Python & ChatGPT for A-Z Data Science and Machine Learning

Open your first analysis environment in Jupyter Notebook, write hello world with the print function, and run the cell with Shift+Enter to view the output.

Master Python variables by assigning values with the equals sign and using descriptive names, following rules such as not starting with a number, underscores for spaces, and case sensitivity.

Explore Python data types—integers, floats, strings or objects, and booleans—and learn how to check types with type(), understand when numbers are quoted, and use true/false booleans.

Learn Python data type conversion using int, float, and str to cast values for data analysis and correct data types in data frames.

Explore Python arithmetic operators, including plus, minus, multiplication, division, modulus, and exponentiation, and see how to perform simple and complex numeric calculations.

Explore Python comparison operators, including greater than, less than, greater than or equal, less than or equal, and not equal, and learn how they produce boolean true or false results.

Learn to use python's logical operators and, or, and not to combine comparisons into boolean results, with practical examples like 45>35 and 35<45.

Create and manipulate Python lists, use indexing and slicing to access data, and apply append, insert, remove, and replacement to manage fruits, ages, and mixed data.

Explore Python sets and operations, including creation, union and intersection to find common values, differences, and modifying sets with add and remove, plus iterating to scale elements.

Discover how Python dictionaries store data as key-value pairs, differentiate them from sets, and use methods like keys, values, and items to access, modify, and extend dictionaries.

Explore Python conditional statements using if, elif, and else to evaluate conditions, print results, and handle true or false scenarios in data analysis.

Apply nested logical expressions in Python to conditional statements, classifying ages as young, adult, or senior, and using not equal to evaluate graduation status.

Explore looping structures with for and while loops to iterate over lists, print items, build new lists of even numbers using modulus and append, and stop the iteration with break.

define, create, and call a custom Python function using the def keyword to calculate percentages from total respondents with total and category parameters, illustrated by male and female examples.

Data science turns raw data into insights using statistics, mathematics, and machine learning through data collection, cleaning, exploration, modeling, evaluation, and deployment with Python and R.

Compare data science and data analysis by defining each and contrasting scope, goals, and tools from Excel and SQL to Python, R, Hadoop, and Spark.

Explore the complete data science workflow from data cleaning and manipulation to EDA, pre-processing, model development, evaluation, and deployment for actionable insights.

Load and clean a dataset in a Jupyter notebook using pandas, read an Excel file with read_excel, and display the first five rows to begin data analysis.

Learn to identify and impute missing values in a dataframe using Python, pandas, and SimpleImputer from scikit-learn, with median for numeric data and most frequent for categorical data.

Identify and correct inconsistent data types in a data frame by verifying numeric columns, detecting non-numeric values, and converting costs to float with Python and pandas.

Identify and assign correct data types while cleaning data in pandas: remove rows with inconsistent values, then cast cost to float and date to datetime64, validating with dtype checks.

Learn how to remove duplicate rows from a dataset using Python, including checking duplicates with df.duplicated and removing them with drop_duplicates.

Master data manipulation by sorting a dataset by a numeric value in ascending or descending order using Python and pandas, with ChatGPT generated code to reveal lowest and highest values.

Learn to filter a dataset using Python conditional expressions (and, or, not) to extract France records or the games category, and to remove France, with practical examples and ChatGPT-assisted code.

Learn to merge datasets by adding an extra variable, such as refund, loading the data correctly, and merging on the common order id to produce a combined dataset.

Concatenate data vertically using pandas pd.concat on masked_df and extra_data to create final_df with about ten extra rows.

Explore exploratory data analysis to inspect a sales data set from a superstore, derive customer insights, profitability metrics, and market targets to guide future predictions.

Learn frequency analysis with Python to identify the primary country of residence and top product categories. Visualize results with bar and pie charts and uncover top customers and sales managers.

Master descriptive statistics for numeric variables with mean, median, mode, percentiles, standard deviations, and variance, and visualize distributions and box plots in Python using describe and seaborn.

Apply the group by method to mix categorical and numeric data, identifying top product categories by median order value and median cost, and explore profitability and refunds insights.

Master pivot table analysis with Python and ChatGPT to identify loyal customers through repeat purchases, median sales, and median cost, and highlight top ten by purchase frequency.

Explore cross-tabulation to measure relationships between two categorical variables using Python and pandas crosstab, and visualize results with seaborn heatmaps to compare sales managers and product categories.

Explore correlation analysis on three numeric variables: order value, cost, and refund; using Python to compute a correlation matrix, visualize it with a heatmap, and identify strong relationships.

Explore hypothesis testing within inferential data analysis, distinguishing population and sample, formulating null and alternative hypotheses, and interpreting results to guide predictive modeling and data-driven decisions.

Explore confidence level, significance level, and p-value to guide hypothesis testing decisions, including interpreting intervals, choosing thresholds, and deciding to reject or fail to reject the null.

Assess hypothesis testing and data analysis by comparing a new teaching method to traditional exam scores, covering null hypotheses, a 0.05 significance level, and one-way ANOVA, correlation, and regression.

Learn to check if numeric variables follow a normal distribution using seaborn kde plots and apply transformations to non-normal data from order value, costs of goods sold, and refund.

Apply square root transformation to numeric variables with numpy to reduce skew and move toward normal distributions, demonstrated on order value and cost; refund resists this method.

Apply logarithmic transformation to three numeric variables and compare its effect on data distribution against the square root transformation, highlighting order value euro, cost, and refund, and preview box-cox next.

Apply the Box-cox transformation to order_value, cost, and refund in Python using scipy.stats.boxcox. The lesson compares it with sqrt and log transforms and previews the Johnson method next.

Apply the Yeo-Johnson data transformation method to numeric variables in Python, compare Johnson and Box-Cox approaches, and decide: Box-Cox for order value euro, Johnson for cost, and none for refund.

Apply one-way ANOVA to compare cost of goods sold across PC, mobile, and tablet devices, using alpha 0.05 to judge the p-value and F-statistic and reject or fail to reject the null.

Learn to perform a Pearson correlation test in Python to assess statistically significant relationships between order value, cost, and refund, including computing correlation coefficients and p-values.

Perform regression analysis to assess the impact of order value and cost on the refund, test at 0.05, and conclude order value significantly influences the refund, while cost does not.