课程
Data Preparation in Excel
- 基础技能水平
- 4.6+
- 8.1K
Understand how to prepare Excel data through logical functions, nested formulas, lookup functions, and PivotTables.
数据准备
课程
Understand how to prepare Excel data through logical functions, nested formulas, lookup functions, and PivotTables.
数据准备
课程
In this interactive Power BI course, you’ll learn how to use Power Query Editor to transform and shape your data to be ready for analysis.
数据准备
课程
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
数据准备
课程
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
数据准备
课程
Explore Excel Power Query for advanced data transformation and cleansing to boost your decision-making and analysis.
数据准备
课程
Enter the world of Alteryx Designer and learn how to navigate the tool to load, prepare, and aggregate data.
数据准备
课程
Improve your Python data importing skills and learn to work with web and API data.
数据准备
课程
Bring your Google Sheets to life by mastering fundamental skills such as formulas, operations, and cell references.
数据准备
课程
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
数据准备
课程
Learn to retrieve and parse information from the internet using the Python library scrapy.
数据准备
课程
Master data preparation, cleaning, and analysis in Alteryx Designer, whether you are a new or seasoned analyst.
数据准备
课程
Building on your foundational Power Query in Excel knowledge, this intermediate course takes you to the next level of data transformation mastery
数据准备
课程
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
数据准备
课程
Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.
数据准备
课程
Learn how to create a PostgreSQL database and explore the structure, data types, and how to normalize databases.
数据准备
课程
Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.
数据准备
数据准备
课程
Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.
数据准备
课程
Expand your Google Sheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
数据准备
课程
Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.
数据准备
课程
Learn how to efficiently collect and download data from any website using R.
数据准备
课程
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
数据准备
课程
Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
数据准备
课程
Explore Alteryx Designer in a retail data case study to boost sales analysis and strategic decision-making.
数据准备
课程
Develop the skills you need to clean raw data and transform it into accurate insights.
数据准备
课程
Master marketing analytics using Tableau. Analyze performance, benchmark metrics, and optimize strategies across channels.
数据准备
课程
Enhance your KNIME skills with our course on data transformation, column operations, and workflow optimization.
数据准备
课程
Advance your Alteryx skills with real fitness data to develop targeted marketing strategies and innovative products!
数据准备
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。