Master Business Analysis in 21 Days: Excel, PowerBI & Python

Master the project charter as the anchor for business analysis, detailing direction, boundaries, expectations, stakeholders, budget, risks, and success criteria before requirements, and keep it a living document.

Map your end-to-end process with Sipoc, detailing suppliers, inputs, process steps, outputs, and customers to reveal stakeholder needs and dual supplier-customer roles.

Master the full elicitation toolkit to uncover true requirements, using interviews, workshops, walk-throughs, brainstorming, document analysis, surveys, prototypes, and reframing to turn assumptions into actionable requirements.

Master visual modeling techniques to clarify processes with process flow, swimlane diagrams, use case diagrams, and data flow diagrams, revealing delays, responsibilities, and system boundaries for clearer analysis.

Apply the Moscow method to lead management software, prioritizing must have, should have, could have, and won't have to guide real time tracking, scoring, and dashboards.

Identify and analyze risks early, prioritize the few that can derail the project, plan responses, and monitor continuously to prevent surprises.

Apply four agile concepts to business analysis for any project. Use sipoc to identify stakeholders, build personas, craft user stories, and define acceptance criteria.

Master business analysis by transforming raw data into actionable insights through augmented analytics, predictive and prescriptive analytics, and self-service BI with Power BI and Excel to drive decisive action.

Explore five means—simple average, CAGR, geometric mean, harmonic mean, and weighted average—plus moving averages, and learn to apply them in Excel to interpret business growth, ROI, and trends.

Compare processes not by averages alone; analyze variation using range and standard deviation to gauge reliability, and compare against the customer target to determine true performance.

Identify risk by examining data distribution rather than averages, using box plots to spot outliers and histograms to reveal skew and overall distribution, with custom bin ranges via pivot tables.

Explore how regression reveals cause and effect in business data, using scatterplots, trendlines, r squared, correlation, and multiple linear regression in Excel to predict sales from leads and other factors.

Learn how business analysts use sampling to gain accurate insights with less data. Use random and representative sampling to reduce bias and reflect the population, enabling practical decision making.

Discover how aging analysis exposes time-based risk by tracking pending items across invoices, tickets, and inventory, and use IFS and pivot tables to prioritize delays.

Compare what you planned with what happened to reveal variance, assess forecast accuracy, learn, and drive timely actions across finance, sales, operations, and HR.

Explore how what-if analysis in Excel transforms spreadsheets into a decision-making engine using data tables, scenario manager, and goal seek to test targets and costs.

Spot anomalies and extract themes from business data using Power BI features like anomaly detection and word clouds to uncover insights from unstructured feedback and monthly trends.

Forecast future values with Excel's exponential smoothing to capture level, trend, and seasonality, guiding budgeting, staffing, and planning, and proactive decision making.

Explore how to build a cause and effect (fishbone) diagram to identify root causes via collaborative brainstorming, categorize causes under methods, machines, materials, people, environment, and measurement, and apply reframing.

Apply the five whys analysis to identify root causes, not just symptoms, and design sustainable, preventive solutions that address the underlying issues behind business problems.

Identify the vital few causes using the 80/20 Pareto principle, build a descending bar and cumulative line chart in Excel, and apply five why analysis to uncover root causes.

Explore the possibilities of pandas for cleaning, transforming, and analyzing structured data in Python, including missing values, indexing, grouping, time features, and Excel integration.

Analyze turnaround time data in Python by loading Excel into pandas, computing descriptive statistics, and visualizing distributions with matplotlib; spot outliers with IQR and detect anomalies with Prophet.

Analyze leads versus sales with linear and polynomial regression, compare r-squared and mean absolute error, and use multiple regression to quantify each predictor's impact.

Harness qualitative data insights by turning raw text into themes with word clouds and frequency charts, using Python libraries like pandas, matplotlib, and stopwords filtering.

Download Power BI desktop for free (64-bit or 32-bit) and publish visuals to the service. Upgrade to Power BI Pro or Premium for advanced machine learning, including classification and regression.

Explore the Power BI interface, import data, and build reports while using tables, relationships, and filters, then extend visuals and sign in with a personal email to desktop and service.

Import the Excel sales data into Power BI, then load and transform it for analysis. Explore the visuals area, fields pane, and modeling tab to build dashboards.

Transform and clean data in power query editor by converting data types, renaming columns, removing nulls, and deleting or merging columns to prepare for visualization and future machine learning models.

Load data and start building visuals using fields and visualizations, with filters introduced in a subsequent session, then create a banner with a text box and formatted colors.

Learn to create a card visualization to display total profit, total sales, and total discounts, and customize formatting, borders, titles, colors, and data label units.

Create a stat column chart showing sales growth across product lines and deals, set decimals to one, use year from date hierarchy, and format data labels and borders for clarity.

Create a multi-row card to display the top three products by profit using top-N filters, format titles and visuals, and build a shareable, auto-updating dashboard with multiple graphs.

Create a 3D tree map to compare units sold by product, format borders and data labels, and customize title and category fonts to build a dashboard in under an hour.

Create a donor pie chart on a new page to show product profit contributions, then customize the legend, data labels, title, and colors.

Build a dual axis line and stacked column chart to compare profit by segment and units sold, with adjustable axis, colors, and title formatting.

Learn to create a ribbon chart to compare performance and ranking across countries and quarters, with customizable tooltips showing country, quarter, unit sold, and profits, including trend hints.

Learn to build a bar chart of profit by product and filter by country with a slicer, configuring multi-select or single-select options (select all, radio button, checkbox) to refine analysis.

Create a funnel chart to compare country-level sales and profit, highlighting the maximum performer and the best vs least performers, with color and font-size customization.

Explore DAX, data analysis expressions, in Power BI and Excel-like formulas to create measures and columns, enabling sum of sales and month extraction from dates.

Publish your Power BI dashboard to the web, sign in, choose a destination, and share or export with options like PDF, PowerPoint, or Excel analysis.