Applied Python for Data Science and Analytics

سرفصل های دوره

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

1. Introduction and Getting Moving with Python

1. Introduction - whats this course about Should you take it

2.1 Final Code (for entire section).html

2.2 Starter Code.html

2. 1 - Sorting Customers , Getting our feet wet

3. 2 - Comprehensions, Summing, Conditionals

4. 3 - datetime module, adding complexity to customers, dictionaries

5. 4 - defaultdict, intro to Pandas

6. 5 - More Pandas, Introduction to .map

2. Applied Problem # 1 - Birthday Paradox

1.1 Colab Notebook.html

1. Birthday Paradox Intro - Reading Data, Sampling

2. Applied 1 - Birthday Paradox Solved

3. Applied 1 - Birthday Paradox Variations

4. Applied 1 - Birthday Outro and Gotcha!

3. Natural Language Parsing and Analysis

1.1 Solved Notebook - Use for starter code.html

1.2 Starter Code.html

1. Natural Language Intro and Tokenization

2.1 Solved Notebook.html

2.2 Starter Code.html

2. Token Frequencies Solved - Regexp Intro

3. Regexp Solved - Using AI to help Code

4. %prun performance and Intro to Sentence Similarity Challenge

5. Pairwise Sentences Partially Solved - itertools

6. Pairwise Sentences Solved - Jaccard Challenge

4. Natural Language Project - Using ScikitLearn

1.1 Starter Notebook.html

1. Intro the Section, zip and bigrams + trigrams challenge

2. Working with CountVectorizer and the Core idea behind document vector spaces

3. Working with numpy and building our own distance metric

4. Dot Product, Cosine Similarity and Embeddings

5. Pairwise Distances, Numpy Array Indexing and .argminmax

6.1 All Solved Code.html

6. Newsgroups Challenge Solved - More on Pandas

5. Building Expertise with Pandas and Analyzing Data

1.1 Starter Code.html

1. Parsing the Garmin dataset using beautiful soup

2. Converting Points into DataFrame, reviewing comprehensions

3. Pandas Type Conversions, Plotting the Workout

4. Computing Cumulative Time and analyzing correlations

5. Pairplots using Seaborn and Using AI to help us compute cumulative distance

6. Using Scipy to find Peaks and Troughs - continuing to code with AI

7. Battling through to get the correct peaks and troughs indicated in our dataframe

8.1 FINAL NOTEBOOK.html

8. Avg Lap Distance and Also computing total climbing using AI and numpy clip

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تولید کننده: Udemy