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Gain an overview of what you’ll be learning and doing in the course and understand why you should learn programming with Python.
Learn to represent data using Python’s data types: integers, floats, booleans, strings, lists, tuples, sets, dictionaries, compound data structure. Perform computations and create logical statements using Python’s operators: Arithmetic, Assignment, Comparison, Logical, Membership, Identity. You'll also declare, assign, and reassign values using Python variables, modify values using built-in functions and methods, and practice whitespace and style guidelines.
Write conditional expressions using if statements and boolean expressions to add decision making to your Python programs, and learn how to use for and while loops along with useful built-in functions to iterate over and manipulate lists, sets and dictionaries. Condense for loops to create lists efficiently with list comprehensions, and use skip iterations in loops using break and continue.
Learn to define your own custom functions, create and reference variables using the appropriate scope, and add documentation to functions using docstrings. You'll also define lambda expressions to quickly create anonymous functions and use iterators and generators to create streams of data.
Learn to install Python 3 and set up your programming environment, and begin running and editing python scripts. You'll also identify and handle errors and exceptions in your code, open, read and write to files, and find and use modules in Python Standard Library and third-party libraries.
Create, access, modify, and sort multidimensional NumPy arrays (ndarrays), and use slicing, boolean indexing, and set operations to select or change subsets of an ndarray. Understand the difference between a view and a copy of ndarray, perform element-wise operations on ndarrays, and use broadcasting to perform operations on ndarrays of different sizes.
Learn to create, access and modify the main objects in Pandas, Series and DataFrames, perform arithmetic operations on Series and DataFrames, load data into a DataFrame, and deal with Not a Number (NaN) values.
You will use Python to answer interesting questions about bikeshare trip data collected from three US cities. You will write code to collect the data, compute descriptive statistics and create an interactive experience in the terminal that presents the answers to your questions.
Data Scientist at Nerd Wallet
Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.
CEO at Mode
Derek is the CEO of Mode Analytics. He developed an analytical foundation at Facebook and Yammer and is passionate about sharing it with future analysts. He authored SQL School and is a mentor at Insight Data Science.
Curriculum Lead at Udacity
Juno is the curriculum lead for the School of Data Science. She has been sharing her passion for data and teaching, building several courses at Udacity. As a data scientist, she built recommendation engines, computer vision and NLP models, and tools to analyze user behavior.
Richard is a Course Developer with a passion for teaching. He has a degree in computer science, and first worked for a nonprofit doing everything from front end web development, to backend programming, to database and server management.
Command Line Instructor
Before joining Udacity, Karl was a Site Reliability Engineer (SRE) at Google for eight years, building automation and monitoring to keep the world's busiest web services online.
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