Name: Data Science with NumPy, Sets, and Dictionaries
Brand: Duke University
Availability: OnlineOnly
Rating: 3.9 (8 reviews)

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课程概述

Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators. Starting with foundational computer science concepts, such as object-oriented programming and data organization using sets and dictionaries, you'll progress to more intricate data structures like arrays, vectors, and matrices. Hands-on practice with NumPy will equip you with essential skills to tackle big data challenges and solve data problems effectively. You'll write Python programs to manipulate and filter data, as well as create useful insights out of large datasets. By the end of the course, you'll be adept at summarizing datasets, such as calculating averages, minimums, and maximums. Additionally, you'll gain advanced skills in optimizing data analysis with vectorization and randomizing data. Throughout your learning journey, you'll use many kinds of data structures and analytic techniques for a variety of data science challenges , including mathematical operations, text file analysis, and image processing. Stepwise, guided assignments each week will reinforce your skills, enabling you to solve problems and draw data-driven conclusions independently. Prepare yourself for a rewarding career in data science by mastering NumPy and honing your programming prowess. Start this transformative learning experience today!...

1 - Data Science with NumPy, Sets, and Dictionaries 的 7 个评论（共 7 个）

创建者 Dan C

•

Jul 20, 2024

This is the second course in the series, and I am giving it a 5-star rating as I did for the first one. It does a great job at introducing numpy arrays, and the way to work with them. You can see how it foreshadows the next course in the series (using Pandas), and it provides enough practice so that you can build a good foundation to do data science. I like how they use real data sets (that have been preprocessed) for illustrations and exercises, and this gives you a sense of the latent power of these tools. The exercises are not too challenging, but it depends on how much prior knowledge you have. Looking forward to the last two courses (4, 5) that are not yet listed. I think if you go through this series and do all the assignments, you will come out with a good understanding of the tools and methods used in data science.