Applied Python for Data Science and Analytics

Ditch the Fluff, Embrace the Challenge: Master Python for Real-World Data Science and Analytics

Created byJeff James

Last updated 5/2024

English

What you'll learn

Moving beyond basic Python tutorials into solving complex and practical problems
Mastering both Python and Pandas, while being fluent enough to let AI help you solve problems
Parsing natural language, structured data and analyze any kind of data you may encounter in business or research
Develop confidence in problem solving and your ability to think through a problem
Using AI as a Copilot as we get into progressively complex problem solving

Course content

7 sections • 42 lectures • 9h 6m total length

Introduction - what's this course about? Should you take it?6:57
1 - Sorting Customers , Getting our feet wet11:38
Make sure you have a google account and can access http://colab.research.google.com - I've provided links to starter and final notebooks. Let's get after it!
2 - Comprehensions, Summing, Conditionals12:22
3 - datetime module, adding complexity to customers, dictionaries11:30
4 - defaultdict, intro to Pandas10:26
5 - More Pandas, Introduction to .map8:38

Natural Language Intro and Tokenization12:49
Learning to wrangle text is a key skill - let's get started!
Token Frequencies Solved - Regexp Intro10:00
Let's get our feet wet with regular expressions, too.
Regexp Solved - Using AI to help Code12:36
https://colab.research.google.com/drive/1_7PJTyY6S-bFwmPEOPrxTAhkwOv_KcOJ?usp=sharing

Sometimes regular expressions can get tricky, especially if we're building them dynamically. Let's use AI to help us solve our problem.
%prun performance and Intro to Sentence Similarity Challenge13:51
Let's think about performance analysis and think about how we might compare each sentence to all other sentences.
Pairwise Sentences Partially Solved - itertools10:34
https://colab.research.google.com/drive/1_7PJTyY6S-bFwmPEOPrxTAhkwOv_KcOJ?usp=sharing

Rather than write 2 for loops, let's use itertools.combinations to help us iterate over all pairs of sentences and then compute the length of token intersections while storing them and creating a dataframe later on.
Pairwise Sentences Solved - Jaccard Challenge12:25
https://colab.research.google.com/drive/1_7PJTyY6S-bFwmPEOPrxTAhkwOv_KcOJ?usp=sharing
Alright, we did it! As a bonus, try to go from mere token interaction length to Jaccard similarity! https://en.wikipedia.org/wiki/Jaccard_index#Overview

Parsing the Garmin dataset using beautiful soup13:56
Converting Points into DataFrame, reviewing comprehensions13:28
Pandas Type Conversions, Plotting the Workout14:58
Computing Cumulative Time and analyzing correlations14:34
Pairplots using Seaborn and Using AI to help us compute cumulative distance12:45
Using Scipy to find Peaks and Troughs - continuing to code with AI14:48
Battling through to get the correct peaks and troughs indicated in our dataframe14:59
Avg Lap Distance and Also computing total climbing using AI and numpy clip14:56
Final notebook https://colab.research.google.com/drive/1ZcO3JzfI59o1pY98ALqND3C5ifEyBs4y?usp=sharing - GREAT WORK

Intro to Supervised ML, Classification using XGBoost14:47
Training our XGBoost Model - Thinking about Decision Criteria -precision, recall14:53
XGBoost automated categorical handling document mentioned https://developer.nvidia.com/blog/categorical-features-in-xgboost-without-manual-encoding/

https://docs.google.com/spreadsheets/d/1RaoRSl-OH7N8gs6htBRXR5aqqP9TQ5RlOYAo7GMm4D8/edit#gid=1218296526 the document I'm using to describe business decisions and the link to true positives, precision, recall, etc...
More about Precision and Recall14:42
XGBoost automated categorical handling document mentioned https://developer.nvidia.com/blog/categorical-features-in-xgboost-without-manual-encoding/

https://docs.google.com/spreadsheets/d/1RaoRSl-OH7N8gs6htBRXR5aqqP9TQ5RlOYAo7GMm4D8/edit#gid=1218296526 the document I'm using to describe business decisions and the link to true positives, precision, recall, etc...
Final Precision Recall Discussion across Scenarios (and Kangaroos)14:13
Saving / Loading our Model and Making Single Predictions14:55
Feature Importance, Dangers of Categorical Encoding w/unseen values11:51
Analyzing and Interpreting Lead time and Avg Room Price per Night Using Pandas14:57
Analyzing Features using Simulation and Prediction (prelude to partial dep)13:58

Requirements

Understanding the basics of Python
A Google account to use Colab Notebooks
Have some appreciation for problems data scientists and analyst must solve on a daily basis.

Description

Unlock the Power of Python for Real-World Data Science and Analytics

Are you ready to take your Python skills to the next level and tackle real-world data science and analytics challenges? Look no further than "Applied Python for Data Science and Analytics," a comprehensive Udemy course designed to bridge the gap between memorization and practical problem-solving.

In this course, you'll learn from Jeff James, a senior machine learning engineering manager with 15 years of applied data analytics and coding experience, who has also taught at the University of Denver. Andrew will guide you through the complexities of the Python standard library, Pandas, SciPY, and powerful machine learning libraries like scikit-learn, empowering you to solve open-ended problems with confidence.

Throughout the course, you'll dive deep into real-world scenarios, learning how to approach and solve challenges that go beyond the typical "table of contents" style video courses. You'll gain hands-on experience working with diverse datasets, applying advanced analytical techniques, and leveraging the full potential of Python's data science ecosystem.

Whether you're a data analyst, aspiring data scientist, or a developer looking to expand your skill set, this course will equip you with the tools and knowledge you need to excel in the field. You'll learn how to:

- Effectively utilize the Python standard library for data manipulation and analysis

- Harness the power of pandas for efficient data wrangling and exploration

- Apply statistical techniques using SciPY to gain deeper insights from your data

- Implement machine learning algorithms using scikit-learn to solve real-world problems

- Develop a problem-solving mindset to tackle open-ended challenges in data science and analytics

By the end of this course, you'll have a robust portfolio of projects showcasing your ability to apply Python to real-world data science and analytics problems. You'll be ready to take on complex challenges, drive data-driven decision-making, and make a tangible impact in your organization.

Don't miss this opportunity to learn from an experienced industry professional and elevate your Python skills to new heights. Enroll now in "Applied Python for Data Science and Analytics" and unlock your full potential in the world of data science and analytics!

Who this course is for:

Data Analysts and Data Scientists
Beginner Python Developers that want to develop deeper skills

Applied Python for Data Science and Analytics

What you'll learn

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Course content

Introduction and Getting Moving with Python6 lectures • 1hr 2min

Applied Problem # 1 - Birthday Paradox4 lectures • 45min

Natural Language Parsing and Analysis6 lectures • 1hr 12min

Natural Language Project - Using ScikitLearn6 lectures • 1hr 22min

Building Expertise with Pandas and Analyzing Data8 lectures • 1hr 54min

Some Additional Python Practice - Not required, But see if you can do it!4 lectures • 57min

Applied Machine Learning - WIP - But Come Learn!8 lectures • 1hr 54min

Requirements

Description

Who this course is for: