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.
Overview
Syllabus
Introduction
Where to start
Data set
Import data
Data frames
Understanding data
Pandas
Subsampling
Highlevel statistics
Combining data
Relationships
Plotting
Excel
Joint Grid
Socioeconomic Status
Boxplots
Spastic Grid
The Magic
Preprocessing
Replacing missing values
Modeling
Taught by
Open Data Science
Related Courses
-
Python for Data Science and Machine Learning beginners
-
Python for Data Science - NumPy, Pandas & Scikit-Learn
-
Data Preparation (Import and Cleaning) for Python
-
Practice Essential Data Science Skills With Diamonds Dataset
-
Introduction to Data Science and scikit-learn in Python
-
Data Analysis with Python: Zero to Pandas
5.0
