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

Explore Python strings and how to create text with quotes. Learn indexing and slicing to access characters and perform common operations like concatenation, repetition, lower/upper, split, and join.

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.

Discover and apply Python string methods, including lower, upper, and strip, to manipulate text and remove whitespace, unlocking effective text processing for data science.

Explore Python string methods, including split by a delimiter or whitespace and replace to swap substrings. Mastering these techniques unlocks powerful text processing in Python.

Learn to format strings in Python using placeholders and the format method, then use f-strings for concise interpolation in Python 3.6 and above.

Master Python lists and core sequence types through indexing, slicing, and common operations like append, insert, del, and pop. Understand mutability, zero-based indexing, and converting lists to tuples or sets.

Examine Python tuples, an immutable, zero-indexed data structure. Learn indexing, slicing, and iteration, convert to lists, and use as dictionary keys for heterogeneous data.

Master python sets by creating them with curly braces or set(), then perform add, remove, union, intersection, difference, and symmetric difference operations, observing automatic duplicate elimination.

Explore Python dictionaries, an unordered key-value data structure, learn to access, modify, add, and remove items, and use methods like get, items, keys, values, update, and clear.

Master Python control flow with if, elif, and else statements, using logical operators to test conditions and drive code execution, including voting eligibility and age-based categories.

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

Master advanced for loops in Python, including range with start-stop-step, tuple and dictionary unpacking in the loop header, and use break, continue, enumerate, and zip.

Explore Python loops, from for loops to while loops, with list and dictionary comprehensions, recursion, break and continue, and patterns to avoid infinite loops.

Explore advanced while loop techniques in Python, including break, continue, counter and event loops, and compare with for loops, with best practices for efficiency and readability.

Explore advanced while loop techniques in Python, including pre-setting variables, descriptive names, and accumulation patterns; prevent infinite loops with breaks and compare while to for when iteration counts are uncertain.

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.

Explore Python functions by mastering positional and keyword arguments, and applying default parameters to handle omitted inputs.

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.

Explore lambda functions, anonymous one-line functions in Python, and their syntax for small, one-time tasks. Use lambda with map to square numbers and with other higher order functions.

Learn how function caching and memoization store and reuse results to boost performance in computational tasks. The lecture shows Python examples using functools decorators to cache fibonacci and factorial computations.

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.

import Python libraries and custom modules, define an even or odd check, and perform file operations using open, read, readlines, with open, and os.remove.

Learn object oriented programming in Python by creating classes with header statements and class bodies, defining a def statement, and using self to access class and instance attributes.

Learn object oriented programming by implementing a constructor with __init__ to initialize user data. Create a rectangle class with an area method to compute areas and average age.

Learn how to implement dunder methods, including __init__, __str__, and __len__, to build a data user class, print instances, and measure their length.

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.

Explore multiple inheritance by inheriting from more than one class, combining fruit and fruit name into citrus fruit, using explicit class names in __init__ to print season, color, and name.

Learn how overriding works in Python by using subclass methods to override superclass methods and calling super() to access original. See init and msg methods and how overridden messages appear.

Learn how decorators create class and static methods in Python, and distinguish how each accesses class state or an instance. Use fruit class examples to illustrate readability and behavior.

Explore how the built-in property decorator converts a class attribute into a computed property so users need no code changes, and observe how fruit type returns name and color.

Explore NumPy as a fast, Python-friendly foundation that turns data into numbers for machine learning and modeling, enabling vectorization through broadcasting.

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.

Explore numpy array creation with random integers and practice indexing. Master slicing to extract the last element from each 3 by 4 by 5 by 6 array using its shape.

Learn array calculations and operators, including addition, multiplication, division, floor division, and power. Explore shape and broadcasting, with modulo, exponential, and log functions.

Discover how to apply numpy aggregation functions to arrays, including sum, mean, max, min, standard deviation, variance, and square roots with practical examples.

Learn to reshape arrays and use transpose to align shapes, enabling safe multiplication and proper broadcasting to avoid value errors.

Explore how to compare NumPy arrays with greater than, less than, equal, and not equal operators, ensure matching shapes, and compute the dot product using array dot operations.

learn to process images with numpy by converting them to arrays and reading them into a 360 by 360 by three rgb array using matplotlib's read function.

Install JupyterLab and pandas using pip in the command prompt, then launch the JupyterLab server. Open a Python 3 notebook and start coding in the first cell.

Download postgresql for windows, run the graphical installer with pgadmin included, set a superuser password, choose port 54322, save the installer, and skip stack builder before finishing.

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.

Learn to restore a PostgreSQL database using a graphical user interface, restoring the Airlines database from a SQL file and verifying eight tables after completion.

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 load a PostgreSQL database with Python, install psycopg2, connect to the database, run selects, and fetch results using fetchmany and fetchall.

Use Python pandas to query the airlines database after loading it, employing pd.read_sql_query to fetch aircrafts, the seats and boarding base join, and a natural join that returns 1000 rows.

Master pandas methods and functions to inspect and slice data frames, including df.columns, selecting multiple columns, tail, iloc, rank, and info, then explore the pandas docs with a web browser.

Visualize csv data using pandas and matplotlib by loading the file, inspecting shape and size, and plotting samples to reveal August as the peak month.

Learn to load csv data with pandas read_csv, inspect data types with dtypes, and analyze numeric columns with describe, focusing on sales to obtain its mean (106,000) and median (75,200).

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.

Visualize scraped web data with pandas and matplotlib, read HTML into a data frame, and plot a pie chart of unique visitors by site; Google, Facebook, and YouTube lead.

Learn how to save scraped website data to a PostgreSQL database using Python, pandas, psycopg2, and SQLAlchemy, including cleaning columns, creating the database, and querying the table.

Learn to send emails with Python by using the built-in datetime module and the SMTP module, including installation steps via the command line and testing in the Sublime Text editor.

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.

Build and run a Python program that reads an Excel email list with pandas, connects to Gmail via smtplib with TLS, and sends personalized campaign emails.

Explore how to access and manipulate financial data in Python using data frames, with column names and indexes, including iloc and loc, and apply conditional and multi-level indexing.

Learn to aggregate and summarize financial data in Python using pandas and matplotlib, computing mean and median prices and grouping by stock with standard deviation.

Develop data science skills by extending and manipulating financial data with pandas: create new columns, drop unnecessary ones, categorize prices, apply functions, and perform conditional data transformations.

Explore essential data exploration techniques in Python using pandas to peek at data with head and tail, and describe statistics for financial datasets.

Master Python data filtering with pandas to extract targeted stocks like Apple, Microsoft, and Tesla using numeric and date conditions, boolean indexing, and multi-criteria inclusion or exclusion.

Learn to create informative financial data visualizations in Python using Matplotlib, including line plots, scatter plots, axis labels, titles, and legends.

Explore histograms to visualize the distribution of financial data and compare normal versus non-normal shapes. Learn to use legends and side-by-side plots and apply Shapiro-Wilk tests for normality.

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 stock price time-series analysis using lags, calculate price changes and multivariate returns, and plot five-day returns to reveal trends in the financial data.

Explore why data drives decision making, learn to turn raw data into meaningful insights using statistics (means, medians, outliers, normal distributions) through visualization and storytelling.

Explore how data tells stories through charts, graphs, and tables, revealing trends, change over time, and patterns to empower informed, data-driven decisions.

Explore data visualization as a powerful statistics tool that presents data attractively and interactively, with benefits like enhanced communication and better decision making, using bar charts and scatter plots.

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.

Explore how line graphs show change over time and how they contrast with bar graphs. Learn to visualize accumulated totals and trends in savings, sales, and production.

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.

Learn how histograms reveal distributions of continuous numerical data using bins and density, compare datasets, and overlay a Gaussian kernel density estimate for a smooth distribution curve.

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 shapes of data through key distributions: normal distribution, binomial distribution, Poisson distribution, and exponential distribution, learning to predict outcomes and uncover stories hidden within the data.

Explore central tendency and data spread to reveal the core of a dataset using mean, median, and mode, and measure spread with range, variance, and standard deviation.

Explore how data spread differs from central tendency by measuring range and interquartile range (IQR), using quartiles to quantify variability and robustly compare distributions.

Explore measuring relationships between variables with correlation, including the Pearson correlation coefficient and matrices, and visualize findings using scatter plots and heatmaps, noting that correlation does not imply causation.

Compute and interpret covariance between two variables, including its positive, negative, and zero values, and compare it with correlation, using a Python example and a covariance matrix.

Explore statsmodels, a Python library for estimating statistical models, hypothesis tests, descriptive statistics, and time series analysis, including ARIMA, linear and generalized linear models, and econometrics.

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.

Formulate null and alternative hypotheses, set alpha, choose a test (t, z, chi-square or F), compute the statistic and p-value, then decide if evidence supports a difference.

Explore t tests, including one-sample and two-sample (Welch) tests, to compare means with small samples or unknown variances. Learn hypotheses, assumptions, test statistics, p value, and confidence intervals.

Explore the paired t test for dependent samples, comparing before and after measurements, and calculating the t statistic and p value to determine if a treatment effect exists.

Explore the chi-square test for independence to determine if two categorical variables, such as gender and smoking status, are associated using contingency tables, hypotheses, p-values, and degrees of freedom.

Apply ordinary least squares to simple linear regression, interpret beta coefficients, p-values, and r-squared, and assess residuals to validate linearity, homoscedasticity, normality, and model fit.

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.

Explore how multicollinearity inflates variances and standard errors in multiple regression, detected via correlation matrix and variance inflation factor, and mitigated by dropping features, principal component analysis, ridge, or Lasso.

Introduce exploratory data analysis (EDA) and explore the Google Play Store Apps dataset using Python in Google Colab, covering data collection, cleaning and processing, exploration, and statistical analysis.

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.

Explore data visualization techniques for exploratory data analysis using the Kaggle Google Play Store dataset, and learn to craft bar, scatter, box plots with matplotlib and seaborn.

Learn to apply statistical analysis and hypothesis testing to the Google Play Store apps dataset, using descriptive statistics, correlation, covariance, and a t test to compare free and paid apps.

Explore data storytelling techniques using the Google Play Store dataset, crafting clear objectives, engaging narratives, and visualizations to reveal insights and guide decisions.

Document and share your exploratory data analysis of the Kaggle Google Play Store Apps dataset. Highlight cleaning, visualizations, statistical analysis, and reproducibility best practices.