Free Course
Intro to Data Analysis
School Of Data Science
Explore a variety of datasets, posing and answering your own questions about each. You'll be using the Python libraries NumPy, Pandas, and Matplotlib.
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- Data Analyst
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This Course
Free Course
Intro to Data Analysis
Industry-relevant content
Projects
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Mentorship
Certification
Career Services / Job Assistance
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Course Details
Course Details Free
Timeline Approx. 6 Weeks
Skill Level Beginner
Included in Product
Rich Learning Content
Taught by Industry Professionals
Student Support Community
Interactive Quizzes
Self-Paced Learning
Intro to Data Analysis
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.
Course Leads
Caroline Buckey
InstructorSyllabus
lesson 1
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.
lesson 2
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.
lesson 3
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.
lesson 4
Investigate a Dataset
- Use NumPy and Pandas to go through the data analysis process on one of a list of recommended datasets.