Time Series Analysis:Hands-On Projects & Advanced Techniques

Discover how to work with Python strings, including creation, indexing, slicing, concatenation, repetition, and key methods like lower, upper, split, and join.

Explore basic string operations in Python, including concatenation with the plus operator, measuring length with the length function, and indexing to access characters from zero.

Explore Python string methods for lowercase, uppercase, and whitespace stripping, and inspect built-in data types and their common methods for text and numeric operations.

Explore Python string methods like split and replace to manipulate text, using delimiters, whitespace handling, and substring replacement through practical examples.

Master Python string formatting by using placeholders with format, and harness f-strings for concise interpolation, embedding variables like name and age in dynamic messages.

Explore lists as a core Python data structure and master indexing. Practice append, insert, delete, sort, reverse, and convert lists to tuples, sets, or dictionaries.

Explore Python tuples, an immutable, zero-indexed, ordered sequence that stores multiple items. Practice indexing, slicing, iteration, and converting to lists, and use tuples as dictionary keys for faster performance.

Explore how to create and manipulate Python sets, using curly braces or set(), then perform add, remove, union, intersection, difference, and symmetric difference operations with unique elements.

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

Use python control flow with if, elif, else and logical operators to direct code execution. Explore age-based conditions and eligibility checks with def statement that tests conditions.

Explore for loops in Python, using iterators, range, enumerate, and zip to iterate over lists, tuples, strings, and other sequences, and learn how to access positions and items.

Master advanced for loops in Python, including unpacking tuples and dictionaries, range controls, break/continue, for-else, safe list modification, and using enumerate, zip, and Cartesian product.

Master while loops in Python and compare with for loops, explore recursion as an alternative, and leverage list and dictionary comprehensions with break patterns.

Master while loop patterns in Python, from counter, input, and event loops to conditional loops, using break and continue effectively, and compare with for loops and recursion.

Explore advanced Python while loop techniques, including setting loop variables before the loop, using descriptive counters and accumulators, and writing safe exit conditions with break to avoid infinite loops.

Explore how Python functions work as reusable machines, defined with def, and how parameters and return statements enable input, processing, and output across code.

Master function arguments in Python by using positional and keyword arguments, and applying default parameters to supply values when omitted.

Master recursion in Python by learning how a function calls itself to solve problems by breaking them into simpler subproblems, with base and recursive cases and countdown and factorial.

Explore Python function decorators and higher order functions to enhance function behavior without altering source code, using examples like an uppercase decorator and a multiplier that returns callable functions.

Explore lambda functions in Python, and apply anonymous, one-line functions with higher order functions like map, filter, and reduce to operate on iterables.

Explore function caching and memoization to boost performance by storing computed results, using Python's functools decorators on fibonacci and factorial examples to demonstrate cached results.

Explore error handling in Python using try and except blocks to manage runtime issues, illustrate name errors, and respond with alternative actions and clear messages.

Explore how Python modules and libraries are created, saved, imported, and reused; learn file operations, including open modes, reading lines, writing, and removing files with os.

Learn to define Python classes, explain class header and body, distinguish class and instance attributes, and implement methods with self, demonstrated through simple Jupyter notebooks.

Learn how constructors initialize class instances with the __init__ method, define attributes like name and age, and use methods to compute average age and rectangle areas in a Jupyter Notebook.

Implement dunder methods in Python, including __init__, __str__, and __len__, to customize object printing. Use a data user example to show length calculations.

Learn how inheritance in Python lets a superclass share attributes with subclasses, reducing code repetition, using a user data class and a phone number subclass.

Encapsulation hides internal parts of an object, restricting access and exposing data via getter and setter methods. Demonstrate a class instance that assigns and retrieves age with proper self reference.

Explore how multiple inheritance works by building a citrus fruit class that inherits from fruit and fruit name, using explicit __init__ to set season and color and print attributes.

Learn how a subclass overrides a superclass init and msg methods, call the superclass method with super, and customize messages in Python-like classes through hands-on Jupyter notebook examples.

Explore decorators in object-oriented programming by distinguishing class methods and static methods from regular methods, illustrating how class state is accessed without instance data.

learn to use the built-in Python property decorator to replace a fruit type attribute with a property, so class Fruit returns name and color (for example lemon has yellow).

Download PostgreSQL by opening your browser and downloading the Windows installer. Run the installer, set a superuser password (store it in a text file), port 5432, and skip Stack Builder.

Create a PostgreSQL database named Airlines in PgAdmin four by selecting the server, creating the database, renaming it to Airlines, and saving with template zero in the public schema.

Learn to restore the airlines database using the graphical user interface by right-clicking the database, selecting restore, and then verify the airlines table after restoration.

Learn to install Jupyter Lab and pandas using cmd and pip, launch the Jupyter Lab server, and start a new Python 3 notebook.

Construct a PostgreSQL query to extract airline bookings and export a csv via copy, then clean and analyze the data with pandas in a Jupyter notebook.

Install and import psycopg2 with pip, connect to the airlines PostgreSQL database in Jupyter Lab, create a cursor, run select * from ticket_flights, and fetch with fetchmany or fetchall.

Learn how to use pandas to run SQL queries against an airlines database loaded via psycopg2, using read_sql_query to create dataframes and demonstrate a natural join to avoid duplicate columns.

Query the airlines database in PostgreSQL, export results to csv, clean departure and arrival city fields, then load and inspect the data with pandas for analysis.

Collect data from sources, read it with python and pandas, seaborn, matplotlib; clean data, handle duplicates and missing values, then perform exploratory data analysis and report insights to decision makers.

Learn to analyze a pandas data frame by listing columns, selecting single or multiple columns, using tail and iloc, checking info and rank, and opening the pandas docs.

Visualize airline sales data with pandas by loading a csv into a data frame with read_csv and plotting bar charts with matplotlib to compare August, July, and June monthly sales.

Learn to use pandas to read_csv data from a CSV file, inspect dtypes, and describe statistics for numeric columns, including sales, and compute mean and median.

Use larger, multiple samples to reduce sampling error, illustrated by a Postgres monthly sales query; then validate with pandas samples and charts showing July leads.

Learn how to scrape websites using Python with BeautifulSoup4 and requests, extract and clean HTML data with pandas or PostgreSQL, and respect robots.txt permissions.

Learn to install lxml and use pandas read_html to scrape a web page table, build a data frame, access columns, and compute statistics like mean, median, standard deviation, and describe.

Learn to visualize scraped data using pandas and matplotlib by reading HTML, building a data frame, and plotting bar and pie charts of website popularity and monthly unique visitors.

Save scraped data to a PostgreSQL database with Python by creating the programming database, cleaning columns, loading a pandas data frame into babbler_language table via SQLAlchemy, then querying the table.

Download and install Sublime Text editor to use as a python IDE, selecting the correct Mac, Windows, or Linux version, pin it to the taskbar, and open your project folder.

Walk through a Python project to arrange folder content: create and rename folders, sort files by type into images, documents, apps, and videos, then copy, verify, and remove originals.

Learn to automate real-world tasks by organizing a folder's contents with Python, using os and shutil to sort files into images, documents, apps, and videos.

Discover how to merge multiple pdf files into one pdf using Python scripting, enabling you to create books, manuals, or chapters for publishing on Amazon Kindle or Google Play Books.

Set up the pdf2 module via cmd or terminal, import the pdf file manager, and merge a list of pdfs into one file using a for loop.

Learn to send emails with Python using SMTP and datetime, install the SMTP library, run commands in cmd or terminal, and test by creating send_emails.py in Sublime Text.

Learn how emails travel from sender to recipient via SMTP, including setting up Gmail and Yahoo accounts, app passwords, and Python code to send messages with a subject.

Automate sending personalized emails from an Excel list using Python, pandas, and TLS, with Black Friday and White Friday conditional messages.

learn how numpy, fast due to its C implementation, acts as a foundation for pandas, scikit-learn, and seaborn, turning data into numeric arrays for machine learning and enabling vectorization.

Install and import numpy, then create and inspect ndarrays using shape and size. Reshape arrays and convert them to data frames with pandas.

Learn to create numpy nd arrays using zeros and ones, generate sequences with arange, and work with random arrays and seeds to produce reproducible results.

Learn numpy.unique and array slicing with hands-on indexing of multi-dimensional arrays, understand the shape three by four by five by six, and extract the last element of every submatrix.

Explore numpy array operations, creating arrays with ones and zeros, and perform calculations using addition, multiplication, division, power, modulo, exponential, and log. Understand broadcasting and shape compatibility.

Explore numpy aggregation functions for arrays, including sum, mean, max, min, std, var, sqrt, and learn the relation between standard deviation and the square root of variance with practical examples.

Learn how to reshape and transpose NumPy arrays to match shapes for operations, avoid value errors from broadcasting, and perform valid multiplication using reshape and transpose.

Explore numpy arrays by applying comparison operators such as greater than, less than, equal, and not equal, ensure same shape, and compute dot products with np.dot.

Use NumPy to process images by converting them to numeric arrays. Upload an image, read it into an array, and inspect its size, shape, and RGB values for processing.

Master data manipulation with data frames in Python using Pandas to clean, summarize, and visualize market history data, including reading CSV and plotting stock prices over time.

Master pandas data manipulation by accessing data in a data frame through column names and positional indexing, filtering by conditions, and exploring multi-level indexing on financial stock data.

Explore data aggregation and summarization in Python to compute mean, median, and standard deviation, visualize price distributions with matplotlib, and perform grouped statistics for financial data.

Create new columns and drop unnecessary ones in Python using pandas to tailor financial data. Apply category cuts, column-wise functions, and conditional updates such as discounting prices for insights.

Explore essential data exploration techniques in Python using pandas to peek at data with head and tail, describe statistics, and visualize distributions with box plots.

Apply Python and pandas to filter stock data, using numeric thresholds, date ranges, and boolean indexing. Learn how to combine conditions and include or exclude specific tickers for targeted analysis.