Intro to Data Analysis

About This Course

This course will introduce you to the world of data analysis. You'll learn how to go through the entire data analysis process, which includes: * Posing a question * Wrangling your data into a format you can use and fixing any problems with it * Exploring the data, finding patterns in it, and building your intuition about it * Drawing conclusions and/or making predictions * Communicating your findings You'll also learn how to use the Python libraries NumPy, Pandas, and Matplotlib to write code that's cleaner, more concise, and runs faster. This course is part of the [Data Analyst Nanodegree](

Why Take This?

This course is a good first step towards understanding the data analysis process as a whole. Before delving into each individual phase, it is important to learn the difference between all phases of the process and how they relate to each other. After taking this course, you will be better positioned to succeed in other courses in the [Data Analyst Nanodegree program]( For example, a student who started with [Data Analysis with R](, which covers the exploratory data analysis phase, might not understand at that point the difference between data exploration and data wrangling. By taking this course first, you will learn what each phase accomplishes and how it fits into the larger process. This course also covers the Python libraries NumPy, Pandas, and Matplotlib, which are indispensable tools for doing data analysis in Python. Their many convenient functions and high performance make writing data analysis code a lot easier!

Prerequisites and Requirements

To take this course, you need to be comfortable programming in Python. * You should be familiar with if statements, loops, functions, lists, sets, and dictionaries. To learn about any of these topics, take the course [Intro to Computer Science]( * You should also be familiar with classes, objects, and modules. To learn about these topics, take the course [Programming Foundations with Python](
Explore a variety of datasets, posing and answering your own questions about each. You'll be using the Python libraries NumPy, Pandas, and Matplotlib.