Data Science Bootcamp with Power Bi and Python

Learn to create a bar chart in Power BI by importing an Excel dataset, transforming columns, and visualizing price changes for products across annual, quarterly, and monthly time frames.

Create and customize a line chart in Power BI by selecting numeric fields, adjusting data labels, line styles (solid, dashed, dotted), and step options, while comparing with other visuals.

Create and customize a Power BI pie chart from a dataset, adding product category as legend and order quantity as values, then tailor labels, colors, and formatting.

Learn to create and synchronize a ring chart with a pie chart in Power BI, using category and subcategory data to build a dashboard with drill-down and filters.

Learn to create a treemap chart in Power BI, linking multiple visuals from a single dataset to reveal category and subcategory insights across countries, with formatting and interactivity.

Explore Power BI tables and mattresses, adding columns, measures, and dimensions to reveal multi-dimensional data with drill-down and formatting options.

Learn how to implement drill down in table and matrix visuals to explore hierarchical data, updating tables and charts when selecting country or product category.

Learn to install Python packages for data science in Power BI, configure the Python home, install pandas and seaborn with pip, and refresh Power BI to build Python visualizations.

Create a simple line chart in Power BI using Python code. Import data from Excel and customize axes and visuals for clear insights.

Master creating versatile visuals by labeling axes, converting line charts to scatterplots and dashed lines, and mixing formats with color and style customization in Power BI and Python.

Create a violin chart in Python using seaborn to visualize the distribution of average time spent by continent, with continent on the x-axis and average time on the y-axis.

Explore violin and rolling chart in Python to analyze site visitors by continent, revealing distribution and concentration across categories, with color palette customization and troubleshooting common syntax issues.

Learn to create a strip plot in Power BI with Python and pandas, plotting average time spent vs site visitors using matplotlib, with customizable marker shapes and colors.

Learn to customize a strip plot by setting the dataset, palette, marker shapes, sizes, colors, and transparency, with debugging tips and guidance for interpreting patterns visually.

Create and use a slicer in Power BI to filter data by country and state with checkboxes, isolate results, and drive multiple charts and dashboards.

Create and customize a date slicer in Power BI, drop order date to reveal month and date fields, and use range and hierarchical filters to drive real-time visuals.

Learn to create a gauge chart by adding a measure as a calculated field, set a target line, and visualize profit across categories with maps and tree maps.

Learn to pull live web data with Power BI by extracting tables from Wikipedia pages, then clean, transform, and rename columns in the query editor to create visualizations.

Extract data from any web page with Power BI, clean and transform it, and create bar, ring, and line charts to analyze European Union population and social expenditure.

Learn to import data from Excel into Power BI, perform Power Query transformations such as row deletions and column split by delimiter, manage applied steps, and build dashboards.

Learn to replace column values with Power Query by importing Excel data and transforming datasets. Optimize memory and automate edits using replace values, splits, and joins.

Learn how to merge two columns in Power BI, choose a separator, create a new combined column, and apply split or replace operations to shape datasets.

Learn to add prefix and suffix to column data in Power BI using the query editor's format options, including currency symbols, and apply suffixes like doctor or mister where needed.

Learn to transform a column in Power BI with the extract function, creating new columns by delimiters, ranges, and character positions for dates, domains, and names.

Add an index column with custom start values and a conditional column in the power query editor to label rows and classify products. Apply simple if-else logic, including date-based conditions.

Explore date functions in Power Query to create multiple date element columns, such as year, quarter, month, week, and day of week, for easier visualizations.