Business Intelligence with PowerBI & Tableau:Complete Course

Explore ad hoc analysis as a business intelligence process designed to answer a specific question. Compare its customizability, ease of use, and time savings with static reports for stakeholders.

Explore online analytical processing (olap) as a key business intelligence technology that enables multidimensional data analysis, fast interactive access, and predictive analysis of feasibility for analysts, managers, and executives.

Embedded business intelligence delivers real-time reporting, interactive dashboards, AI and machine learning insights, self-service access, API-driven integration, and scalable, secure analytics.

Explore collaborative business intelligence by merging BI software with social tools and Web 2.0 to enable data-driven decisions, enterprise reporting, and shared insights across stakeholders using Power BI and Tableau.

Explore the core concepts of statistics for data science and business intelligence, including population versus sample, descriptive statistics, and inferential statistics, with visuals and practical examples.

Explore data visualization with histograms, a vertical bar chart that uses uniform class intervals to display frequency across categories, while understanding class bounds and frequency interpretation.

Use scatterplots to explore two numerical variables, show density and a best-fit line, and identify positive, negative, or no relationships, with X as independent and Y as dependent.

Practice calculating standard deviation and variance for two variables—sales and profit—using the course data source, and validate your answers after the quiz.

Explore normal and standard normal distributions, learn how z-scores measure deviation from the mean, and understand standard error within the sampling distribution.

Understand estimators and estimates, and how the sample mean estimates the population mean. Learn the central limit theorem: with large samples, the distribution of sample means becomes normal and bell-shaped.

Choose t score for small samples with known or unknown population standard deviation, or z score for larger samples, using the student t distribution in data science and business intelligence.

Explore hypothesis testing by defining the null and alternative hypotheses, and using p-values, significance level, and critical regions to draw conclusions.

Explore the significance level and confidence intervals, explaining alpha values of 0.05 or 0.01, and connect to data science and business intelligence concepts.

Apply the paired samples t-test to compare dependent two means using before-and-after data. Learn how to compute differences and use Excel to assess training program or policy impact.

Decide which statistical test to perform when the population variance is unknown through a quiz, with the answer to be revealed in the next video.

learn to compute descriptive statistics in Excel using the Data Analysis Toolpak, including mean, standard deviation, median, mode, skewness, range, and count, by selecting input ranges and output locations.

Explore practical measures of central tendency in Excel, computing mean, median, mode, range, count, and sum with simple formulas using age data for beginners.

Practice calculating the mean, median, and mode in Excel using the budget amount data set, and verify your results in the next video.

Compute mean, median, and mode in Excel using simple formulas on a budget amount dataset. Explore single and multiple mode options and see how different approaches yield the same results.

Compute correlation between sales and profit using Excel's data analysis tool, setting input and output ranges with labels. Results reveal a direct positive relationship among sales, profit, discount, and quantity.

Practice a paired-sample t test on buying price versus selling price, then compare two independent samples with equal means and a 95 percent confidence interval.

Perform a z-test on the provided practice file data and verify your answer in the next video, and understand when to use the z-test versus the t-test.

Perform a z-test for two samples of means to compare sales and profit, input variances and ranges, compute z-values, assess two-tail or one-tail significance, and validate the hypothesis.

Explore Power BI basics, data modeling, query editor, DAX functions, measures, and reporting, then visualize, analyze, and share dashboards on powerbi.com.

Explore Power BI to model data, edit queries, and use DAX functions for visualization and analysis. Publish secure reports and dashboards by connecting multiple data sources.

Refresh all data sources in Power BI by clicking the refresh button, updating reports when data changes, and refreshing dashboards on Power BI.com.

Explore Tableau's features and advantages for business intelligence, learn to create charts and calculations for sales, marketing revenue, and other data, and compare Tableau with other softwares.

Explore active and inactive relationships in Power BI by creating multiple relationships between tables, where one is active and the others inactive, with cross filtration.

Explore how cardinality defines table relationships (one-to-one, one-to-many, many-to-many) and how Power BI automatically sets and adjusts it, including cross-filter direction impacts, with product, sales, and customer tables.

Build a complete Power BI data model by linking five tables with product key, customer key, and calendar date, mastering one-to-many relationships, query editor, and reporting.

Learn to create simple DAX measures in Power BI, including counting distinct customers from the order table, and explore choosing functions with easy, Excel-like guidance.

You learn to create a DAX measure that calculates the variance percentage using the divide function to guard against zero denominators, with verification in the next video.

Create an explicit measure to calculate the sum of sales in the sales table using the sales amount field.

