# 10最好的数据科学教程推荐

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## 10最好的数据科学教程推荐

### 1. Machine Learning A-Z™: Hands-On Python & R In Data Science 经过 “Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team” Udemy课程 我们的最佳选择

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

Welcome to the course! Here we will help you get started in the best conditions.
——————– Part 1: Data Preprocessing ——————–
Data Preprocessing in Python
Data Preprocessing in R
——————– Part 2: Regression ——————–
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
Regression Model Selection in Python
Regression Model Selection in R
——————– Part 3: Classification ——————–
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Classification Model Selection in Python
Evaluating Classification Models Performance
——————– Part 4: Clustering ——————–
K-Means Clustering
Hierarchical Clustering
——————– Part 5: Association Rule Learning ——————–
Apriori
Eclat
——————– Part 6: Reinforcement Learning ——————–
Upper Confidence Bound (UCB)
Thompson Sampling
——————– Part 7: Natural Language Processing ——————–
——————– Part 8: Deep Learning ——————–
Artificial Neural Networks
Convolutional Neural Networks
——————– Part 9: Dimensionality Reduction ——————–
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Kernel PCA
——————– Part 10: Model Selection & Boosting ——————–
Model Selection
XGBoost
Bonus Lectures

### 2. Python for Data Science and Machine Learning Bootcamp 经过 Jose Portilla Udemy课程

“Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!”

Course Introduction
Environment Set-Up
Jupyter Overview
Python Crash Course
Python for Data Analysis – NumPy
Python for Data Analysis – Pandas
Python for Data Analysis – Pandas Exercises
Python for Data Visualization – Matplotlib
Python for Data Visualization – Seaborn
Python for Data Visualization – Pandas Built-in Data Visualization
Python for Data Visualization – Plotly and Cufflinks
Python for Data Visualization – Geographical Plotting
Data Capstone Project
Introduction to Machine Learning
Linear Regression
Cross Validation and Bias-Variance Trade-Off
Logistic Regression
K Nearest Neighbors
Decision Trees and Random Forests
Support Vector Machines
K Means Clustering
Principal Component Analysis
Recommender Systems
Natural Language Processing
Neural Nets and Deep Learning
Big Data and Spark with Python
BONUS SECTION: THANK YOU!

### 3. The Data Science Course 2022: Complete Data Science Bootcamp 经过 “365 Careers, 365 Careers Team” Udemy课程

“Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning”

Part 1: Introduction
The Field of Data Science – The Various Data Science Disciplines
The Field of Data Science – Connecting the Data Science Disciplines
The Field of Data Science – The Benefits of Each Discipline
The Field of Data Science – Popular Data Science Techniques
The Field of Data Science – Popular Data Science Tools
The Field of Data Science – Careers in Data Science
The Field of Data Science – Debunking Common Misconceptions
Part 2: Probability
Probability – Combinatorics
Probability – Bayesian Inference
Probability – Distributions
Probability – Probability in Other Fields
Part 3: Statistics
Statistics – Descriptive Statistics
Statistics – Practical Example: Descriptive Statistics
Statistics – Inferential Statistics Fundamentals
Statistics – Inferential Statistics: Confidence Intervals
Statistics – Practical Example: Inferential Statistics
Statistics – Hypothesis Testing
Statistics – Practical Example: Hypothesis Testing
Part 4: Introduction to Python
Python – Variables and Data Types
Python – Basic Python Syntax
Python – Other Python Operators
Python – Conditional Statements
Python – Python Functions
Python – Sequences
Python – Iterations
Python – Advanced Python Tools
Part 5: Advanced Statistical Methods in Python
Advanced Statistical Methods – Linear Regression with StatsModels
Advanced Statistical Methods – Multiple Linear Regression with StatsModels
Advanced Statistical Methods – Linear Regression with sklearn
Advanced Statistical Methods – Practical Example: Linear Regression
Advanced Statistical Methods – Logistic Regression
Advanced Statistical Methods – Cluster Analysis
Advanced Statistical Methods – K-Means Clustering
Advanced Statistical Methods – Other Types of Clustering
Part 6: Mathematics
Part 7: Deep Learning
Deep Learning – Introduction to Neural Networks
Deep Learning – How to Build a Neural Network from Scratch with NumPy
Deep Learning – TensorFlow 2.0: Introduction
Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
Deep Learning – Overfitting
Deep Learning – Initialization
Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
Deep Learning – Preprocessing
Deep Learning – Classifying on the MNIST Dataset
Deep Learning – Business Case Example
Deep Learning – Conclusion
Appendix: Deep Learning – TensorFlow 1: Introduction
Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset
Appendix: Deep Learning – TensorFlow 1: Business Case
Software Integration
Case Study – What’s Next in the Course?
Case Study – Preprocessing the ‘Absenteeism_data’
Case Study – Applying Machine Learning to Create the ‘absenteeism_module’
Case Study – Loading the ‘absenteeism_module’
Case Study – Analyzing the Predicted Outputs in Tableau
Appendix – Additional Python Tools
Appendix – pandas Fundamentals
Appendix – Working with Text Files in Python
Bonus Lecture

### 4. R Programming A-Z™: R For Data Science With Real Exercises! 经过 “Kirill Eremenko, Ligency I Team, Ligency Team” Udemy课程

“Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2”

Hit The Ground Running
Core Programming Principles
Fundamentals Of R
Matrices
Data Frames
Advanced Visualization With GGPlot2
Homework Solutions
Bonus Tutorials

### 5. Data Science A-Z™: Real-Life Data Science Exercises Included 经过 “Kirill Eremenko, Ligency I Team, Ligency Team” Udemy课程

“Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!”

Get Excited
What is Data Science?
————————— Part 1: Visualisation —————————
Introduction to Tableau
How to use Tableau for Data Mining
Advanced Data Mining With Tableau
————————— Part 2: Modelling —————————
Stats Refresher
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Building a robust geodemographic segmentation model
Assessing your model
Drawing insights from your model
Model maintenance
————————— Part 3: Data Preparation —————————
Business Intelligence (BI) Tools
ETL Phase 1: Data Wrangling before the Load
ETL Phase 2: Step-by-step guide to uploading data using SSIS
Handling errors during ETL (Phases 1 & 2)
SQL Programming for Data Science
ETL Phase 3: Data Wrangling after the load
Handling errors during ETL (Phase 3)
————————— Part 4: Communication —————————
Working with people
Presenting for Data Scientists
Homework Solutions
Bonus Lectures

### 6. “Machine Learning, Data Science and Deep Learning with Python” 经过 “Sundog Education by Frank Kane, Frank Kane, Sundog Education Team” Udemy课程

“Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks”

“Getting Started
Statistics and Probability Refresher, and Python Practice
Predictive Models
Machine Learning with Python
Recommender Systems
More Data Mining and Machine Learning Techniques
Dealing with Real-World Data
Apache Spark: Machine Learning on Big Data
Experimental Design / ML in the Real World
Deep Learning and Neural Networks
Generative Models
Final Project
You made it!”

### 7. Statistics for Data Science and Business Analysis 经过 “365 Careers, 365 Careers Team” Udemy课程

“Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis”

“Introduction
Sample or population data?
The fundamentals of descriptive statistics
Measures of central tendency, asymmetry, and variability
Practical example: descriptive statistics
Distributions
Estimators and estimates
Confidence intervals: advanced topics
Practical example: inferential statistics
Hypothesis testing: Introduction
Hypothesis testing: Let’s start testing!
Practical example: hypothesis testing
The fundamentals of regression analysis
Subtleties of regression analysis
Assumptions for linear regression analysis
Dealing with categorical data
Practical example: regression analysis
Bonus lecture”

### 8. Python A-Z™: Python For Data Science With Real Exercises! 经过 “Kirill Eremenko, Ligency I Team, Ligency Team” Udemy课程

“Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization”

Welcome To The Course
Core Programming Principles
Fundamentals Of Python
Matrices
Data Frames
Advanced Visualization
Homework Solutions
Bonus Lectures

### 9. Data Science and Machine Learning Bootcamp with R 经过 Jose Portilla Udemy课程

Learn how to use the R programming language for data science and machine learning and data visualization!

Course Introduction
Course Best Practices
Windows Installation Set-Up
Mac OS Installation Set-Up
Linux Installation
Development Environment Overview
Introduction to R Basics
R Matrices
R Data Frames
R Lists
Data Input and Output with R
R Programming Basics
Advanced R Programming
Data Manipulation with R
Data Visualization with R
Data Visualization Project
Interactive Visualizations with Plotly
Capstone Data Project
Introduction to Machine Learning with R
Machine Learning with R – Linear Regression
Machine Learning Project – Linear Regression
Machine Learning with R – Logistic Regression
Machine Learning Project – Logistic Regression
Machine Learning with R – K Nearest Neighbors
Machine Learning Project – K Nearest Neighbors
Machine Learning with R – Decision Trees and Random Forests
Machine Learning Project – Decision Trees and Random Forests
Machine Learning with R – Support Vector Machines
Machine Learning Project – Support Vector Machines
Machine Learning with R – K-means Clustering
Machine Learning Project – K-means Clustering
Machine Learning with R – Natural Language Processing
Machine Learning with R – Neural Nets
Machine Learning Project – Neural Nets
Bonus Section

### 10. Complete Machine Learning & Data Science Bootcamp 2022 经过 “Andrei Neagoie, Daniel Bourke, Zero To Mastery” Udemy课程

“Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!”

“Introduction
Machine Learning 101
Machine Learning and Data Science Framework
The 2 Paths
Data Science Environment Setup
Pandas: Data Analysis
NumPy
Matplotlib: Plotting and Data Visualization
Scikit-learn: Creating Machine Learning Models
Supervised Learning: Classification + Regression
Milestone Project 1: Supervised Learning (Classification)
Milestone Project 2: Supervised Learning (Time Series Data)
Data Engineering
Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2
Storytelling + Communication: How To Present Your Work
Career Advice + Extra Bits
Learn Python
Learn Python Part 2
Extra: Learn Advanced Statistics and Mathematics for FREE!
Where To Go From Here?
BONUS SECTION”

## 下面是一些关于学习的常见问题数据科学

### 学习数据科学需要多长时间？

“学习数据科学需要多长时间”这个问题的答案是。 . .这取决于。每个人都有不同的需求，每个人都在不同的场景下工作，所以一个人的答案可能与另一个人的答案完全不同。