Data Analyst in Python, Tableau, SQL & ChatGPT with Projects

Explore how to view unique values in a column with the unique and nunique methods, including handling nulls with dropna options and counting distinct postcodes, countries, and product subcategories.

Create a city column in the Airbnb data and set it to New York for all rows; then drop that column using labels and axis, with inplace to update.

Use the dataframe replace method to remove percent signs, apply regex, and normalize values, with examples like caps to hats and regions to West USA.

Learn to group data by a column with groupby, compute mean, max, or min, and extend to multi-column groupings like job title and location for salary insights.

We create bar plots of average salary in USD by experience level, convert the grouped data to a data frame, sort by salary, and add axis labels plus a title.

Explore scatter plots with the world happiness dataset, plotting happiness score against healthy life expectancy, using alpha, grid, labels, and color to reveal correlations and outliers.

Resize plots with plt.figure by setting figsize and dpi to control size and clarity. Create charts of date and price, high and low, with colors and side by side layouts.

Combine four plots into a 2x2 Matplotlib figure, customize styles, and format a line chart, horizontal bar, pie chart, and bar chart for sales by month.

Create a multi-plot figure from a data frame, combining a line chart, histogram, and horizontal bar in a 2x2 layout, with labeled axes and a crime over time title.

Create and load database schemas for classic models and Netflix data using SQL scripts, drop and recreate schemas, and import CSVs to practice data manipulation.

Explore the Netflix relational schema through an entity relationship diagram, uncovering primary keys, foreign keys, and table relationships like titles, show country, cast, directors, and categories.

Apply multiple filters using in and not in to select records based on a list of values, such as release years 2020 or 2021 and date added.

Extract unique values with distinct from single or multiple columns, illustrated on Netflix titles. Order results by release year or type in ascending or descending order.

Explore inner joins by combining rows from two or more tables on a common key, returning only matching records, illustrated with Netflix titles, show director, and director ID.

Master left joins in SQL by retrieving all records from the left table with matched right-table data, handling nulls, and comparing behavior to inner joins through practical examples.

Explore how group by aggregates data by a selected column, enabling daily revenue sums and per-customer last and first orders, with counts of distinct orders.

Learn how to use subqueries in SQL to create derived tables, alias results, and compute aggregates like average payments per day, enabling nested queries and advanced data retrieval.

Segment customers by purchase value using a case statement. Compute total from orders and order details, then classify as high, medium, or low value and left join to customers table.