Practicalities involved in Exploratory Data Analysis (EDA)

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Practicalities involved in Exploratory Data Analysis (EDA), in Python.

This course gives the student insight into the practicalities involved in performing an exploratory data analysis (EDA) on a tabulat dataset using the Python programming language.

The student will be given the step by step format in how to carry out an EDA. He will learn what a Jupyter Notebook is and two places that he can access such a notebook for free.

The student will learn about several Python libraries, being: numpy, pandas, sklearn, scipy, statsmodels, matplotlib, and seaborn.

The student will then be introduced to functions and attributes in Python that are necessary to conduct an EDA. These terms include: read_scv, set_option, iloc, head, tail, to_datetime, sum, info, describe, columns, to_list, shape, size, count, isnull, any, fillna, groupby, unique, nunique, replace, astype, value_counts, sort_values, max, min, median, mode, mean, var, cov, map, to_numeric, crosstab, and shuffle.

The student will be introduced to visualisation techniques in the matplotlib library, using the functions pie and scatter.

The student will then be introduced to visualisation techniques in the seaborn library, using the following functions: displot, boxplot, countplot, violinplot, pairplot, and heatmap.

And finally, what the student has learned will be summarized and he/she will be invited to take other courses written by this creator.



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