Power BI & Excel Complete Bootcamp for Business Intelligence

Explore the basics of statistical data analysis, including how to examine, clean, transform, and model data to uncover insights and inform decision making using R, Python, or SPSS.

Compare statistical tests and exploratory data analysis (EDA) to understand their distinct roles, with EDA exploring data visually for patterns and anomalies, and statistical tests providing hypothesis-driven inferences.

Explores population vs sample and compares sampling methods like simple random, stratified, systematic, cluster, convenience, and snowball, highlighting advantages, challenges, and when to use them.

Explore the two main statistics types—descriptive analysis, which summarizes data with metrics like mean, median, mode, and standard deviation, charts, and inferential analysis, which uses samples to infer population trends.

Recap the measures of descriptive statistics such as mean, median, mode, range, variance, standard deviation, IQR, skewness, kurtosis, and quartiles, including 25th, 50th, and 75th percentiles.

Explore inferential statistics through one sample t test, independent sample t test, paired sample t test, and one way ANOVA, with examples comparing means across populations or groups.

Examine chi-square tests for independence to reveal relationships between categorical variables, and quantify linear relationships with the Pearson correlation between continuous data, illustrated by gender and product preference.

Explore simple and multiple linear regression to model and predict a dependent variable from one or more predictors, and interpret beta values and R square.

Learn how hypothesis testing uses sample data to infer population parameters and decide between null and alternative hypotheses, using significance levels and p values.

Choose the right statistical test for a scenario by aligning hypotheses with data conditions. Then perform assumption testing for normality, linearity, and homoscedasticity to validate the test.

Explore confidence level, significance level (alpha), and p-value in hypothesis testing, including how they guide decision making with null and alternative hypotheses, for business intelligence analysis.

Learn how to decide and conclude findings in hypothesis testing by comparing the p value to a 5% significance level and stating whether to accept the null or alternative hypothesis.

Compare class A new-method and class B traditional-method scores via a hypothesis testing workflow. Apply independent sample t test, Shapiro-Wilk, and p-value at alpha 0.05 to decide H0 or H1.

Visualizing data translates numbers into charts and maps, revealing trends, patterns, and outliers to support faster, data-driven decisions. Dashboards communicate insights clearly, enabling strategic planning and targeted actions.

Explore data visualization methods such as bar charts, stacked bar charts, and line graphs to compare category values, show mean values, and illustrate trends over time.

Explore how to visualize data using pie charts, histograms, scatter plots, and heatmaps to reveal distributions, correlations, and relative proportions.

Learn to import tabular data into Power BI from CSV files using get data from other sources, configure the delimiter, and use Power Query Editor to transform data before loading.

Rename data tables and columns in the Power Query editor, explore applied steps, and learn to name variables meaningfully in Power BI without coding.

Learn to set correct data types in Power BI with the Power Query editor, applying whole numbers or decimals to columns like customer ID.

Split an address column into city and state using a comma delimiter in Power Query Editor, then remove the original address column to streamline the data table.

Learn how to replace values in a column using Power Query Editor in Power BI, replacing 'tax' with 'Texas' to correct data and standardize state names.

Discover text data manipulation in Power BI by splitting the address column into city and state with delimiter, formatting text case, and merging columns into a new address.

Learn to use Power BI's numerical tools for statistical analysis and calculations on the transaction amount data, including total, average, and count distinct, while transforming data with Power Query.

Learn to manipulate a date and time column in Power BI using the date option to extract year, month, quarter, and day, and create transaction month and year for insights.

Learn to create a new inventory status column in Power BI using conditional columns in Power Query Editor, labeling as alert when stock quantity is below 20 and normal otherwise.

Perform group by analysis in Power BI to compute average prices by product category using the Power Query editor, with basic grouping and insights into category-level pricing.

Merge Power BI queries using the customer ID as the common key, performing an inner join to combine demographics with transaction history and reveal spending patterns.

Learn to append queries in Power BI by concatenating two product catalog tables, creating a new data frame with 100 products, and aligning columns for a complete product catalog data.

Learn data modeling in Power BI by creating relationships between datasets to build interactive dashboards, and connect product catalog with transaction history via product id to analyze totals by category.

Learn to create data model relationships in Power BI by manually linking tables, managing relationship settings, and auto detect, connecting product catalog, transaction history, and customer demographics.

Learn to manage and edit data model relationships in Power BI by deleting and rebuilding connections, using primary and foreign keys, and updating relationships via the manage relationships option.

Power BI shows how to format a data column for clear analysis, including currency, data type, decimal points, and data category for map visuals.

Create a chart showing total transaction amounts by state and drill down to cities using a state hierarchy to compare spending across locations.

Learn how to use data analysis expressions (DAX) to create measures and calculate total purchase amount, total quantity sold, and total sales across states in Power BI.

Create new columns with DAX in Power BI, using if and switch to determine order type and transaction quarter. Explore transaction month and year to enable quarterly analysis.

Leverage DAX and M code to count customers by gender and generate related measures; calculate the number and percentage of customers across male, female, and non-binary groups in Power BI.

Learn to build a Power BI dashboard from 2024 sales data, focusing on KPIs, trends, product categories, and interactive filters by quarter, city, and state.

Create the first section of the business performance dashboard by building KPI goal cards for total sales, quantity sold, orders, and customers on a blank canvas.

Create a Power BI line chart to show sales trends by date using 2024 data. Explore the date hierarchy from year to day and add forecasting and a trend line.

Create area charts in Power BI to show trends for total quantity sold and total orders by month, using date data and order ID as the y axis.

Learn how to create a gauge chart to visualize total sales, quantity sold, orders, and customers for 2024 versus 2023 targets, using a speedometer KPI dashboard with target levels.

Decorate the business kpi analysis dashboard by styling the title, applying fonts and colors, adding visual borders, and aligning charts to clearly compare actuals against goals across product categories.