Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning.
In this video course, you will explore two of the most important Python packages used by Data Analysts. You will start off by learning how to set up the right environment for data analysis with Python. Here, you’ll learn to install the right Python distribution, as well as work with the Jupyter notebook, and set up a database. After that you will dive into Python’s NumPy package, Python’s powerful extension with advanced mathematical functions. You will learn to create NumPy arrays, as well as employ different array methods and functions. Then, you will explore Python’s Pandas extension, where you will learn to subset your data, as well as dive into data mapping using Pandas. You’ll also learn to manage your data sets by sorting and ranking them. Finally, you will learn to index and group your data for sophisticated data analysis and manipulation.
About the Author
Curtis Miller is a graduate student at the University of Utah, seeking an Master’s in Statistics (MSTAT) and a Big Data Certificate.
In the past, Curtis has worked as a Math Tutor, and has a double major adding mathematics with an emphasis in statistics as a second major.
Curtis has studied the gender pay gap, and presented his paper or Gender Pay Disparity in Utah, which grabbed the attention of local media outlets.
He currently teaches Basic Statistics at the University of Utah. He enjoys writing and is an avid reader, and enjoys studying politics, economics, history, and psychology and sociology.
We prepare to analyze data with Python by installing Anaconda
You'll learn to write literate Python code with Jupyter Notebooks
You'll learn when and how to use alternative development environments to Jupyter
You'll learn how to manage packages and environments with conda.
We set up a MySQL database to store the data used in data analysis
You'll learn about NumPydtypes, special values, ndarrays, and matrices.
How can we create an ndarray with our data? We see the NumPy functions for creating ndarrays.
We need to access data in an ndarray; here, we see how subsetting is done, so we get exactly the data we need.
How can we use ndarrays for computation? We see how arithmetic and linear algebra with NumPy arrays is done.
How can we use NumPy to perform calculations fast? With ufuncs and vectorization!
What is pandas? We get an overview of the pandas functionality.
How is a Series or DataFrame structured? How can we create them? You'lllearn how Series and DataFrames work by creating some from scratch.
How do we work with a subset of data in a pandas object? Here,you'lllearn about and use slicing operations.
How can we perform computations using pandas objects? Here we use arithmetic, vectorized functions, and function mappings to get useful quantities.
What do we do with the missing data? We talk about strategies for handling missing data and how to implement them with pandas
How do we sort/rank data? We see how to use pandas objects for this task.
How do we use hierarchical indices? We talk about how to create hierarchical indices with pandas and how to use them for data subsetting.
How can we visualize data in a pandas object? We apply pandas plotting convenience methods.
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