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所在平台: Udemy |
课程主页: https://www.udemy.com/course/breaking-in-to-data-science-with-python/
课程评论：没有评论
Coursera 课程《Breaking into Data Science & Machine Learning with Python》旨在帮助您入门数据科学和机器学习领域。 **课程亮点：** * **个人经验分享：** 讲师以自身非计算机科学或统计学背景，成功转型数据科学家的经历为出发点，分享其职业转型过程中的宝贵经验和方法。 * **实战导向：** 课程将深入讲解 Python 及其核心库，如 Pandas、NumPy 和 Scikit-learn，并提供大量的代码示例和实操项目。 * **概念解析：**Methoxy 旨在“去神秘化”机器学习中的复杂概念，并会通过白板讲解等多种方式，帮助学员理解核心要点，并强调数据科学家对于白板的运用能力。 * **重点和限制：** 课程侧重于实际应用和工具的使用，而非深入讲解所有相关的数学理论。 * **持续更新：** 课程内容将不断更新，以确保学习最前沿的工具和库。 **适合人群：** * 希望转行进入数据科学或机器学习领域，但没有传统 CS 或统计学背景的学员。 * 想要系统学习 Python 数据科学库和机器学习基础的初学者。 * 寻求实用指导和实战经验的学员。 **总体而言，本课程为您进入数据科学领域提供了一个极佳的起点，并为您未来的学习之路打下坚实基础。**
Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a "yield analysis engineer" (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes. I took numerous interviews, completed tons of projects and finally I broke into data science. I consider this as one of very important achievement in my life. Without having a degree in computer science (CS) or a statistics I got my second job as a Data Scientist. Since then I have been working as a Data Scientist. If I can break into data science without a CS or Stat degree I think you can do it too! In this class allow me sharing my journey towards data science and let me help you breaking into data science. Of course it is not fair to say that after taking one course you will be a data scientist. However we need to start some where. A good start and a good companion can take us further.We will definitely discuss Python, Pandas, NumPy, Sk-learn and all other most popular libraries out there. In this course we will also try to de-mystify important complex concepts of machine learning. Most of the lectures will be accompanied by code and practical examples. I will also use "white board" to explain the concepts which cannot be explained otherwise. A good data scientist should use white board for ideation, problem solving. I also want to mention that this course is not designed towards explaining all the math needed to "practice" machine learning. Also, I will be continuously upgrading the contents of this course to make sure that all the latest tools and libraries are taught here. Stay tuned!
