學習大數據 (big data) 的技能

一些工具或念個學位

可以參考 DS Examiner, Data Scientist Foundations: The Hard and Human Skills You Need, November 8, 2013

或者  Insight Data Science Fellows Program 說明了可能使用的工具

1. Software Engineering Best Practices: Learn how to contribute to a large code-base and instrument a web application to collect data. Tools you may learn: Python, Git, LAMP web stack, Javascript, Flask.
2. Storing and Retrieving Data: How to clean data, store it in the appropriate database or distributed data storage system and then run queries to retrieve the information needed for analysis. Tools you may will learn: MySQL, Hadoop, Hive.
3. Statistical Analysis & Machine Learning: Learn industry best practices for doing basic and advanced statistical analysis on large data sets. Tools you may learn: R, NumPy & SciPy, Mahout.
4. Visualizing and Communicating Results: Learn how to effectively communicate your findings visually and verbally. Tools you may learn: D3 Javascript library, visualization and presentation best practices. 

當然，如果大學時的基礎夠好，也可以自學，例如

• 微積分和線性代數： 
• Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics for Machine Learning
• 機率和統計：
• Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R
• Ron Kohavi, Diane Tang, and Ya Xu, Trustworthy Online Controlled Experiments
• Larry Wasserman, All of Statistics 
• 最佳化 (optimization) 或作業研究：
• Pedro Domingos, A few useful things to know about machine learning, Communications of the ACM, 2012.
• Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning
• Kevin Patrick Murphy, Probabilistic Machine Learning: An Introduction
• 實現系統所需的工具：程式設計 (Python, R)，資料庫
• John V Guttag, Introduction to computation and programming using Python: with application to computational modeling and understanding data
• Robert Johansson, Numerical Python: Scientific computing and data science applications with Numpy, SciPy and Matplotlib (CYCU 電子書)
• 資料結構和演算法：
• Bradley Efron and Trevor Hastie, Computer Age Statistical Inference: Algorithms, Evidence and Data Science
• Jure Leskovec, Anand Rajaraman, and Jeff Ullman, Mining of Massive Datasets

可以念個碩士，以 UT Austin 為例，美國德州州民學雜費 $32,000 美元、其他人 (含國際學生) $38,000 美元 (註 2)，一年修完 12 門課即可，例如

MIS 381N: Data Analytics Programming
STA 380.17: Introduction to Predictive Modeling
MIS 381N: Decision Analysis
BA 385T: Financial Management
MIS 381N.1: Introduction to Database Management
MIS 382N: Advanced Predictive Modeling
BA 191: Career Services Strategies- MSBA
MIS 381N: Stochastic Control & Optimization
STA 380.18: Learning Structures & Time Series
MIS 382N.11: Business Analytics Capstone
MIS 182N: Data Visualization

或者 UC Berkeley 的 Master of Information and Data Science 

Research Design and Application for Data and Analysis
Exploring and Analyzing Data
Storing and Retrieving Data
Applied Machine Learning
Visualizing and Communicating Data
Experiments and Experimentation with Data
Privacy, Security, and Ethics of Data
Really Big Data: Scaling up and Parallelism
Synthetic Capstone Course

或者 Columbia 的 Certification of Professional Achievement in Data Sciences

Algorithms for Data Science
Probability & Statistics
Machine Learning for Data Science
Exploratory Data Analysis and Visualization

或者 Stanford 的 Data Mining and Applications Graduate Certificate

STATS202 Data Mining and Analysis
STATS216 Introduction to Statistical Learning
STATS290 Paradigms for Computing with Data
STATS315B Modern Applied Statistics: Data Mining

或者 CMU 的 Master of Science in Machine Learning

Machine Learning (10-701)
Statistical Machine Learning (10-702)
Intermediate Statistics (10-705)
5 選 2：Multimedia Databases (15-826), Algorithms (15-750) or Algorithms in the Real World (15-853), Optimization (10-725), or Graphical Models (10-708).
3 門選修

或者 MIT 的 Master of Business Analytics ，一年完成，學費 75000 美金。　　

15.093J/6.255J: Optimization Methods
15.079: Introduction Applied Probability or 6.431 Applied Probability
15.062: Data mining: Finding the Data and Models that Create Value
15.572: Analytics Lab
15.071: The Analytics Edge
Analytics Project Course
three focused electives in E-Commerce, Finance, Managerial Economics, Marketing, OM, OR/Statistics 

或者到 Coursera 搜尋 data，可以找到許多免費的課程，例如 Introduction to Data Science、Web Intelligence and Big Data 等等。MOOC 上有許多優良的課程，個人推薦 edx 上 Dimitris Bertsimas 和 Allison O’Hair 的 The Analytics Edge、Coursera 上 Andrew Ng 的  Machine Learning、和 Stanford 上 Trevor Hastie 和 Rob Tibshirani 的 Statistical Learning。

(註 1) 以中央銀行新臺幣 / 美元銀行間收盤匯率的 29 計算，一門課的費用是 91,833 台幣 (38,000 * 29 / 12)。