Claude AI for Data Analysis & Business Intelligence

Define a KPI snapshot with Claude AI to generate an interactive dashboard showing total sales, total profit, total margin, and average order value.

Open VS Code, create a new ipynb file, and import pandas, seaborn, matplotlib.pyplot as plt, and numpy in a Python 3.11.5 environment to prepare for data analysis.

Load Titanic.csv with pandas read_csv, create a dataframe, and view the number of records and columns. Next, explore more data analysis tips in upcoming sessions.

Fetch columns in a pandas dataset using df.info to reveal the dataset's columns, rows, and data types.

Learn to perform descriptive analysis with pandas, using df.info and df.describe to extract summary statistics, and organize your findings with Markdown headings in VS Code.

Identify unique values across columns with a single pandas command, inspect sex and survived, and count category frequencies with value_counts for p class.

Learn to filter the specific numbers of records from a large Pandas dataset using the Ilok function to fetch a range such as 100 to 500.

Apply a logical condition in a data frame using the log function to filter records with a column value greater than or equal to two.

Learn to replace null values in a pandas DataFrame by selecting a column, applying a fill function, and updating in place, then verify removal using is null checks and sum.

Explore pandas series, a one-dimensional label array that holds integers, strings, and floats. Create a series from a Python list, import pandas as pd, and print the results.

Create your first data frame from a dictionary using pandas, defining name, age, and city columns, then print the resulting data frame to view the data.

Learn how to inspect data in detail by loading a CSV with pandas and previewing with df.head() and df.tail(). Use df.info() and df.describe() for structure and numeric summaries.

Master using loc to select single or multiple columns in a dataframe, filter rows by label, with examples like first name and last name, and view the first rows.

Learn to use the iloc function in pandas to select the first two rows and columns, and extend to more rows or columns as needed.

Import the libraries and load the Titanic dataset from a GitHub URL to begin exploratory data analysis with pandas, then inspect df.info, shape (891, 12), and data types.

Inspect data types with df.dtypes and identify missing values using isnull().sum() in pandas. Generate a numeric summary with describe() and review how to handle missing values for real-world datasets.

Identify missing values with df.isnull, fill missing ages with the median using fillna, verify no nulls remain, and prepare to visualize the Titanic dataset.

Visualize Titanic survival data using seaborn count plots in a Python notebook, exploring overall survival counts and gender-based survival distributions with hue for male and female.

Visualize the Titanic dataset age distribution with Seaborn and Matplotlib using a histogram, perform exploratory analysis on each column, and interpret the concentration around age 30.

Create a Seaborn count plot of the Titanic dataset to show survival by gender, with sex on the x axis and hue by survived (0 or 1).

Create a histogram of the age column using pandas, with 20 bins and rgb color, to visualize passenger ages on the Titanic.

Create a bar plot to visualize passenger counts by class in the Titanic dataset, with labeled axes and customizable colors to inform business decisions.

Create a scatter plot from a Titanic data frame to relate age to amount paid, with x-axis for age, y-axis for amount paid, red points, axis labels, and a title.

Create a box plot in a dataframe to explore distributions and detect outliers, interpret payment ranges, and consider removing outliers for clearer visualization.

Explore the pandas cheat sheet to learn core data manipulation with pandas, including creating dataframes, renaming and melting columns, sorting, dropping, merging, grouping, and basic Seaborn visualization.

Learn data cleaning with pandas: identify and fix errors, handle missing values, remove duplicates, convert data types, normalize, validate, and save clean data for analysis.

Learn to build a data analyst app with Google Gemini. Use AI Studio to generate code, perform data profiling, descriptive analysis, and visualize with bar, pie, and stacked charts.

Master data manipulation by exploring Kaggle public datasets, including the Google App Store and Google Play Store datasets, and performing exploratory data analysis with ipynb workflows.

Master Power Query in Excel to transform and append data from multiple sheets, create a master sheet, and merge tables with left, right, and full outer joins.

Append multiple Excel sheets into a single master sheet using Power Query to consolidate datasets from Germany, Canada, the United States, Mexico, France, and India.