课程
Introduction to Generative AI in Snowflake
- 中级技能水平
- 4.8+
- 336 条评价
Learn to build AI applications using Snowflake Cortexs built-in LLM functions for text analysis, generation, and multi-step workflows.
人工智能
观看专家讲师的短视频,然后在浏览器中通过互动练习实践所学内容。
或
课程
Learn to build AI applications using Snowflake Cortexs built-in LLM functions for text analysis, generation, and multi-step workflows.
人工智能
课程
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
机器学习
课程
Learn how to use and create sub-agents in Claude Code to manage context, delegate tasks, and build workflows that keep your conversation clean and focused.
人工智能
课程
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
应用金融
课程
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
机器学习
课程
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
机器学习
课程
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
应用金融
课程
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
概率与统计
课程
Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.
概率与统计
课程
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
数据工程
课程
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
概率与统计
课程
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
报告
课程
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
概率与统计
课程
Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.
数据处理
课程
Design resilient, production-ready n8n automations that fetch APIs, process data in batches, handle errors, and run unattended on a schedule.
人工智能
课程
Build the foundation you need to think statistically and to speak the language of your data.
概率与统计
课程
Learn to perform linear and logistic regression with multiple explanatory variables.
概率与统计
课程
In this course, students will learn to write queries that are both efficient and easy to read and understand.
软件开发
课程
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
数据可视化
课程
This course will show you how to integrate spatial data into your Python Data Science workflow.
数据处理
课程
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
机器学习
课程
Learn how to design Power BI visualizations and reports with users in mind.
数据可视化
课程
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
概率与统计
课程
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
软件开发
课程
Use Seaborns sophisticated visualization tools to make beautiful, informative visualizations with ease.
数据可视化
课程
Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.
机器学习
机器学习
课程
Learn Snowflake data types and functions to manipulate text, numbers, and dates while building custom functions and pivot tables.
数据处理
课程
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
概率与统计
课程
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
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。
您需要学习 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您按自己的节奏学习。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,您需要一些 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您成为真正的数据科学家。
掌握数据科学基础后,您可以专攻各种领域,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。