Data Science & Machine Learning Study Bootcamp: From A to Z.

Master basic string operations in Python by concatenating with the plus operator, computing string length with the length function, and indexing with zero-based positions.

Explore how to use Python for loops to iterate over lists, strings, and more, using range, enumerate, and zip to process multiple sequences efficiently.

learn how to create and use functions in Python with def, parameters, and return, to accept input, perform operations, and produce output, with practical examples.

Master recursion in Python by learning how a function calls itself, defined by a base case and a recursive case, breaking problems into subproblems with examples like countdown and factorial.

Master error handling in Python by using try and except blocks to run code when no errors occur and gracefully handle NameError with informative messages.

Learn how inheritance enables code reuse in Python by using a superclass and subclasses, with examples like fruits and user data, and overriding.

Hide internal object details to enable easy use with access controlled by getter and setter methods. Demonstrate with a Jupyter notebook class using get and set for age.

Learn to work with numpy ndarrays, exploring shape and size across 1d, 2d, and 3d arrays, and reshape when needed. Convert arrays to pandas dataframes to analyze data.

Create numpy nd arrays using zeros, ones, and arrange; specify shapes and generate random arrays with seed for repeatable results, including randint and rand functions.

Create a new PostgreSQL database in admin four, rename it Airlines, select template zero, save, then open public schema and tables (currently empty) in preparation for restoration.

Master querying an airline database, export results to a CSV, clean departure and arrival city fields, and analyze the data in Python with pandas in Jupyter notebooks.

Learn to avoid sampling error by using larger and bigger samples from population, with Postgres SQL queries and pandas-based analysis in Jupyter Lab to compare July, August, and June sales.

Learn to scrape websites with Python using requests and BeautifulSoup, parse HTML, extract data, and clean results with pandas or Postgres while respecting robots.txt and privacy.

Install the lxml module and use pandas to scrape a web page table with read_html, then extract columns and compute mean, median, standard deviation, variance, and describe statistics.

Learn how email delivery works using SMTP, including configuring Gmail and Yahoo accounts, generating app passwords, enabling less secure app access, and sending emails with Python's smtplib via starttls.

Explore visualizing trends in real-world financial data by building a histogram of P/E ratios, identifying an outlier, and naming it, all using Python with pandas and matplotlib.

Identify when to use line, bar, and scatter plots for financial data, and learn to create these visualizations with matplotlib and seaborn to reveal trends and relationships.

Index and resample time series using real financial data, set the date as the index, and compare annual versus monthly stock price trends with mean calculations.

Explore how bar charts visualize categorical data by comparing frequencies across categories, and apply best practices like appropriate data type, clear axes, labels, and color schemes to create effective visualizations.

Use pie charts to show market share and part-to-whole relationships; learn when they excel, avoid pitfalls, and create readable, labeled slices with Python Matplotlib.

Explore how box plots reveal salary distribution and outliers using the five-number summary, including min, q1, median, q3, max, iqr, and whiskers, with a comparison to bar charts.

Explore the Galton dataset with pandas describe and statsmodels, visualize distributions with histograms and KDE, and evaluate a simple OLS model linking parent height to child height.

Learn to build and interpret a multiple linear regression model with statsmodels, using multiple predictors to predict a target, assess assumptions, and read outputs like coefficients, r-squared, and p-values.

Apply data cleaning and preprocessing techniques to the Google Play Store Apps dataset, handling missing values, duplicates, outliers, normalization and standardization, and encodings like one-hot and label encoding.