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!
Lesson 1: Data Analysis Process
In this lesson, you will learn about the data analysis process, which includes posing a question, wrangling and exploring your data, drawing conclusions and/or making predictions, and communicating your findings. You will complete an analysis of Udacity student data using pure Python, with minimal reliance on additional libraries.
Lesson 2: NumPy and Pandas for 1D Data
In this lesson, you will start learning to use NumPy and Pandas to make the data analysis process easier. This lesson focuses on features that apply to one-dimensional data. You'll learn to use NumPy arrays, Pandas Series, and vectorized operations.
Lesson 3: NumPy and Pandas for 2D Data
In this lesson, you'll continue learning about NumPy and Pandas, this time focusing on two-dimensional data. You'll learn to use two-dimensional NumPy arrays and Pandas DataFrames. You'll also learn to group your data and to combine data from multiple files.
Final Project: Investigate a Dataset
In the project, you will use NumPy and Pandas to go through the data analysis process on one of a list of recommended datasets.
Davidcompleted this course, spending 10 hours a week on it and found the course difficulty to be medium.
Took this course as a part of the Data Analyst Nanodegree. Nice coverage of the data analysis / data science process. The videos are well-produced and the instructor (Caroline Buckey) is clear and personable. Lots of programming quizzes enforce the concepts learned in the videos. Left the course confident in my new NumPy and Pandas skills. The final project, which is graded and reviewed in the Nanodegree but not in the free individual course, can be a nice add to a portfolio.
Gabrielcompleted this course, spending 3 hours a week on it and found the course difficulty to be medium.
Teaching fundamental analysis skill of numpy and pandas with actual data projects. Suitable for the ones with basic python knowledge. The content is very practical and applicable. I believe it will be more helpful, if more introduction about the dataset is given.