课程
Data Structures and Algorithms in Python
- 高级技能水平
- 4.4+
- 3.4K
Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!
软件开发
课程
Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!
软件开发
课程
Learn how to implement and schedule data engineering workflows.
数据工程
课程
Learn the fundamentals of working with big data with PySpark.
数据工程
课程
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
软件开发
课程
Apply PyTorch to images and use deep learning models for object detection with bounding boxes and image segmentation generation.
人工智能
课程
Master Git’s advanced features to streamline data science and engineering workflows, from complex merging to large-scale project optimization.
软件开发
课程
Build powerful multi-agent systems by applying emerging agentic design patterns in the LangGraph framework.
人工智能
课程
Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.
软件开发
课程
What makes LLMs tick? Discover how transformers revolutionized text modeling and kickstarted the generative AI boom.
人工智能
课程
Take your dbt skills to the next level with this hands-on course designed for data engineers and analytics professionals.
数据工程
课程
Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.
机器学习
课程
Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.
人工智能
课程
Discover the exciting world of Deep Learning for Text with PyTorch and unlock new possibilities in natural language processing and text generation.
人工智能
课程
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
机器学习
课程
This course focuses on feature engineering and machine learning for time series data.
机器学习
数据准备
课程
Learn how to use FastAPI to develop APIs that support AI models, built to meet real-world demands.
人工智能
课程
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
机器学习
课程
Build your OOP skills with descriptors, multilevel inheritance, and abstract base classes!
软件开发
课程
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
人工智能
课程
In this interactive course, you’ll learn how to use functions for your Tableau calculations and when you should use them!
数据可视化
课程
Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control
机器学习
课程
Learn how to transform raw data into clean, reliable models with dbt through hands-on, real-world exercises.
数据工程
课程
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
机器学习
课程
Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.
人工智能
课程
Prepare for your next coding interviews in Python.
软件开发
课程
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
应用金融
课程
Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
人工智能
课程
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
机器学习
课程
Learn about ARIMA models in Python and become an expert in time series analysis.
机器学习
数据科学是一个专注于从数据中获取信息的专业领域。数据科学家使用编程技能、科学方法、算法等来分析数据,形成可操作的洞察。
你需要学习 Python 或 R 等编程语言,掌握数学和统计学原理。数据分析方法和数据科学工具的知识也是必不可少的。学习数据科学有很多方法。除了正式的教育途径,如学位或大学学习,还有很多其他资源可以帮助你按自己的节奏学习。除了在线课程和教程,还有书籍、视频等。
除了数学和统计学知识,数据科学家还需要 Python、R 和 SQL 等语言的编程技能。此外,数据科学需要处理大型数据集的能力、数据可视化、数据整理和数据库管理知识。机器学习和深度学习技能也很有用。
在专业领域,几乎每个行业都可以在某种程度上使用数据科学。医疗机构使用数据科学来检测和治疗疾病,金融公司用它来检测和预防欺诈。各种行业都将数据科学用于营销,如构建推荐系统和分析客户流失。
是的,数据科学是美国和全球增长最快的行业之一。它也是薪酬最高的职业之一。根据 Payscale 的数据,在美国,有经验的数据科学家平均收入为 97,609 美元,满意度评分为五星中的四星。
这里有几个需要考虑的因素。首先,数据科学学位的竞争可能很激烈,通常需要持续的高分。同样,数据科学所需的许多技能需要大量的学习和耐心。掌握所有必要的基础知识可能需要几个月的时间,还需要大量的实践经验才能获得入门级职位。
是的,你需要一些 Python、R、SQL、Java 和 C/C++ 等语言的编程经验。不过,由于语法相对简单,Python 编程语言通常是新手的首选。
对于没有编程经验和/或数学背景的人来说,通常需要 7 到 12 个月的密集学习才能达到入门级数据科学家的水平。但是,重要的是要记住,仅仅学习数据科学的理论基础可能不会让你成为真正的数据科学家。
掌握数据科学基础后,你可以专攻各种领域,包括机器学习、人工智能、大数据分析、商业分析和智能、数据挖掘等。
随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。