Mastering Data Science & AI with Python & Real-World Project

Discover Python strings, from creation and quoting to slicing and indexing. Apply common operations like concatenation, repetition, and methods such as lower, upper, split, and join.

Master string operations in Python by concatenating with the plus operator, measuring length with len, and indexing from zero to access characters.

Explore string methods in Python to manipulate and transform text, using lower, upper, and strip to handle whitespace and case efficiently.

Explore Python string methods, focusing on split and replace to manipulate text. Learn how split defaults to whitespace and how replace substitutes substrings to transform strings, with practical examples.

Learn string formatting in Python to create dynamic strings with placeholders, using the format method and f strings with name and age examples.

Master Python lists as mutable, ordered data structures, learning indexing, slicing, append, insert, del, pop, sort, reverse, and loops, plus converting lists to tuples or ranges.

Explore Python tuples, an immutable data structure similar to lists that store multiple items, with indexing, slicing, iteration, and conversion to lists or sets.

Explore Python sets, learn to create sets with braces or the set function, and master operations like add, remove or discard, union, intersection, difference, and symmetric difference.

Explore Python dictionaries, a versatile unordered data structure of key-value pairs, and master common operations and methods such as get, items, keys, values, update, clear, and iteration.

Master Python control flow with if, elif, and else to execute blocks based on conditions; apply and, or, not to combine tests, including voting eligibility and age categories.

Explore python for loops that repeat actions over sequences like lists, tuples, and strings. Use range, enumerate, and zip to iterate with positions, values, and paired lists.

Explore advanced for loops in Python, including unpacking tuples and dictionaries, looping with start, stop, and step (including backwards), and leveraging break, continue, enumerate, zip, and itertools.

Master while loops and recursion in Python, exploring their use alongside for loops. Build list and dictionary comprehensions, and apply break, max iterations, and termination conditions for safe looping.

Master advanced Python while loops, including break, continue, counter and event loops, and compare them to for loops and recursion for efficient, clear code.

Master advanced while loop techniques in Python, including setting loop variables before the loop, using descriptive names, accumulating totals, preventing infinite loops with break, and choosing while over for loops.

Master Python functions by defining them with def, using parameters to handle input, and returning values to produce outputs. Learn how these building blocks enable reusable code and organized logic.

Master function arguments in Python by exploring positional and keyword arguments, and using default parameters to control values during function calls.

Explore recursion in Python by a function calling itself, with base and recursive cases, solving problems like countdown and factorial by breaking them into simpler subproblems.

Explore Python function decorators and higher order functions to boost behavior and flexibility, including modifying output without changing source and returning or accepting functions.

Master lambda functions in Python, learning syntax and anonymous, one-line definitions, and using them with higher-order functions like map, filter, and reduce to transform data.

Explore function caching and memoization to optimize Python code, using functools cache and a memoize decorator on fibonacci and factorial examples to boost performance.

Harness Python's try and except blocks to handle errors and print messages when things go wrong. Learn to catch name errors and other exceptions to keep programs running.

Explore Python modules and libraries, import and reuse pre-written code, and manage files with open, read, write, and os.remove, including defining simple functions like even or odd.

Define classes with a header and body, use self, and implement class and instance attributes. Learn CamelCase naming and create simple user data classes in a Jupyter notebook.

Learn object oriented programming with constructors, __init__, and classes by building a user data class and a rectangle class with an area method and average area calculations.

Explore dunder methods by implementing __init__, __str__, and __len__ to print objects and control creation, string representation, and length, shown with a data user class.

Learn how inheritance in Python lets you build a general superclass and extend it with subclasses to reuse and extend code, reducing repetition. Through concrete examples like a user data class with name and age and a phone number class that inherits from it, including overriding and constructor initialization, you see practical application.

Explore encapsulation to hide internal object parts, restrict access, and expose data through getter and setter methods, demonstrated with a class example in a Jupyter notebook.

Explore multiple inheritance in Python by defining a citrus fruit class that inherits from fruit and fruit name, merges init parameters (season, color, name), and prints the combined attributes.

Explore how a subclass overrides a superclass method in Python, call the superclass via super(), and see how overriding changes messages printed by objects like communication.

Explore decorators like class method and static method to see how they access class state versus object instances, improving readability in class design.

Learn how to use the built-in property decorator in Python to implement a computed fruit_type from name and color, ensuring stable behavior as attributes change.

Install and import numpy, then explore ndarrays, their shape, ndim, and size to understand array dimensions. Convert arrays to pandas dataframes and reshape them to fit dataframe structures for analysis.

Create numpy ndarrays using zeros, ones, and shape parameters; generate sequences with arrange, and produce random arrays with randint and seed for repeatable results.

Explore practical numpy techniques, including m.dot unique and array slicing, using random arrays, indexing basics, and slicing strategies to extract elements from multi-dimensional arrays.

Explore numpy array operations by creating, comparing, and manipulating arrays with addition, multiplication, and more, including division, floor division, power, modulo, exponential, and log, while learning broadcasting and shape compatibility.

Learn to use numpy aggregation functions on arrays, including sum, mean, max, min, std, and var, and compute square roots for ndarrays to analyze data distributions.

Learn how to reshape and transpose NumPy arrays to align shapes for multiplication, using broadcasting rules to avoid value errors.

Use numpy ndarray comparison operators such as greater than, less than, equal, and not equal to compare arrays, explore shape requirements, and compute dot products with np.dot.

Install and launch JupyterLab using the command line, install pandas with pip, and open a Python 3 notebook in JupyterLab to run your first code cell.

Download and install PostgreSQL on Windows using the graphical installer, set the default port 5432, create a superuser password, and finish the setup without launching Stack Builder.

Learn to create a PostgreSQL database with PgAdmin 4, name it Airlines using template zero, access the public schema, and note no tables exist yet.

Learn to restore a Postgres database with the Admin four SQL user interface by selecting the airlines database, loading a SQL file from resources, and confirming the eight-table restore.

Learn to query an airline database, export results to a csv, clean city fields from json data, and load and inspect the data in Python using pandas for analysis.

load a Postgres database with Python using psycopg2, connect to the airlines database, and use fetchmany to retrieve the first five, twenty, and ten records, plus fetchall for all records.

Master querying airline data using Python pandas after loading with psycopg2, executing SQL queries to fetch from tickets_flights, aircrafts, seats, and boarding tables, including natural joins and 1000-row limits.

Explore how to analyze a data frame with pandas in Python by inspecting columns, selecting multiple columns, retrieving rows with iloc, and using tail, rank, and info.

Visualize csv data with pandas and matplotlib by reading a csv into a data frame, inspecting shape and size, sampling data, and plotting bar charts of monthly sales.

Explore pandas data analysis by loading a csv with read_csv, inspecting data types, and describing numeric columns like sales to compute mean and median.

Learn to avoid sampling error by using larger, multiple samples from the population and validate results with SQL queries and pandas sampling, confirming July, August, and June sales.

Learn to scrape a website in Python using requests and BeautifulSoup, fetch and parse HTML, and extract data with find, find_all, and select, while respecting robots.txt permissions.

Learn to install lxml with pip, scrape a web page table using pandas read_html, convert it to a data frame, and compute mean, median, standard deviation, and describe.

Visualize scraped data by importing pandas and matplotlib, reading HTML into a data frame, and plotting a pie chart of websites and their monthly unique visitors.

Save scraped data from a website into a PostgreSQL database using python, pandas, and psycopg2; load with to_sql and verify by querying the table.

Learn how to send emails with Python using SMTP and datetime to schedule delivery, install the smtp module with pip3, and test importing smtp in Sublime Text.

Explore how emails are sent via SMTP using Python, including setting up Gmail and Yahoo accounts, app passwords, and composing messages with recipients and subject.

Create a Python script that reads an Excel email list with pandas, connects to smtp.gmail.com via TLS, and sends Black Friday or White Friday offers.

Master data manipulation with data frames in Python using pandas, reading market history csv, cleaning and analyzing data, and visualizing stock prices over time with matplotlib.

Access data in data frames using column names and indexes, including conditional and multi-level indexing, with pandas in Python for finance insights.

Aggregate and summarize financial data in Python using pandas for mean and median prices, then apply grouped aggregation and multiple metrics (mean, median, std) with visualizations.

Learn to extend and manipulate data in Python using pandas by creating new columns, dropping columns, and applying conditional and categorization techniques on financial data.

Master essential data exploration in Python with pandas, using head and tail previews, describe for numeric and non-numeric columns, and box plots to reveal insights from financial data.

Explore filtering data in Python with pandas to extract targeted financial stocks by price, date range, and symbols, using boolean indexing and multi-condition logic.

Explore data visualization in Python with matplotlib and pyplot, learning to plot stock prices over time, label axes and titles, compare multiple lines, and create scatter plots to reveal relationships.

Learn how histograms visualize the distribution of financial data, compare normal and non-normal data with histograms and the shapiro-wilk test, and add legends for clarity using matplotlib.

Explore visualizing trends in real world financial data with python by building a histogram of p/e ratios, identifying outliers, and naming notable stocks.

Explore Python data visualization with matplotlib and seaborn to plot financial data, choose suitable plot types for trends, distributions, and correlations.

Explore why data drives modern decision making and learn practical statistics—from means and medians to outliers and normal distributions—to turn raw data into actionable insights.

