8 docs tagged with "Data Science"

This article provides an Introduction to Data Science in Python. Python has become the de facto language for data science due to its simplicity, powerful libraries, and vibrant community.

Following our Introduction to Data Science in Python, this article introduces NumPy (Part 1): Introduction to NumPy arrays. NumPy is the fundamental package for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.

Following our Introduction to NumPy arrays, this article explores NumPy (Part 2): Array indexing, slicing, and operations. We'll learn how to access and manipulate data within NumPy arrays and how to perform mathematical operations on them.

Following our exploration of NumPy, this article introduces Pandas (Part 1): Introduction to Series and DataFrames. Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.

Following our Introduction to Series and DataFrames, this article explores Pandas (Part 2): Reading and writing data (CSV, Excel). A common task in data science is to read data from various file formats and to write data out to files.

Following our lesson on Reading and writing data, this article explores Pandas (Part 3): Data selection and indexing. Selecting and filtering data is one of the most common tasks in data analysis.

Following our lesson on Data selection and indexing, this article explores Pandas (Part 4): Data cleaning and preparation. Real-world data is often messy. Data cleaning is a crucial step in any data analysis workflow.

Following our exploration of The "What" and "Why" of Python (Part 1), this article delves into Python's role in modern software development. We'll explore why Python is the go-to language for web development, data science, machine learning, and automation.