This course takes a step-by-step approach to the process of building robust models to predict real-world outcomes and uncover valuable insights from your data. You’ll start with a solid foundation in probability and statistical distributions, learning how to estimate parameters and fit models using industry-standard libraries such as SciPy and NumPy. You'll dive into the theory and practice of regression analysis, learning about modeling correlations and interpreting coefficients for actionable business intelligence. Beyond model building, you’ll gain critical skills in evaluating model performance, troubleshooting common pitfalls, and understanding the nuanced differences between statistics, modeling, and machine learning. By the end of the course, you’ll confidently leverage Scikit-learn to implement predictive algorithms, distinguish between inference and prediction, and apply your knowledge to solve complex, real-world problems.
Data Science Fundamentals Part 2: Unit 3
本课程是 Data Science Fundamentals, Part 2 专项课程 的一部分
位教师：Pearson
包含在 
中
您将学到什么
Build and evaluate statistical models to predict outcomes using Python libraries such as SciPy, NumPy, and Scikit-learn.
Understand and apply the fundamentals of probability, statistical distributions, and regression analysis.
Identify and overcome common challenges in model fitting and performance evaluation.
Distinguish between statistical inference and prediction, and leverage machine learning algorithms for real-world applications.
您将获得的技能
- Machine Learning
- Probability Distribution
- Probability & Statistics
- Data Analysis
- Regression Analysis
- Supervised Learning
- Business Analytics
- Statistical Analysis
- Estimation
- Predictive Analytics
- Statistical Modeling
- Scikit Learn (Machine Learning Library)
- Predictive Modeling
- Performance Metric
- Statistical Inference
要了解的详细信息
添加到您的领英档案
August 2025
2 项作业
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有1个模块
This module introduces the fundamentals of statistical modeling and machine learning using Python. You’ll learn to analyze Airbnb listing data, starting with probability and statistical distributions, then progress to parameter estimation and regression analysis. The module covers building and evaluating predictive models, understanding model performance, and overcoming common challenges. You’ll also explore the distinctions between statistics, modeling, and machine learning, and gain hands-on experience with Scikit-learn to make predictions. By the end, you’ll know how to create, interpret, and assess statistical models for real-world data analysis and prediction tasks.
涵盖的内容
24个视频2个作业
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常见问题
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
更多问题
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