Enroll in Business Analytics

What Is Business Analytics

Explore descriptive, diagnostic, predictive, and prescriptive analytics, and see how each type supports different business decisions in real organizations and everyday scenarios.

The Analytics Lifecycle

Walk through the stages of an analytics project, from defining the question and collecting data to modeling, reporting, and monitoring outcomes over time.

Roles in a Data Team

Compare the responsibilities of business analysts, data analysts, data engineers, and data scientists, and learn how they collaborate on shared projects.

Framing Business Problems

Translate vague stakeholder requests into clear, measurable analytical questions with defined objectives, scope, assumptions, and success criteria that guide the work.

Types of Data

Distinguish structured from unstructured data, and categorical from numerical variables, and understand how each type shapes the analysis methods you can apply.

Data Sources and Collection

Identify common internal and external data sources, including surveys, transactions, and web data, and evaluate their relevance, cost, and trustworthiness.

Data Quality and Integrity

Recognize missing values, duplicates, outliers, and inconsistencies, and learn how poor data quality leads to misleading conclusions and costly decisions.

Tables, Rows, and Relationships

Understand how data is organized into tables with keys and relationships, preparing you for spreadsheets, databases, and later work with business intelligence tools.

Excel Workspace and Navigation

Get comfortable with workbooks, worksheets, ribbons, shortcuts, and selection techniques that speed up everyday spreadsheet work and reduce common navigation mistakes.

Entering and Formatting Data

Apply number formats, styles, alignment, and table formatting to make datasets readable, consistent, and ready for analysis and sharing with colleagues.

Formulas and Cell References

Write basic formulas using relative, absolute, and mixed references, and learn the order of operations to build calculations that copy correctly.

Essential Functions

Use SUM, AVERAGE, MIN, MAX, COUNT, and ROUND to summarize numeric data quickly, and learn how to check results for common errors.

Cleaning Text Data

Use TRIM, CLEAN, LEFT, RIGHT, MID, and Text to Columns to fix inconsistent text entries and split combined fields into usable columns.

Handling Duplicates and Missing Values

Detect and remove duplicates, locate blanks, and choose sensible strategies for filling or excluding missing values without distorting your analysis results.

Logical Functions

Build decision rules with IF, AND, OR, and nested conditions to categorize records, flag exceptions, and automate simple business logic in spreadsheets.

VLOOKUP, HLOOKUP, and XLOOKUP

Retrieve matching information across tables using lookup functions, compare their strengths and limits, and troubleshoot common errors such as mismatched keys.

Descriptive Statistics in Excel

Calculate mean, median, mode, variance, and standard deviation, and interpret what these measures reveal about the center and spread of business data.

PivotTables for Summarizing Data

Create PivotTables to group, aggregate, and compare data by category and time, and refine them with calculated fields and value settings.

Choosing the Right Chart

Match chart types such as bar, line, pie, and scatter to specific analytical questions, and avoid common design choices that mislead viewers.

Building an Excel Dashboard

Combine PivotTables, charts, slicers, and key figures into an interactive one-page dashboard that lets stakeholders explore performance without editing the underlying data.

How Databases Support Business Decisions

Explore where databases sit in the analytics workflow, how companies store operational data, and why analysts rely on SQL instead of spreadsheets alone.

Tables, Rows, Columns, and Data Types

Learn how tables are structured, how data types such as text, numbers, and dates behave, and why choosing the right type matters.

Primary Keys and Foreign Keys

Discover how keys uniquely identify records and link tables together, enabling accurate joins and protecting the integrity of customer, order, and product data.

Reading Entity Relationship Diagrams

Interpret entity relationship diagrams to understand one-to-many and many-to-many links, and plan which tables you need to answer a business question.

SELECT and FROM Fundamentals

Write your first queries to retrieve specific columns, use aliases, and limit results, building habits for clean, readable, and efficient SQL code.

Filtering Rows with WHERE

Apply comparison operators, AND, OR, IN, BETWEEN, and LIKE to isolate exactly the customers, orders, or transactions that a business question requires.

Sorting and Removing Duplicates

Use ORDER BY to rank results and DISTINCT to remove repeated values, producing tidy lists such as top products or unique customers.

Handling NULL Values

Understand what missing data means in SQL, test for NULL correctly, and use COALESCE to replace gaps so reports remain accurate.

Aggregate Functions: COUNT, SUM, AVG

Calculate totals, averages, minimums, and maximums across records to produce headline business metrics such as total sales and average order value.

GROUP BY and HAVING

Group records by category, region, or time period and filter the grouped results to compare performance across segments of the business.

INNER JOIN and LEFT JOIN

Combine customers, orders, and products using inner and left joins, and learn when each type keeps or drops unmatched records.

Multi-Table Joins and Join Pitfalls

Chain several joins together, avoid duplicated rows and fan-out errors, and verify that your combined results reconcile with known totals.

Subqueries in SELECT, FROM, and WHERE

Nest queries inside other queries to compute benchmarks, filter by derived values, and break multi-step business questions into manageable pieces.

Common Table Expressions for Readable Queries

Structure complex logic with CTEs, name intermediate steps, and write maintainable queries that colleagues can review, reuse, and extend easily.

