Data Science is truly comprised of two main topics: math and programming. However, one does not need to be a computer scientist or mathematician, one does not even need to have taken algebra or a basic programming class to start.
Due to the unfortunate spread of the Coronavirus, most of us are left working remotely, social distancing, thus resulting in a lot of free time with very little to do. In this article, I want to explain how you can use your free time in the most efficient manner by learning a new skill that can progress your career to new lengths.
In this series of posts, we'll cover various applications of statistics in Python. This first post talks about calculating the mean using Python.
Python is a multi-purpose programming language that can be used for creating apps, data analysis, machine learning, visualization, and more. There are a handful of helpful packages out there including NumPy and Pandas for data analysis, Matplotlib for visualization, Sci-Kit Learn for machine learning, and more. Python is a relatively easy language to learn so you can get up and running quickly.
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In this tutorial, we'll cover the cental tendency statistic, the median. The median is the middle value in a dataset when ordered from largest to smallest or smallest to largest.
The mode is the value that occurs the most frequently in the data set. A good trick to remember the definition of mode is it sounds very similar to “most”. This is probably the least useful out of three statistics but still has many real-world applications.
The standard deviation or variance, the standard deviation is just the variance square rooted or raised to ½. The standard deviation is more commonly used, and it is a measure of the dispersion of the data. This is very different than the mean, median which gives us the “middle” of our data, also known as the average.
This tutorial will walk through calculating three key summary measures of variability in data - range, IQR, and percentile.
In this article, we'll discuss two of the most popular tools for data analytics, Excel and Python. We'll walk through how each of these tools fares across a variety of dimensions to help you with your next steps in professional development and learning.
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