Data Analysis Fundamentals with Python and ChatGPT

Explore how ChatGPT helps you learn Python for data analysis and generate pandas code to perform complete data analyses in Jupyter notebooks, including grouping by customer and total sales.

Explore Python's role in data analysis, highlighting its simple, readable syntax and versatile ecosystem. Learn how Pandas, NumPy, SciPy, Matplotlib, and Seaborn empower data manipulation, computation, and visualization across platforms.

Open your first analysis environment in Jupyter Notebook and learn the basics of Python by printing 'Hello world' with the print function, then run the cell with shift+enter.

Learn how to use variables in Python, assign values, follow naming conventions—valid characters, cannot start with a number, case sensitivity, and descriptive identifiers to manage data.

Learn Python data types - integers, floats, strings (objects), and booleans - how to use type checks, differentiate whole numbers from decimals, and handle quoted text in analysis.

Master type conversion in Python by casting between int, float, and str, and assign new data types for values with practical examples.

Explore Python arithmetic operators—plus, minus, multiplication, division, modulus, and exponent—through practical examples that compute sums, differences, products, quotients, remainders, and powers.

Learn Python comparison operators: greater than, less than, greater than or equal, less than or equal, equal, and not equal—and how booleans reflect true or false results and data type effects.

Explore Python lists by creating lists with square brackets, indexing from zero, and slicing ranges, then modify data with append, insert, remove, and replace operations.

Create Python sets and explore operations such as union, intersection, and difference; modify sets with add and remove; and apply a loop to multiply each value by two.

Explore Python dictionaries as key-value pairs, differentiate from sets, and use keys, values, and items to access, modify, and delete or extend dictionaries with new keys.

Explore Python boolean logic with and, or, and not by combining comparison operators to evaluate true or false conditions, using simple code examples.

Explore Python conditional statements using if, elif, and else to evaluate conditions and print outcomes based on whether conditions are true or false.

Apply logical expressions within Python conditional statements to classify age groups and weather conditions, using and or not operators to determine young, adult, senior, uncomfortable, and eligible.

Learn to use for loops and while loops to iterate over lists, print items, build an even-number list, and control flow with break and conditions.

Define, create, and call a Python function named percentage using def to compute the percentage of a category from total respondents, demonstrated with male and female examples totaling 1000 respondents.

Learn how to load CSV and Excel datasets in a Jupyter Notebook using Python and pandas, with code generated by ChatGPT, including package installation and data inspection.

Identify and assign correct data types in Python by analyzing numeric, integer, float, object, boolean, and date-time variables, addressing missing values, and validating with ChatGPT-generated code.

Identify and impute missing values in data using delete and replace strategies, including mean, median, and mode, demonstrated with Python and ChatGPT guidance.

Identify and handle outliers by recognizing extreme high and low values, using histograms and unique values to detect them, and removing or preserving them with Python and ChatGPT guidance.

Identify and remove duplicate values in a dataset to ensure unbiased analysis, using Python to drop duplicates in Jupiter notebook, reducing 379 rows with 108 duplicates to 271 rows.

Learn how to encode categorical variables by assigning numeric values, distinguish nominal and ordinal data, and apply Python mapping for Likert and other scales ahead of regression or correlation.

Learn feature binning in Python by converting numeric variables to categorical using income and age examples, and creating categories with pandas cut.

Learn to encode categorical variables with dummy and feature encoding, using gender and education as examples in Python. Apply dummy encoding for nominal variables and feature encoding for ordinal ones.

Learn how to sort and filter datasets with Python and ChatGPT, using ascending and descending orders, subsetting by conditions like gender, to reveal insights.

Learn how margin data adds extra variables to an existing dataset and merge using inner and outer joins on a common variable in Python with Pandas.

Learn to concatenate data vertically using Python and pd.concat to merge datasets with similar variables, creating a final table of augmented rows from March data and extra data in Jupyter.

Explore distribution of numeric data with histograms, identify normal versus skewed distributions, and detect outliers. Apply transformation methods—log, reciprocal, square root, Box-Cox, and Yeo-Johnson—to approximate normality.

Learn to convert object dates to datetime in Python, then extract day, month, and year into separate columns for time-based analysis using pandas.

Explore three core visualizations for categorical data: bar plots, line charts, and pie charts, using value counts to reveal category frequencies with Python and matplotlib.

Explore how to interpret a pie chart by comparing percentages and frequencies, visualizing gender shares with Python and Matplotlib.

Learn how to read and construct line charts to visualize trends with x and y axes and monthly data, using Python, group by, and dynamic line plots.

Master histograms, box plots, and scatter plots to reveal distribution and relationships in numeric data. Interpret frequencies, percentiles, and outliers with practical age and income examples.

Explore how a scatter plot reveals the relationship between two numeric variables, age and yearly income USD, by building axes, plotting points, and interpreting a possible positive link using Python.