, and analytics across tables.

Understand how a star schema connects a central sales fact table to dimension tables using primary and foreign keys, enabling clear, fast insights.

Define and clarify KPI as a key performance indicator, a focused metric that signals business performance and guides decisions, with examples like revenue and profit.

Identify key sales KPIs by examining revenue, growth, and trends to assess current performance. Use these metrics to monitor improvement and guide future business decisions.

Explore finance KPIs that reveal how revenue becomes profit after expenses, how budgets control spending, and how these metrics guide smarter financial decisions.

Explore HR KPIs focusing on people, defining headcount as total employees and attrition as departures, with examples and implications for hiring, work allocation, retention, and dashboards for decision making.

Track key performance indicators with dashboards to monitor sales, profit, headcount, and attrition. Identify insights, take action, and measure impact to turn data into decisions.

Explore the data-to-insight journey from collection to dashboards. See how teams store raw data from websites, apps, and transactions, clean it, analyze it, and turn it into actionable insights.

ETL extracts data from Excel files, databases, websites, or apps, transforms it by cleaning and applying business rules, and loads the results into a reporting system for dashboards.

Explore how Excel, SQL, and Power BI fit into the data to dashboard journey, with Excel for exploration, SQL for data preparation, and Power BI for visualization and sharing insights.

A day in the life of a BI developer, who checks data refreshes and pipelines, cleans and validates data, writes queries, builds dashboards, and shares insights to guide business decisions.

Identify what the business cares about and which numbers matter by understanding data and KPIs. Learn Excel, then SQL, then Power BI to build dashboards and a portfolio.

Explore a sample sales dashboard that uses metrics like total revenue, orders, and customers to reveal trends, comparisons, and filters by date, branch, product category, sales type, and campaigns.

Master Excel basics to work with data, sorting and filtering to track KPIs, fix errors, and recognize patterns, building a solid data foundation before SQL or dashboards.