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1
Introduction
2
Where to start
3
Data set
4
Import data
5
Data frames
6
Understanding data
7
Pandas
8
Subsampling
9
Highlevel statistics
10
Combining data
11
Relationships
12
Plotting
13
Excel
14
Joint Grid
15
Socioeconomic Status
16
Boxplots
17
Spastic Grid
18
The Magic
19
Preprocessing
20
Replacing missing values
21
Modeling
Description:
Dive into a comprehensive workshop on Python for data science, covering essential techniques using popular open-source libraries like pandas and scikit-learn. Learn to cleanse data, perform exploratory analysis, and build predictive models with robust cross-validation using a real-world dataset. Designed for beginners with basic Python knowledge, explore data frames, high-level statistics, data relationships, and various plotting techniques. Master preprocessing steps, including handling missing values, and delve into modeling concepts. Benefit from the expertise of industry professionals as they guide you through practical applications of data science principles.

Introduction to Python for Data Science

Open Data Science
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