CASE Statements and Conditional Logic

Create custom categories, flags, and buckets with CASE expressions, such as customer tiers or order size bands, directly inside your queries.

Window Functions for Ranking and Running Totals

Use ROW_NUMBER, RANK, and running sums with OVER and PARTITION BY to rank products and track cumulative revenue without collapsing rows.

Creating Tables and Modifying Data

Use CREATE, INSERT, UPDATE, and DELETE safely to build practice tables and understand how analysts handle changes to stored data responsibly.

Views and Reusable Query Logic

Create views that package common business logic, giving teams consistent metric definitions and simplifying repeated reporting queries across projects.

Query Performance and Indexing Basics

Learn why queries run slowly, read simple execution plans, and understand how indexes and smarter filtering improve speed on large datasets.

Preparing Datasets for Dashboards

Shape clean, analysis-ready tables by deduplicating, standardizing fields, and aggregating at the right level for downstream visualization tools.

Setting Up Python and Jupyter Notebooks

Install Python, configure Jupyter, manage packages with pip, and run your first analysis notebook while learning good documentation habits for reproducible work.

Variables, Data Types, and Operators

Work with numbers, strings, booleans, and operators to perform calculations and build simple logic that supports everyday business analysis and reporting tasks.

Lists, Dictionaries, and Tuples

Store and organize data using Python collections, and practice indexing, slicing, and iterating to extract the information needed for analytical questions.

Control Flow and Loops

Use conditionals and loops to apply business rules, categorize records, and process large collections of values efficiently without manual repetition.

NumPy Arrays and Vectorized Operations

Create arrays, apply vectorized calculations, and use broadcasting to process numerical data quickly compared with traditional loops in plain Python.

Pandas Series and DataFrames

Load CSV and Excel files into DataFrames, inspect structure, select rows and columns, and filter records to answer targeted business questions.

Cleaning Missing and Inconsistent Data

Detect missing values, duplicates, and formatting errors, then apply sensible imputation and correction strategies while documenting each cleaning decision clearly.

Grouping, Aggregation, and Pivot Tables

Summarize data with groupby, aggregation functions, and pivot tables to compare segments, products, and regions much like in Excel and SQL.

Descriptive Statistics in Practice

Calculate and interpret mean, median, variance, percentiles, and skewness in Python to summarize business metrics and understand how values are distributed.

Charting with Matplotlib

Build line, bar, and scatter charts with Matplotlib, customizing labels, scales, and layouts so visuals communicate trends clearly to stakeholders.

Statistical Visualization with Seaborn

Create histograms, box plots, heatmaps, and pair plots with Seaborn to examine distributions, spot outliers, and compare groups quickly.

Correlation and Relationship Analysis

Measure and visualize correlations between variables, distinguish correlation from causation, and identify candidate drivers of revenue, churn, or cost.

Probability Fundamentals for Business

Learn probability rules, conditional probability, and Bayes theorem through practical examples such as customer conversion, risk events, and quality control.

Probability Distributions

Explore normal, binomial, and Poisson distributions, simulate them in Python, and match each to realistic business processes and outcomes.

Sampling and Confidence Intervals

Understand sampling methods and the central limit theorem, then compute confidence intervals to express the uncertainty around estimated business metrics.

Hypothesis Testing and P-Values

Formulate null and alternative hypotheses, run t-tests and chi-square tests in Python, and interpret p-values and errors responsibly.

Simple Linear Regression

Fit and interpret a single-variable regression model, understand slope, intercept, and R-squared, and use it to estimate business outcomes.

Multiple Regression and Feature Selection

Extend regression to several predictors, handle categorical variables, check multicollinearity, and choose features that improve explanatory power sensibly.

Regression Diagnostics and Assumptions

Test residuals, linearity, and variance assumptions, detect influential points, and decide when a model can be trusted for decisions.

Logistic Regression for Classification

Predict binary outcomes such as churn or purchase with logistic regression, interpret odds ratios, and evaluate results using confusion matrices.

Power BI Desktop and Service Overview

Tour the Power BI ecosystem, including Desktop, Service, and mobile, and understand how reports flow from authoring to sharing across an organization.

Connecting to Excel, CSV, and Web Data

Import data from files and web pages, compare import and DirectQuery options, and refresh sources so reports stay accurate and current.

Connecting to SQL Databases

Link Power BI to SQL Server and other databases, write native queries, and choose storage modes that balance performance with data freshness.

Cleaning Data with Power Query

Use Power Query to remove errors, split columns, fix data types, and filter rows, creating repeatable cleaning steps for any dataset.

Star Schema and Dimensional Modeling

Understand fact and dimension tables, grain, and star schema design, and learn why good modeling underpins accurate and fast Power BI reports.

Creating and Managing Relationships

Define one-to-many relationships, set cross-filter direction, and troubleshoot ambiguous paths so filters flow correctly across your data model.

Building a Date Table and Time Intelligence Setup

Create a dedicated calendar table, mark it as a date table, and prepare hierarchies that support year, quarter, and month analysis.

Role-Playing Dimensions and Many-to-Many Cases

Handle multiple date relationships, bridge tables, and many-to-many scenarios while avoiding common modeling mistakes that distort business results.

DAX Basics: Measures and Calculated Columns

Learn DAX syntax, the difference between measures and calculated columns, and write core aggregations like sum, average, count, and distinct count.

Filter Context and CALCULATE

Understand row and filter context, then use CALCULATE to modify filters and build flexible measures such as segment sales and share of total.

Logical, Text, and Iterator Functions

Apply IF, SWITCH, SUMX, and AVERAGEX to create conditional logic, weighted metrics, and row-by-row calculations within business reports.

Time Intelligence Functions

Calculate year-to-date, month-over-month, and year-over-year growth using DAX time intelligence functions built on a properly marked date table.

Choosing the Right Visual for the Question

Match bar, line, scatter, map, and table visuals to analytical questions, and avoid misleading charts that confuse or misdirect decision makers.

Dashboard Layout and Design Principles

Apply hierarchy, whitespace, color, and consistent formatting to build clean layouts, using themes and templates that reflect brand standards.

Slicers, Filters, and Cross-Highlighting

Add slicers, page and report filters, and sync controls so users can explore data interactively without breaking the underlying analysis.

Drill-Through, Tooltips, and Bookmarks

Create drill-through pages, custom tooltips, and bookmark-driven navigation to give users guided, layered exploration of detailed business information.

Publishing Reports and Managing Workspaces

Publish reports to the Power BI Service, organize workspaces and apps, and share content with the right audiences and permissions.

Scheduled Refresh and Gateways

Configure on-premises gateways and scheduled refreshes, monitor failures, and keep dashboards updated automatically without manual intervention.

Row-Level Security and Governance

Implement row-level security roles, apply sensitivity labels, and follow governance practices that protect confidential data while enabling self-service analytics.

Data Storytelling for Stakeholders

Structure insights into a narrative with context, evidence, and recommendations, tailoring messages to executive, managerial, and technical audiences.

Predictive Analytics Workflow and Problem Framing

Understand the end-to-end predictive workflow and learn to translate vague business questions into measurable prediction targets, features and success criteria.

Simple and Multiple Linear Regression in Python

Fit linear regression models using scikit-learn and statsmodels, then read coefficients, p-values and residuals to understand drivers of business outcomes.

Feature Engineering and Encoding for Business Data

Create useful predictors from raw business data using encoding, scaling, date features and interaction terms to improve model performance and interpretability.

Model Diagnostics and Regression Assumptions

Check linearity, multicollinearity, heteroscedasticity and outliers, and apply practical fixes so regression results remain reliable and defensible to stakeholders.

Logistic Regression for Churn and Conversion

Build logistic regression models to estimate the probability of customer churn or purchase, and interpret odds ratios for decision makers.

Decision Trees and Random Forests

Train tree-based models, tune depth and ensemble size, and use feature importance to explain which factors most influence customer behavior.

Handling Imbalanced Data and Thresholds

Address rare-event problems such as fraud using resampling, class weights and threshold tuning that reflect the true cost of each error.

Classification Metrics and Confusion Matrix Analysis

Evaluate models with precision, recall, F1, ROC curves and AUC, and connect each metric to profit, risk and customer experience.

Time Series Components and Exploratory Analysis

Decompose business time series into trend, seasonality and noise, and use plots and autocorrelation to understand patterns before modeling.

Moving Averages and Exponential Smoothing

Create baseline forecasts using moving averages and Holt-Winters smoothing, and learn when simple methods outperform complex models.

ARIMA and Seasonal Forecasting Models

Fit ARIMA and SARIMA models, check stationarity, choose parameters and generate forecasts with confidence intervals for planning decisions.

Forecast Validation and Accuracy Measurement

Apply rolling-origin validation and metrics such as MAPE to compare forecasting approaches and avoid misleading results from data leakage.

Model Tuning and Pipelines with scikit-learn

Use pipelines, grid search and cross-validation to build reproducible models that combine preprocessing and training in one maintainable workflow.

Integrating Predictions into Power BI and Excel

Export model outputs and connect them to Power BI dashboards and Excel reports so teams can act on predictions in daily workflows.

Model Monitoring, Drift and Maintenance

Track model performance over time, detect data drift and set retraining triggers so predictions remain accurate as business conditions change.

Ethics, Bias and Privacy in Analytics

Examine algorithmic bias, consent and data protection rules, and apply practical checks that keep analytics projects fair, lawful and trustworthy.

Capstone Kickoff: Scoping and Data Preparation

Select a business case, define objectives and stakeholders, then clean and combine datasets using SQL, Python and Excel as the project foundation.

Capstone Analysis: Exploration and Predictive Modeling

Conduct exploratory analysis, engineer features and build candidate models, documenting assumptions and choosing the best approach for your case.

Capstone Dashboard and Insight Delivery

Design an interactive Power BI dashboard that presents findings, model results and prioritized recommendations for a non-technical business audience.

Capstone Presentations and Peer Review

Present your project to mentors and peers, defend methodological choices and incorporate structured feedback to strengthen your final deliverables.