Data Science

Data Science Curriculum

入门数据科学的基本主题

1.数学基础

(I) Multivariable Calculus

大多数机器学习模型都是使用具有多个功能或预测变量的数据集构建的。因此，熟悉多变量演算对于建立机器学习模型极为重要。这是您需要熟悉的主题：

Functions of several variables
Derivatives and gradients
步骤功能，sigmoid函数，logit函数，relu（整流线性单元）函数
成本功能
Plotting of functions
Minimum and Maximum values of a function

（ii）线性代数

线性代数是机器学习中最重要的数学技能。数据集表示为矩阵。线性代数用于数据预处理，数据转换和模型评估。这是您需要熟悉的主题：

Vectors
Matrices
基质的转置
The inverse of a matrix
The determinant of a matrix
点产品
特征值
Eigenvectors

（iii）优化方法

大多数机器学习算法通过最小化目标函数来执行预测建模，从而学习必须应用于测试数据以获得预测标签的权重。这是您需要熟悉的主题：

成本功能/目标功能
Likelihood function
错误函数
Gradient Descent Algorithm and its variants (e.g., Stochastic Gradient Descent Algorithm)

2. Programming Basics

Python and R are considered the top programming languages for data science. You may decide to focus on just one language. Python is widely adopted by industries and academic training programs. As a beginner, it is recommended that you focus on one language only.

Here are some Python and R basics topics to master:

Basic R syntax
基础R编程概念，例如数据类型，矢量算术，索引和数据框架
如何在R中执行操作，包括分类，使用DPLYR进行数据争吵以及使用GGPLOT2可视化数据
R Studio
Object-oriented programming aspects of Python
Jupyter笔记本
Be able to work with Python libraries such as NumPy, pylab, seaborn, matplotlib, pandas, scikit-learn, TensorFlow, PyTorch

3. Data Basics

了解如何以各种格式操纵数据，例如CSV文件，PDF文件，文本文件等。了解如何清洁数据，估算数据，扩展数据，导入和导出数据以及从Internet进行废除数据。一些感兴趣的软件包是熊猫，numpy，pdf工具，字符串等。此外，R和Python包含几个可用于练习的内置数据集。学习数据转换和降低降低技术，例如协方差矩阵图，主成分分析（PCA）和线性判别分析（LDA）。

4. Probability and Statistics Basics

Statistics and Probability is used for visualization of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with:

意思是
Median
模式
标准偏差/差异
Correlation coefficient and the covariance matrix
Probability distributions (Binomial, Poisson, Normal)
p值
Baye’s Theorem (Precision, Recall, Positive Predictive Value, Negative Predictive Value, Confusion Matrix, ROC Curve)
A/B Testing
蒙特卡洛模拟

5. Data Visualization Basics

学习良好数据可视化的基本组成部分。良好的数据可视化由几个组件组成，这些组件必须拼凑在一起以生成最终产品：

A）数据组件: An important first step in deciding how to visualize data is to know what type of data it is, e.g., categorical data, discrete data, continuous data, time-series data, etc.

b)几何组件：在这里，您可以在其中确定哪种可视化适合您的数据，例如散点图，线图，条图，直方图，Q-Q图，光滑的密度，盒子图，配对图，热图等。

c)Mapping Component:Here, you need to decide what variable to use as your x-variable and what to use as your y-variable. This is important, especially when your dataset is multi-dimensional with several features.

d)比例组件：在这里，您可以决定要使用哪种尺度，例如线性比例，日志刻度等。

e)标签组件：这包括轴标签，标题，图例，使用字体大小等内容。

f)Ethical Component: Here, you want to make sure your visualization tells the true story. You need to be aware of your actions when cleaning, summarizing, manipulating, and producing a data visualization and ensure you aren’t using your visualization to mislead or manipulate your audience.

Important data visualization tools include Python’s matplotlib and seaborn packages, and R’s ggplot2 package.

6.线性回归基础知识

Learn the fundamentals of simple and multiple linear regression analysis. Linear regression is used for supervised learning with continuous outcomes. Some tools for performing linear regression are given below:

Python: NumPy, pylab, sci-kit-learn

R：凯特包裹

7.机器学习基础知识

A）Supervised Learning (Continuous Variable Prediction)

基本回归
多回归分析
正则回归

b) Supervised Learning (Discrete Variable Prediction)

Logistic Regression Classifier
Support Vector Machine (SVM) Classifier
K-Nearest邻居（KNN）分类器
Decision Tree Classifier
Random Forest Classifier
天真的贝叶斯

c）无监督的学习

Kmeans clustering algorithm

用于机器学习的Python工具：Scikit-Learn，Pytorch，TensorFlow。

8. Time Series Analysis Basics

在结果取决于时间依赖的情况下，例如预测股票价格。有3种分析时间序列数据的基本方法：

指数平滑
ARIMA (Auto-Regressive Integrated Moving Average), which is a generalization of exponential smoothing
GARCH（广义自动回归条件异方差），这是一个类似Arima的模型，用于分析方差。

These 3 techniques can be implemented in Python and R.

9. Productivity Tools Basics

Knowledge on how to use basic productivity tools such as R studio, Jupyter notebook, and GitHub, is essential. For Python, Anaconda Python is the best productivity tool to install. Advanced productivity tools such as AWS and Azure are also important tools to learn.