EDA In Python Pandas Numpy Matplot (PAID 5 hrs) $99

ven. 24 août à 18:00

Fuseau horaire : Paris (GMT+02:00)

Mathmatter Tutoring
Jackson Heights
États-Unis
Queens

Data Wrangling, Exploratory Data Analysis, and Feature Engineering in Pandas Python Class PPT

https://docs.google.com/presentation/d/1PHuf4U2xEO_ikI_F1zZHIqeNnBSo5Eqxw-fAY6BV9-k/edit?usp=sharing NoteBook:

https://notebooks.azure.com/shivgan3/libraries/DataWranglingEDA RESEARCH DESIGN AND PYTHON PANDAS / DATA WRANGLING TERMS AND CONCEPTS Walkthrough the data science workflow using a case study in the Pandas library

Import, format and clean data using the Pandas Library

Draw Parallels with Excel / SQL STATISTICAL FUNDAMENTALS I

Understand the utility of difference data structures

Use NumPy and Pandas libraries to analyze datasets using basic summary statistics: mean, median, mode, max, min, quartile, inter-quartile, range, variance, standard deviation and correlation

Create data visualization - scatter plots, scatter matrix, line graph, box blots, and histograms- to discern characteristics and trends in a dataset

Identify a normal distribution within a dataset using summary statistics and visualization

Difference between Normalization and Standardization STATISTICAL FUNDAMENTALS II

Explain the difference between causation vs. correlation (rank correlation and pearson)

Test a hypothesis within a sample case study (Simple example of normal distribution)

Validate your findings using statistical analysis (p-values, confidence intervals) MORE INPUTS ON EXPLORATORY USING PANDAS

Deeper insight into exploratory data analysis

Correlation Matrix MISSING DATA

Handling Missing data

Source: https://www.meetup.com/fr-FR/New-York-Python-SQL-Bootcamp-Data-Science-Analytics/events/252977685/


Mathmatter Tutoring
Jackson Heights
États-Unis

Technologie
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