Python 数据科学课程可以帮助您学习数据操作、统计分析、机器学习和数据 Visualization。您可以培养使用 Python 编程、使用 Pandas 和 NumPy 等库以及应用算法解决实际问题的技能。许多课程都会介绍一些工具,如用于交互式编码的 Jupyter Notebook、用于创建可视化的 Matplotlib 和用于实现机器学习模型的 Scikit-learn 等,所有这些工具都能提高您分析和解释复杂数据集的能力。

您将获得的技能: Python 程序设计, JSON, Pandas(Python 软件包), 数据导入/导出, 还原式 API, 面向对象编程(OOP), 数据操作, 数据结构, 数据分析, 计算机编程, NumPy, 文件输入/输出, 网页抓取, 自动化, Jupyter, 应用编程接口 (API), 编程原则
初级 · 课程 · 1-3 个月

IBM
您将获得的技能: Python 程序设计, 模型评估, 数据分析, Pandas(Python 软件包), 数据导入/导出, Scikit-learn (机器学习库), 回归分析, 数据预处理, 数据操作, 数据清理, 数据转换, 统计分析, 预测建模, NumPy, 数据科学, Matplotlib, 探索性数据分析, 数据驱动的决策制定, 数据可视化
中级 · 课程 · 1-3 个月

University of Michigan
您将获得的技能: Python 程序设计, 交互式数据可视化, 数据可视化软件, 图论, 模型评估, Pandas(Python 软件包), 社交网络分析, 数据预处理, 自然语言处理, 监督学习, 文本挖掘, 数据操作, 科学可视化, 功能工程, 网络分析, 应用机器学习, NumPy, 可视化(计算机制图), Matplotlib, 数据可视化
中级 · 专项课程 · 3-6 个月
University of Michigan
您将获得的技能: Python 程序设计, 数据分析, Pandas(Python 软件包), 数据导入/导出, 数据预处理, 统计分析, 数据操作, 数据清理, 编程原则, NumPy, 数据科学, 透视表和图表
中级 · 课程 · 1-4 周

Duke University
您将获得的技能: Matplotlib, Pandas (Python Package), NumPy, Data Visualization, Data Cleansing, Data Structures, Data Visualization Software, Predictive Analytics, Debugging, Object Oriented Programming (OOP), Data Manipulation, Regression Analysis, Python Programming, Data Science, Algorithms, Simulations, Data Preprocessing, Exploratory Data Analysis, Predictive Modeling, Data Analysis
初级 · 专项课程 · 3-6 个月

您将获得的技能: Pandas (Python Package), NumPy, Data Manipulation, Data Preprocessing, Package and Software Management, Data Analysis, Data Transformation, Data Integration, JSON, Object Oriented Programming (OOP), Data Wrangling, Data Science, Python Programming, Computer Programming, Programming Principles, Data Import/Export, Software Design, Data Validation, Computational Logic, Data Structures
初级 · 专项课程 · 3-6 个月

DeepLearning.AI
您将获得的技能: Pandas (Python Package), Data Visualization, Time Series Analysis and Forecasting, Matplotlib, Data Visualization Software, Statistical Inference, Statistical Analysis, Seaborn, Exploratory Data Analysis, Descriptive Statistics, NumPy, Data Manipulation, Programming Principles, Python Programming, Regression Analysis
初级 · 课程 · 1-3 个月

University of Colorado Boulder
您将获得的技能: Python 程序设计, 数据可视化软件, Pandas(Python 软件包), 数据导入/导出, 脚本, 数据操作, 数据结构, 绘图(图形), 编程原则, 计算机编程, Matplotlib, 功能设计, NumPy, 脚本语言, 软件包和软件管理, 软件工程, 数据科学, 柱状图, Seaborn, 数据可视化
初级 · 专项课程 · 1-3 个月

您将获得的技能: Object Oriented Programming (OOP), Data Structures, Python Programming, NumPy, Pandas (Python Package), Data Analysis, Scripting, Data Manipulation, Data Visualization, Algorithms, Debugging
高级设置 · 课程 · 1-3 个月

IBM
您将获得的技能: Python 程序设计, 仪表板, Pandas(Python 软件包), 数据分析, 数据操作, 数据处理, 数据收集, 网页抓取, 数据展示, 数据科学, Jupyter
中级 · 课程 · 1-4 周

您将获得的技能: Descriptive Statistics, Model Evaluation, Feature Engineering, Probability & Statistics, Supervised Learning, Statistical Hypothesis Testing, Exploratory Data Analysis, Box Plots, Regression Analysis, Statistics, Predictive Modeling, Time Series Analysis and Forecasting, Data Science, Histogram, Data Visualization, Statistical Analysis, Integrated Development Environments, Computer Networking, Python Programming, Server Side
初级 · 专项课程 · 3-6 个月

您将获得的技能: Python 程序设计, 数据可视化软件, 仪表板, Pandas(Python 软件包), 描述性统计, 数据导入/导出, 数据分析, 存储过程, 统计分析, 统计, 数据展示, 概率分布, 网页抓取, SQL, 计算机编程工具, 数据科学, Jupyter, 编程原则, 关系数据库, 数据可视化
攻读学位
初级 · 专项课程 · 3-6 个月
用于数据科学的 Python 是一种编程语言和一套工具,用于分析和解释复杂的数据。它之所以重要,是因为它能让专业人员从数据中提取洞察力,从而推动各行各业的决策和创新。Python 凭借其简单性和多功能性,已成为数据科学家的首选,使其成为任何希望进入这一领域的人的必备工具。
掌握了数据科学方面的 Python 技能,您就可以从事各种职位,包括数据分析师、数据科学家、机器学习工程师和 Business Intelligence 分析师。这些职位通常涉及分析 Data Set、创建预测模型以及向利益相关者传达研究结果,因此熟练掌握 Python 是当今就业市场的宝贵财富。
To succeed in Python data science, you'll need a mix of technical and analytical skills. Key competencies include proficiency in Python programming, understanding of data manipulation libraries like Pandas and NumPy, and familiarity with data visualization tools such as Matplotlib and Seaborn. Additionally, knowledge of statistical concepts, machine learning algorithms, and database management with SQL will enhance your capabilities in this field. Continuous learning and practice are essential to stay updated with evolving technologies.
Yes. You can start learning Python data science on Coursera for free in two ways:
If you want to keep learning, earn a certificate in Python data science, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.
To learn Python data science, start by familiarizing yourself with Python programming basics. Online courses, tutorials, and coding exercises can help you build a solid foundation. Once comfortable with Python, explore data manipulation and analysis libraries like Pandas and NumPy. Engage in hands-on projects to apply your skills, and consider joining online communities for support and networking. Consistent practice and real-world applications will reinforce your learning and boost your confidence.
Typical topics covered in Python data science courses include data cleaning and preprocessing, exploratory data analysis, statistical analysis, data visualization, and machine learning fundamentals. Courses may also introduce specific libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn. By covering these areas, learners gain a comprehensive understanding of how to work with data effectively and derive meaningful insights.
For training and upskilling employees in Python data science, courses like the Python, SQL, Tableau for Data Science Professional Certificate and the Data Science and Analysis Tools - from Jupyter to R Markdown Specialization are excellent choices. These programs are designed to equip teams with practical skills and knowledge, fostering a data-driven culture within organizations.