APDS: Intro to Advanced Python for MLOps and Data Science

Name: APDS: Intro to Advanced Python for MLOps and Data Science
Rating: 4.0 (49 reviews)

Introduction: Code as Data

Created byScott Gorlin, Pazpaz The Coder, Albert Hurwitz

Last updated 5/2023

English

What you'll learn

Excel as a Data Scientist by unleashing your programming skills with Advanced Python
Learn to write Higher Level programs by treating your code as data
Create simpler, yet more sophisticated and maintainable, programs
Scale yourself and your impact with reusable code

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

3 sections • 15 lectures • 41m total length

Sketch: Welcome!2:34
Learn to unleash your data science potential with Python for end-to-end machine learning pipelines, focusing on training, packaging, and deploying models in MLops.
What is Advanced Python for Data Science6:31
Define advanced data science by moving ideas from notebooks to production, storing and processing data at scale, and sharing reproducible, standards-driven code with teams using higher-level Python patterns for MLOps.
What is This Course?4:08
Explains the three-day intro bootcamp structure, learning goals, and practical practice; introduces data-centric Python concepts, refactoring, and a final project, with hands-on work in IDEs and GitHub.
Post it on IG: Summary of Today1:02
Advanced Python for MLOps and data science focuses on guidelines, reproducibility, and reusability, while using Instagram updates and hashtags to reinforce standards and tease code-as-data insights.
What is advanced python?

Sketch: Why Code as Data1:04
Adopt the treat your code as data mindset to automate running 29 statistical tests with a reusable pipeline, reducing code repetition and easing unit tests.
Terminology: What is Code as Data5:40
Explore treating code as data by turning functions into configurable blocks, using data frames and first-class functions to build reusable, smaller, more stable Python programs.
Tech Intro: Assign Functions to Variable2:38
Learn how Python separates logic from data by treating functions as data, assigns functions to variables, uses np.sum as an example, and inspects func.__name__ to reveal the function's logic.
Functions of Functions2:45
Learn to create functions of functions by passing callables as arguments, returning functions, and inspecting a function’s module, name, and result with first-class citizen behavior in python.
Tech Intro: Exercise Assigning Functions to Variables0:57
Assign numpy statistical functions to variables and print each function name with its result for first 200 natural numbers, debugging with errors and tests to verify correctness in Udemy editor.
Assign Functions to Variables
Assign Functions to Variables
Concepts: What Do We Gain Here?1:12
Treat code as data and isolate the true logic, enabling easier reuse, testing, and extension by updating a single top-level function to change outputs like pdfs, emails, or database storage.
Daily Post1:28
Learn to treat code as data, separate the how from the what, and apply first class citizens by assigning functions to variables, sending functions to other functions, and returning functions.

Sketch: TikTok reports - Three Evaluations, One Class3:40
Explore treating model evaluation as data by daily aggregations and filters on TikTok channel metrics, validating multiple models with three reports on likes, followers, and sentiments.
Tech Intro: Code-as-Data for TikTok Data Evaluation2:54
Develop a simple program to aggregate and filter three csv reports—followers, sentiments, and likes—by date and county in exercise.py, implementing a grouper function and MyEval checks, with sentiment values 0–1.
Tech Intro: Work Locally with The Starter Code3:06
Clone the starter code from GitHub, set up a local Python environment with pipenv in PyCharm, implement evaluate.py via tests, then copy your solution back to Udemy and check solutions.
Starter Code Concepts
Code as Data for TikTok Data Evaluations
Concepts: What do we gain here1:28
Learn to treat code as data, build reusable, configurable blocks, and future-proof projects by enabling easy data input, logging, reporting, and algorithm extensions.

Requirements

You should be comfortable with basic to intermediate Python
You should be familiar with basic Data Science and Machine Learning concepts
Knowledge of version control (git) is helpful

Description

This is Advanced Python for Data Science!

Today, most people enter the world of Data Science through the buzz and allure of “AI.” We tackle Kaggle challenges, voraciously consume Stack Overflow, and eat, live, and breathe through the Jupyter Notebook. Python, along with its “killer app” of Machine Learning, has done nothing short of revolutionize the way we “do data science,” and the world is a more interesting place because of it!

Most of the time, your impact as a Data Scientist is limited by your ability to enact your ideas - not by the ideas themselves. You can train a model on ‘clean’ data using Scikit Learn or FastAI, or run an ANOVA, in a notebook. Enacting that idea means getting to the data in the first place. It means knowing how to store it. It means processing your data at scale. It means running your processing script, reliably, every day on fresh data. It means testing that script. It means collaborating on that script with a coworker - or 10 - as the project scales. It means curating a library and building tools to solve the same problem for 5 new projects. It means packaging a model up for distribution - sharing with another data scientist, or deploying it as a service.

It means changing the way you think about problems by adopting new paradigms that accelerate you - and your work - across your organization. It means building an approach to data science within the broader python ecosystem.

This is a course about how to be an Advanced Data Science Programmer, leveraging tools and techniques from the broader Python ecosystem. In this introductory course of the Advanced Python for MLOps and Data Science series, we will introduce you to the HEROS concept, and focus on Higher-Levels of coding.

You will learn how to treat your code as data, which is one of the most important ways to leverage Higher Levels of coding, and understand why repetitive code is a good opportunity to refactor into two parts - the purely “logical” part of your code, and the details - or “data”. We will then deep dive into real-world examples and see how we can produce many different outcomes, whether there are reports, analysis or models - with just few lines of logical code.

So, are you ready to take your next step in the MLOps and Advanced Python for Data Science Journey?

Buckle up, and let's get coding!

Who this course is for:

Data Scientists who want to take their coding skills to the next level, learning advanced programming techniques in Python

APDS: Intro to Advanced Python for MLOps and Data Science

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction to Advanced Python for Data Science4 lectures • 14min

Code as Data7 lectures • 16min

Case Study4 lectures • 11min

Requirements

Description

Who this course is for: