關於此課程：Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Data sets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.
此課程適用人群：this course is geared for anyone with basic programming skills and has a knowledge of general mathematics
教學方： Joseph Santarcangelo, Ph.D., Data Scientist at IBM
| 基本信息 | 課程 3（共 4 門，Applied Data Science Specialization ） |
| 級別 | 初級 |
| 承諾學習時間 | This course requires approximately two hours a week for six weeks |
| 語言 | English |
| 如何通過 | 通過所有計分作業以完成課程。 |
| 用戶評分 |
- 視頻: Understanding the Data
- 視頻: Python Packages for Data Science
- 視頻: Importing and Exporting Data in Python
- 視頻: Getting Started Analyzing Data in Python
- LTI Item: Lab 1:Importing Datasets
- 視頻: Dealing with Missing Values in Python
- 視頻: Data Formatting in Python
- 視頻: Data Normalization in Python
- 視頻: Binning in Python
- 視頻: Turning categorical variables into quantitative variables in Python
- LTI Item: Data Wrangling
- 視頻: Descriptive Statistics
- 視頻: GroupBy in Python
- 視頻: Analysis of Variance ANOVA
- 視頻: Correlation
- 視頻: Correlation - Statistics
- LTI Item: Exploratory Data Analysis
- 視頻: Linear Regression and Multiple Linear Regression
- 視頻: Model Evaluation using Visualization
- 視頻: Polynomial Regression and Pipelines
- 視頻: Measures for In-Sample Evaluation
- 視頻: Prediction and Decision Making
- LTI Item: Model Development
- 視頻: Overfitting, Underfitting and Model Selection
- 視頻: Ridge Regression
- 視頻: Grid Search
- LTI Item: Model Evaluation and Refinement
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| 購買課程 | |
|---|---|
| 訪問課程材料 | 可用 |
| 訪問評分的材料 | 可用 |
| 收到最終成績 | 可用 |
| 獲得可共享的課程證書 | 可用 |
This course is very good start for students who are planning to go into machine learning specifically.Students who have no Idea about regression and math find bit hard but little more effort from student side is needed. At the end you will have a zeroth tool for machine learning.
The course is very interesting and concise, it has a very logical flow. The best parts about the course are quiz embedded in the lectures and detailed lab assignments. However, there are few errors in the lab and assignments, which need to be rectified. Otherwise, it would have been 5star from me. Thank You for desiging this course.
This course is very helpful as it is more industry oriented. I have personally found this course to be very smooth and beneficial for learning.
greatcourse aerg