Python programming with MySQL database: for Data Science

Learn the basics of HTML, including what HTML stands for, opening and closing tags, and how to build interconnected web pages viewable in browsers, following W3C standards.

Learn how to choose a good code editor for python programming with MySQL data science, and explore popular options like Sublime Text, Visual Studio Code, and Notepad++.

Install Sublime Text editor by downloading from the Sublime Text website, choosing Windows or other OS, running the setup, and launching the editor to start coding.

Explore an overview of a web page, including domain names, hosting, publishing in the public domain, and how browsers load pages by typing a domain.

Explore the basic structure of a full HTML webpage, including the head and body sections, opening and closing tags, and how style tags shape the page’s presentation.

Create a first web page by setting up a project folder, writing a simple html document with a title and hello world, and viewing it in a browser.

learn to use heading tags from h1 to h6 to control font size and weight for clear page structure, and save a file to preview in a browser.

Explore how to use the HTML paragraph tag to structure text, control fonts, alignment, and line breaks, with examples of p, h1, and preformatted text.

Learn to format text with HTML tags such as bold, italic, strong, mark, and subscript or superscript, and preview paragraph styling in the browser for data science.

Learn to create unordered lists in html with ul and li, explore bullet styles, and compare to ordered lists, using examples like subjects and days of the week.

Learn to create and customize ordered lists in HTML by using the type attribute to switch between numbers, uppercase letters, lowercase letters, and Roman numerals, enabling clear grouping of items.

Learn how to define and apply css classes to html elements, style text with color and font properties, and reuse styles with a stylesheet to create visually appealing web pages.

Apply ids to HTML div elements and connect them to CSS styles. Practice setting background colors, padding, borders, and text spacing to build simple styled sections.

Master comment lines in python programming to document code, using opening and closing tags to hide or reveal text, write descriptive lines for design or changes, and selectively display paragraphs.

Add images to web pages with the img tag and src attribute, set size with width and height, and use Creative Commons images organized in an images folder.

Explore how to create HTML forms for registration and login, using input types such as text, email, and file uploads. Label inputs with lowercase names; prepare data for database submission.

Learn to represent a table like an Excel spreadsheet by creating a table with rows, columns, headers, and cells, and inserting sample data row by row.

Learn to create hyperlinks with anchor tags, linking internal forms and external sites, and open targets in new tabs using fully qualified domain names.

Learn how to display reserved characters in HTML using entities, including ampersand, less than, greater than, and common symbols like yen, pound, and the copyright or registered trademarks.

Explore how the div tag creates divisions or sections to group elements, apply CSS with classes and IDs, and control layout, alignment, borders, and backgrounds in web pages.

Learn to embed a Google map on a contact page, obtain an API key, and implement the basic html and JavaScript code, with notes on free and commercial licenses.

Learn to add an audio file to a web page using the HTML audio element with a source tag and mp3 format, plus built-in controls.

Learn to embed a video on a webpage using the HTML5 video tag, specify mp4 sources, and configure width, height, aspect ratio, and playback controls for browser compatibility.

Explore how to embed a webpage within a page using the iframe tag, controlling display with the source, height, width, and target attributes.

Explore HTML input types from text and button to checkbox, color, email, file, hidden, month, number, password, radio, search, and time, and learn how name and value attributes shape inputs.

Explore input attributes for forms, including default values, read only and disabled fields, size and maxlength controls, pattern validation, placeholders, required and autofocus, and min/max date patterns.

Build a basic HTML registration form with name, email, password, address, state, country, and pin code fields, using labels, input types, maxlength, and a submit or reset button.

Design a simple contact us form with name, mobile, country, subject fields and a submit button, styled with CSS for containers, labels, inputs, and a country select.

Participate in a coding exercise to build a simple student registration form with fields for name, father's name, email, password, address, country, and action buttons submit, cancel, and reset.

Designs a student registration form by implementing labels and input types for name, email, password, address, state, country, and pin code, with submit, reset, and cancel controls.

Explore Python as an interpreter and high-level language that emphasizes code reliability through indentation and object-oriented design. This design supports reusable code and saves time for large-scale projects.

Discover why Python is popular across software and data science, thanks to vast libraries for data analytics, visualization, machine learning, and AI, plus easy web and IoT integrations.

Install python on Windows by downloading from the official site, choosing Python 3.10.2 64-bit, adding to PATH, and verifying with IDLE and a simple print command.

Learn how to download and install the PyCharm community edition, choose your OS, set up the bin path and desktop shortcut, and launch to create your first Python program.

Launch the PyCharm community edition, create a new Python project with a virtual environment, configure the base interpreter, and prepare to write your first beginner's hello world app.

Learn to write a hello world program in Python for data science by creating a Python file in an IDE, writing a print statement, and running it to see output.

Learn Python data types and how to identify them with the type function, including strings, integers, floats, booleans, and how arithmetic, concatenation, and printing work with variables.

Learn python type casting by converting values between int, float, and string. Understand how casting affects data types, potential data loss, and how to verify types with the type function.

learn to capture user inputs in python with the input function, assign to variables, and convert strings to integers or floats for calculations, including name and age examples.

Learn how to use Python comments to explain code, document purpose, and improve readability, including single-line and multi-line comments, while understanding that comment lines are ignored by the compiler.

Explore strings in Python by learning how to use double quotes for string literals, avoid errors with apostrophes, and print multi-line text using triple quotes.

Learn string indexing in Python using zero-based, positive indices from left to right and negative indices from right to left. Practice retrieving single characters and previewing string slicing.

Learn how to slice strings in Python by using indices and ranges to extract substrings. Practice reversing a string with [::-1] to show the entire text.

Learn to format strings with dynamic data using f-strings, variables, and input to create personalized messages and letters.

Explore arithmetic operators in Python by using integers and float values to perform addition, subtraction, multiplication, division, percent, and power, with examples and operator precedence.

Learn how to use Python assignment operators, such as =, +=, -=, and *=, with practical examples showing how they modify variables efficiently.

Learn logical operators in Python, including and, or, and not, to evaluate two values and decide when to enter loops or blocks in data science tasks.

Understand how the and operator in Python requires both conditions to be true, using x>3 and x<10, and apply this to loan eligibility with income and credit score.

Explore how the or operator evaluates conditions in Python to determine loan eligibility, combining statements about income and credit to yield a true outcome when any condition is true.

Explore how the not operator negates booleans, influencing loan eligibility by flipping true and false, and combine with and/or for clear logical expressions.