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Practicalities involved in Exploratory Data Analysis (EDA)

Description

This course provides the coed perception into the practicalities involved in performing an exploratory knowledge evaluation (EDA) on a tabulat dataset utilizing the Python programming language.

The scholar will likely be given the step-by-step format in the best way to perform an EDA. He’ll study what a Jupyter Pocket book is and two locations that he can entry such a pocket book at no cost.

The scholar will study a number of Python libraries, being: numpy, pandas, sklearn, scipy, statsmodels, matplotlib, and seaborn.

The scholar will then be launched to capabilities and attributes in Python which can be essential to conduct an EDA. These phrases embody: read_scv, set_option, iloc, head, tail, to_datetime, sum, information, describe, columns, to_list, form, dimension, depend, isnull, any, fillna, groupby, distinctive, nunique, change, astype, value_counts, sort_values, max, min, median, mode, imply, var, cov, map, to_numeric, crosstab, and shuffle.

The scholar will likely be launched to visualisation methods in the matplotlib library, utilizing the capabilities pie and scatter.

The scholar will then be launched to visualisation methods in the seaborn library, utilizing the next capabilities: displot, boxplot, countplot, violinplot, pairplot, and heatmap.

And eventually, what the coed has discovered will likely be summarized and he/she will likely be invited to take different programs written by this creator.

Who this course is for:

  • This course is appropriate for any Python builders who’ve an curiosity in performing exploratrory knowledge evaluation.

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