Master sumx in Power BI to compute total profit per row by multiplying units sold with revenue per cookie and subtracting cost per cookie in the cookie types table.

Explains how to define quick measures in Power BI, select base values from the orders table, choose aggregations like sum, average, min, max, and compute revenue minus cost.

Learn to add a year field to sales data in the calendar tab and display individual year values instead of a summed total.

Participate in a quiz to add a new column named total sales in the sales table, summing sales amount and tax amount, then verify in the next video.

Learn how to add a new column in the sales table that sums sales amount and tax amount, and format the total sales amount with zero decimal places.

Explore time intelligence in Power BI by building sales year-to-date and quarter-to-date measures with calculate and sum, using a date table and calendar hierarchy.

Explore the Tableau website to learn what Tableau is, its data culture, and community. Compare Tableau Desktop, Tableau Server, and Tableau Online plans and pricing, and try Tableau for free.

Connect Tableau to an Excel file, import orders, returns, and people, define table relationships, add fields, and understand cardinality.

Explore creating a sales bin chart with a Gantt view by date, using drag-and-drop data fields and two measures (sales and profit), plus aggregation and granulation.

Create a calculated field to compute profit margin by dividing profit by sales in Tableau, and identify calculated fields by the equal sign while reviewing the data source.

Explore how to create a calculated field in Tableau using an if condition to classify profit margin as profit or loss, verify the calculation, and visualize outcomes for business intelligence.

Discover how Tableau uses color to distinguish discrete and continuous data, with blue for discrete and green for continuous, and how the software auto-identifies data types for visualization.

Learn how to join tables in Tableau, including inner joins, and compare joins with relationships to decide when to use joins or relationships in data modeling.

Link three tables to demonstrate aggregation and granularity, revealing sales and profit by region, category, and size. Apply filters and marks to switch from totals to detailed, granular insights.

Explore parameter-driven visuals in Tableau by using a single parameter to drive category and region displays. Learn how parameters, filters, and charts shape data visualization and prepare for a project.

Understand the differences between data joining and blending. Joining uses the same source with left, inner, or outer joins; blending uses different sources at different granularities via separate queries.

Learn how to pivot data in Power BI or Tableau by turning column values into rows using the pivot function, enabling row-based views of monthly sales data.

Analyze the provided sales data and represent it as a line graph over a chosen time period, then interpret the trend to assess performance and forecast future sales.

Create a line graph of sales over time using order date, at year, quarter, and month levels, to reveal an overall increasing trend and forecast future sales.

Learn to clean messy data with Tableau's data interpreter, removing null values and repeatable values from an Excel file to prepare data for analysis; review and undo changes if needed.

Create a four-sheet dashboard from sheet data, adjust size and layout, and set filters to synchronize charts for interactive insights.

Create a descriptive tableau story by combining sheets, adding entry points and titles, and presenting dashboards and graphs like scatterplots and time series to convey data insights.

Explore why Tableau outshines Excel with drag-and-drop data modeling, multi-source data integration, and easy, code-free manipulation for efficient business intelligence and visualization.

Construct a revenue per region visualization in Tableau by creating a donut chart with dual axes and a zero field to merge charts, displaying percent of total labels.

Create calculated fields for male and female revenue. Build a category-based butterfly chart with a zero axis to compare gender revenue and customize axes and colors for the dashboard.

Create a comprehensive dashboard in Tableau to visualize revenue by region, month, state, gender-wise categories, and age, while adjusting layout, filters, colors, and sheet arrangement.

Differentiate data warehouse and data mart: a data warehouse links multiple databases for cross-source analysis, while a data mart serves a specific user community with easier decisions.

The universe acts as a semantic layer between the user interface and the data warehouse, defining table relationships and enabling BI reports to access all relevant data.

Learn to articulate BI project experience with PowerBI and Tableau, covering data collection, analysis, visualization, business strategy, risk mitigation, and agile BI practices.

Clarify BRD and SRS roles in defining software requirements and contracts. Explain how BRD is business-led and SRS covers functional, nonfunctional, scope, and feasibility through requirement engineering.

Explore open-ended questions for BI analysts and interview strategies. Learn data collection methods, qualitative and quantitative research, BI tools, and risk management.

Compare annual salaries for business analyst and related data roles across regions, highlighting entry to expert level ranges, regional differences, and salary negotiation tips for BI careers.

Celebrate completing this well-structured course and apply AI, machine learning, statistics, and data science skills to real-world workplace challenges with Power BI and Tableau.