ChatGPT Data Analyst: Fast-track Data Analysis with Open AI

Learn how data analysis inspects, cleans, transforms, and models data to uncover insights and support decision making, covering descriptive, diagnostic, predictive, and prescriptive analytics, starting with data collection.

Clean data by addressing missing values and outliers, then manipulate and explore it through exploratory data analysis and distribution analysis, leading to statistical analysis and hypothesis testing.

Explore inferential data analysis and hypothesis testing, including null and alternative hypotheses, to infer population traits from a sample through data collection, organization, analysis, interpretation, and reporting.

Understand confidence level, significance level, and p-value in hypothesis testing and how these measures guide decisions between the null and alternative hypotheses.

Follow the hypothesis testing workflow: formulate null and alternative hypotheses, set a 0.05 significance level, assess normality, and apply ANOVA, correlation, or regression to compare teaching methods on exam scores.

Analyze your data set with ChatGPT in a no-code workflow, producing insights and a report with writing and visualizations after cleaning inconsistencies and missing values.

Upload an Excel dataset and analyze missing values with Python and pandas, counting missing values for gender, income, and BMI (3, 3, 5) to enable imputation in the next lesson.

Use ChatGPT to impute missing values with the simple imputer, assigning most frequent for gender and mean or median for income and BMI.

Learn how to use ChatGPT to inspect data types in your dataset with pandas dtypes, identify misclassified variables like age, and prepare data for analysis by converting to numeric types.

Identify inconsistent values in the age variable by converting to numeric with pd.to_numeric and filtering nulls to reveal the culprit, such as index 78 and id 79.

Use ChatGPT data analyst to drop the inconsistent value triple x from the age variable, remove the row, and convert age to numeric in pandas.

Identify and remove duplicated rows in a dataset using ChatGPT-4 assisted Python commands. Ensure unbiased analysis and a clean dataset for further analysis.

Learn to load the final cleaned data into all_cleaned_df, sort by income in descending order with ChatGPT, and display the first five rows to identify top earners.

Learn to filter datasets with conditional prompts in chatgpt, select records older than 30 and filter by income, and preview the top results from the cleaned dataset.

Join two data sets using inner join to retain only common ids, and compare with left, right, and full joins, using pandas merge on the id key.

Use ChatGPT to perform left, right, and outer joins, showing left joins preserve left data with nulls, right joins bring in right data, and outer joins combine both.

Apply feature binding with GPT-4 to convert a numeric income variable into three categories: low, medium, and high, using pandas qcut and quantiles.

Master feature encoding by converting categorical data to numeric values with a dot map in python, mapping gender to 1 for male, 2 for female, and 0 for other.

Create dummy variables for marital_status values such as single, divorced, and widowed using pandas get_dummies with a marital_status prefix, merging as columns into the cleaned data frame to generate indicators.

Explore nominal data analysis and exploratory data analysis with GPT-4; perform frequency and percentage analyses on education levels, visualized via bar and pie charts using value counts.

Use GPT-4 data analyst to perform descriptive analysis on age, income, and BMI, generate box plots, and extract means, medians, standard deviations, and key distribution insights.

Learn to perform group by analysis in Python to compute the average income by education level, sort results, and visualize insights with a line chart using ChatGPT for data analysis.

Learn to perform a cross-tabulation between gender and education with pandas, then visualize it as a stacked bar chart and interpret the gender-based education patterns.

Learn to perform correlation analysis with ChatGPT data analyst, compute a correlation matrix for age, income, and BMI, and visualize results with a seaborn heatmap.

Explore how to perform a one-way ANOVA to test income differences across education levels using ChatGPT Data Analyst, interpret F statistics and p-values, and decide on significance.

Apply the Pearson correlation test to age and income, interpret r and p, and confirm no significant relationship (r ≈ 0.028, p ≈ 0.371); regression analysis follows.

Perform regression analysis with Python and statsmodels to test if age influences income. The age coefficient is about 32, but p-value 0.371 and R² 0.001 show no significant effect.

Derive and scale features for a machine learning model, preparing numeric and dummy variables (age, BMI, education_numeric, gender, marital status) to predict income using a standard scaler.

Split the data into train and test sets using train_test_split from scikit-learn to train a model on scaled features x and target y and evaluate performance with 25% test data.

Build and evaluate machine learning models using random forest regressor and support vector regressor, training on Xtrain/ytrain, predicting on Xtest, and assessing performance with mean squared error and R-squared.