My solutions for the "Data Analysis with Python" course on FreeCodeCamp.org. The course covers the fundamentals of data science using Python, the basic data sources like CSV and SQL, and the key libraries NumPy, Pandas, MatPlotLib and Seaborn. Visit the freecodecamp website for more info: https://www.freecodecamp.org/learn/data-analysis-with-python/

The following information about the projects is copied from the above website.

Create a function named calculate() in mean_var_std.py that uses Numpy to output the mean, variance, standard deviation, max, min, and sum of the rows, columns, and elements in a 3 x 3 matrix. The input of the function should be a list containing 9 digits. The function should convert the list into a 3 x 3 Numpy array, and then return a dictionary containing the mean, variance, standard deviation, max, min, and sum along both axes and for the flattened matrix.

In this challenge you must analyze demographic data using Pandas. You are given a dataset of demographic data that was extracted from the 1994 Census database. You must use Pandas to answer a set of questions detailing specific subgroups of the population.

The rows in the dataset represent patients and the columns represent information like body measurements, results from various blood tests, and lifestyle choices. You will use the dataset to explore the relationship between cardiac disease, body measurements, blood markers, and lifestyle choices.

For this project you will visualize time series data using a line chart, bar chart, and box plots. You will use Pandas, Matplotlib, and Seaborn to visualize a dataset containing the number of page views each day on the freeCodeCamp.org forum from 2016-05-09 to 2019-12-03. The data visualizations will help you understand the patterns in visits and identify yearly and monthly growth.

You will analyze a dataset of the global average sea level change since 1880. You will use the data to predict the sea level change through year 2050.