Learning Python for Data Science
Offered By: Harvard University via edX
Course Description
Overview
Data science is an ever-evolving field, constantly iterating and innovating as technologies and algorithms improve. In order to drive your career forward, you must stay on the cutting-edge of the newest programming languages, such as Python, to stand out from the rest.
Based around three courses, this Professional Certificate in Learning Python for Data Science focuses on hands-on learning—putting your Python skills into practice for applied data science. Each course will build upon each other, preparing you to solve complex business challenges using coding and data analysis. No prior coding experience required to enjoy this program.
Taught by experts in the field, you will learn the foundations of Python programming and statistics before moving into more advanced learning around Python for machine learning and AI—all while building your quantitative reasoning and statistical skills. By combining these tools, you will not only become a more invaluable contributor to your team and organization, but you also will kickstart your career in the in-demand field of data science.
Syllabus
Course 1: CS50's Introduction to Programming with Python
An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.
Course 2: Fat Chance: Probability from the Ground Up
Increase your quantitative reasoning skills through a deeper understanding of probability and statistics.
Course 3: Introduction to Data Science with Python
Learn the concepts and techniques that make up the foundation of data science and machine learning.
Courses
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Created specifically for those who are new to the study of probability, or for those who are seeking an approachable review of core concepts prior to enrolling in a college-level statistics course, Fat Chance prioritizes the development of a mathematical mode of thought over rote memorization of terms and formulae. Through highly visual lessons and guided practice, this course explores the quantitative reasoning behind probability and the cumulative nature of mathematics by tracing probability and statistics back to a foundation in the principles of counting.
In Modules 1 and 2, you will be introduced to basic counting skills that you will build upon throughout the course. In Module 3, you will apply those skills to simple problems in probability. In Modules 4 through 6, you will explore how those ideas and techniques can be adapted to answer a greater range of probability problems. Lastly, in Module 7, you will be introduced to statistics through the notion of expected value, variance, and the normal distribution. You will see how to use these ideas to approximate probabilities in situations where it is difficult to calculate their exact values.
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An introduction to programming using a language called Python. Learn how to read and write code as well as how to test and "debug" it. Designed for students with or without prior programming experience who'd like to learn Python specifically. Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs, and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files. Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems. No software required except for a web browser, or you can write code on your own PC or Mac.
Whereas CS50x itself focuses on computer science more generally as well as programming with C, Python, SQL, and JavaScript, this course, aka CS50P, is entirely focused on programming with Python. You can take CS50P before CS50x, during CS50x, or after CS50x. But for an introduction to computer science itself, you should still take CS50x!
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Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?
Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).
Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.
Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.
Taught by
Benedict Gross, Joseph Harris, Pavlos Protopapas, Emily Riehl and David J. Malan
Tags
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