See how data becomes a narrative with charts, graphs, and tables, surfacing trends, patterns, and change over time to empower informed, evidence-based decisions.

Explore data visualization as a powerful statistics tool that communicates insights, reveals patterns, and guides better decisions across business analytics, healthcare, environmental analytics, and social media analysis.

Explore bar charts as a powerful way to visualize categorical data and compare frequencies across categories, while learning best practices for clear labeling, color schemes, and axes.

Explore when to use line graphs versus bar charts, visualize change over time, and track cumulative totals with Python examples from the fictional store Trendy Items.

Learn how pie charts visualize market share and parts-to-whole relationships, covering anatomy, best uses, pitfalls, and practical Python matplotlib steps for clear visuals.

Visualize numerical data distributions with histograms, bins, and density using Python libraries like Matplotlib, NumPy, and SciPy, and compare distributions while noting skewness and outliers.

Explore box plots as a concise visualization of numerical distributions, using the five-number summary (min, Q1, median, Q3, max) to spot outliers and compare salaries across departments.

Explore how data distributions reveal patterns and guide predictions, from the normal bell curve to binomial, Poisson, and exponential models, with real-world examples.

Explore central tendency and data spread using Python and NumPy, including mean, median, and mode, plus range, variance, and standard deviation, with code examples.

Explore measures of data spread, including range and interquartile range, to quantify dispersion around the central value. Learn how quartiles define the middle 50% and variability.

Explore how to summarize data with pandas, compute descriptive statistics, and detect and remove outliers using the interquartile range (IQR) method, with visualizations from box plots and histograms.

Learn how to measure variable relationships with correlation, interpret the Pearson coefficient, and visualize results using scatter plots and heatmaps in pandas.

Compute and interpret covariance, revealing how two variables move together—positive, negative, or zero—without standardizing their scale. The lecture also shows a Python example calculating sample covariance and covariance matrices.

Explore statsmodels, a Python library for estimating statistical models, hypothesis tests, descriptive statistics, time series, linear regression and generalized linear models, and econometric tools.

Load the Galton dataset with Statsmodels, summarize heights via pandas describe, and fit an OLS model predicting child height from parent height, then visualize with histograms, KDEs, and scatter plots.

Explore hypothesis testing: formulate H0 and H1, set alpha, choose a test statistic, determine the rejection region, and use a two-sample t test to conclude with a practical teaching-method example.

Master the one-sample and two-sample t tests to compare means, test null and alternative hypotheses, interpret p-values and confidence intervals, and apply Welch's t test for unequal variances.

learn how the paired t test compares means from related samples, such as before–after measurements, by computing the mean difference, t statistic, and p value to test zero difference.

Explore the chi-square test for independence to assess relationships between two categorical variables using contingency tables, observed versus expected frequencies, degrees of freedom, p-values, and significance level.

Explore ordinary least squares linear regression, interpreting coefficients, p-values, and R-squared to understand predictions. Analyze residuals for linearity, independence, homoscedasticity, and normality, using plots and diagnostics.

Expand prediction from a single predictor to multiple predictors using statsmodels, fit an ordinary least squares model, and interpret coefficients, R-squared, and p-values.

Explore multicollinearity in multiple linear regression, detect it with correlation matrices and variance inflation factors (VIF), and mitigate by dropping features or using PCA, ridge, or Lasso.

Explore the Google Play Store Apps dataset with Python to learn exploratory data analysis (eda). Use pandas, numpy, matplotlib, and seaborn to reveal ratings, size, and price patterns.

Apply data cleaning and preprocessing techniques to the Google Play Store Apps dataset, handling missing values, duplicates, and outliers, and perform normalization and encoding for machine learning.

Explore data visualization techniques for exploratory data analysis using the Kaggle Google Play Store dataset, employing matplotlib and seaborn to create bar, scatter, box, histogram, heatmap, and pie plots.

Explore exploratory data analysis techniques, compute descriptive statistics, correlation, and covariance, and perform hypothesis testing with t tests on the Kaggle Google Play Store Apps dataset to make data-driven decisions.

Explore data storytelling techniques using the Kaggle google play store dataset, crafting clear objectives, engaging narratives, and visualizations to reveal insights, takeaways, and actionable recommendations.

Master the Kaggle Google Play Store apps dataset through end-to-end exploratory data analysis, documenting, visualizing, and sharing reproducible results with best practices.

Explore binary classification with scikit-learn by generating a dataset, splitting into training and testing sets, training a logistic regression model, and evaluating with accuracy and confusion matrix.

Explore the supervised learning workflow from data collection and preparation through feature engineering, model selection, training, and evaluation using Python and the Iris dataset.

Evaluate machine learning models using train test split and accuracy, visualize model complexity, and balance overfitting and underfitting with a practical iris dataset example.