Certificate of Completion - 2023 Python Data Analysis & Visualization Masterclass

I earned a Certificate of Completion, which verifies that I successfully completed the '2023 Python Data Analysis & Visualization Masterclass' beginner's level course on 20/10/2023. The course was instructed by Colt Steele on Udemy. Colt Steele is both a developer and a teacher. The certificate confirms that the entire course was completed, as validated by the student. The course duration is equivalent to the total video hours at the time of my most recent completion, which is 20.50 hours. In this course, I learned about various aspects of data analysis and visualization using pandas, matplotlib, and seaborn. Here's a summary of the topics covered: o Pandas: • DataFrames & Series • Analyze dozens of real-world datasets • Parse various csv files types during import • Perform basic statistics • Hierarchial Columns - Groupby and Aggrigation. Hierarchial Indexing - Multi Indexing • Working With Text • Apply, Map and Applymap • Combining, Merging Series & DataFrames • Pull Out Specific Column(s) • Pull Out Specific Row(s) Based on Row Index Label. • Pull Out Desired Row(s) and Column(s). Rows Pulled Out Based on Row Index Label • Pull Out Specific Row(s) Based on Row Position • Pull Out Desired Row(s) and Column(s). Rows Pulled Out Based on Row Position • Deal with NaN and None and <NA> • Drop/Show/Fill Rows or Column Where Volues are NaN • Convert dtype to different type. Before converting get rid of NaN values in the column • Including NaN values in DataFrame and deciding if to ignore it or not during calculations • Working with Dates • Drop Column(s) or Row(s) • Creating New Columns/Rows • Save the changed dataframe to csv file • Value Count the Number of Identical Values in a Column • Count the Unique Values in a Column • Sort Values/Index • Sort in Ascending or Descending Order using nlargest() or nsmallest() methods • Between/ isin methods • Pull Out Specific Row Values from the Singel Column • Pull Out Specific Row Values from Multiple Columns using AND&, OR| and Negate~ methods • Renaming Columns and Index Labels • Replace Values in the Single Column • Accessing a Group of Rows and Columns by Index Label(s) or by Boolean Array • Pandas Plotting • Set option on display row globally. Set the Index Column to a different column after import of data. Set the Index Column/ the DateTime Column/ the Memory efficiency during the read of the file. o Matplotlib: • First plotting option - three plots in one axis on one figure • Second plotting option - three plots each on its separate axis and all axis grouped on one figure • Third plotting option - three plots and each one on its separate figure • OOP Appproach using plt.subplots() • Functional Approach using plt.subplot() • Figure size and dpi, Saving/Exporting Figures With savefig(),Set the styling, Customize the Line Styles, Colors, Widths, Markers, Changing X & Y Ticks(values) and Changing Their Labels, Zoom in / narrow down the plot, Adding Legends to Plots, customize location(loc), fontsize, labelcolor, facecolor, shadow, frameon • Bar Plot, Histogram, Scatter Plots, Pie Chart o Seaborn: • Seaborn Relational Plots - Relplot: Scatterplot and Lineplot • Seaborn Distributions Plots - Displot Plots: Histogram, Kdeplot, Cdeplot and Rugplot • Seaborn Categorical Plots - Catplot Plots: countplot, stripplot, swarmplot, boxplot, violinplot, pointplot, and barplot • Seaborn Controlling Aesthethics What I liked also about this course was that each section of it had a number of exercises and challenges. You can find this course at https://www.udemy.com/course/python-data-analysis-visualization/