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Learn moreIntro to Data Analysis
Free Course
Data Analysis Using NumPy and Pandas
Estimated time
Approx. 6 Weeks
Skill level
Beginner
Prerequisites
See prerequisites in detail
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, and building intuition
- Drawing conclusions and/or making predictions
- Communicating your findings
You'll also learn how to use the Python libraries like NumPy, Pandas, and Matplotlib to write code that's cleaner, more concise, and faster.
This course is part of the Data Analyst Nanodegree program.
What you will learn
Data Analysis Process
- Learn about the data analysis process.
- Pose a question, wrangle your data, draw conclusions and/or make predictions.
- Complete an analysis of Udacity student data using pure Python, with few additional libraries.
NumPy and Pandas for 1D Data
- Start learning to use NumPy and Pandas to make the data analysis process easier.
- Features that apply to one-dimensional data.
- Learn to use NumPy arrays, Pandas Series, and vectorized operations.
NumPy and Pandas for 2D Data
- Continue learning about NumPy and Pandas, this time focusing on two-dimensional data.
- Learn to use two-dimensional NumPy arrays and Pandas DataFrames.
- Group your data and to combine data from multiple files.
Investigate a Dataset
- Use NumPy and Pandas to go through the data analysis process on one of a list of recommended datasets.
Prerequisites and requirements
To take this course, you should be comfortable programming in Python and familiar with Python concepts like classes, objects, and modules. The Introduction to Python Programming course would be a good place to start learning that material.
See the Technology Requirements for using Udacity.
Why take this course?
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!
Learn with the best.
Caroline Buckey
Instructor