说明
分类：kaiwu
点击数：490

《盗梦空间》（Inception）

Christopher Nolan (Producer, Writer, Director), & Emma Thomas (Producer). (2010). Inception [Film]. Warner Bros., Syncopy. USA.

克里斯托弗.诺兰(导演、编剧、制片)、 艾玛.托马斯 (制片). (2010). 盗梦空间 [电影]. 华纳兄弟娱乐公司. 美国

1.影评网站

1.1 豆瓣网
https://movie.douban.com/subject/3541415/
https://site.douban.com/106310/

 

No.9豆瓣电影Top250

盗梦空间 Inception (2010)

导演: 克里斯托弗·诺兰
编剧: 克里斯托弗·诺兰
主演: 莱昂纳多·迪卡普里奥 / 约瑟夫·高登-莱维特 / 艾利奥特·佩吉 / 汤姆·哈迪 / 渡边谦 / 更多...
类型: 剧情 / 科幻 / 悬疑 / 冒险
制片国家/地区: 美国 / 英国
语言: 英语 / 日语 / 法语
上映日期: 2010-09-01(中国大陆) / 2020-08-28(中国大陆重映) / 2010-07-16(美国)
片长: 148分钟
又名: 潜行凶间(港) / 全面启动(台) / 奠基 / 心灵犯案 / 记忆迷阵 / 记忆魔方
IMDb链接: tt1375666
官方小站: 盗梦空间-Inception
inception douban
 

1.2 imdb
https://www.imdb.com/title/tt1375666/

inception imb

1.3 allmovie

https://www.allmovie.com/movie/v480818

 

1.4 Rotten Tomatoes

https://www.rottentomatoes.com/m/inception

Dom Cobb (Leonardo DiCaprio) is a thief with the rare ability to enter people's dreams and steal their secrets from their subconscious. His skill has made him a hot commodity in the world of corporate espionage but has also cost him everything he loves. Cobb gets a chance at redemption when he is offered a seemingly impossible task: Plant an idea in someone's mind. If he succeeds, it will be the perfect crime, but a dangerous enemy anticipates Cobb's every move.

 

Rating:PG-13 (Sequences of Violence|Sequences of Action) 
Genre:Sci Fi, Mystery And Thriller, Action 
Original Language:English 
Director:Christopher Nolan 
Producer:Christopher Nolan, Emma Thomas 
Writer:Christopher Nolan

 Release Date (Theaters):  Wide 

Release Date (Streaming): 
Box Office (Gross USA):$292.6M 
Runtime: 
Production Co:Warner Bros., Syncopy 
Sound Mix:DTS, Dolby Digital, SDDS 
Aspect Ratio:Scope (2.35:1)

2.播放网址
2.1 腾讯视频
https://v.qq.com/x/cover/h0meep6p766jgqh.html

2.2 优酷视频
https://v.youku.com/v_show/id_XMjI4OTQ0MDky.html

2.3 哔哩哔哩
https://www.bilibili.com/bangumi/play/ss28586 

2.4 爱奇艺
https://www.iqiyi.com/v_19rra64i9c.html

 

 3.相关书籍

Botz-Bornstein, T. (2011). Inception and Philosophy: Ideas to Die For. Open Court. https://www.amazon.com/dp/0812697332
cover inception and philosophy2011
 
 
Johnson, D.  (2012). Inception and Philosophy: Because It’s Never Just a Dream. Wiley. https://www.amazon.com/dp/1118072634
cover inception and philosophy2011ideas to die for
 
 

说明
分类：kaiwu
点击数：66
CTABLES
  /VLABELS VARIABLES=MR1 MR2 MR3 MR4 MR5 MR6  gender DISPLAY=BOTH
  /TABLE imd1[MEAN F40.2]+ imd2[MEAN F40.2]+ imd3[MEAN F40.2]+ imd4[MEAN F40.2]+ imd5[MEAN F40.2]+ imd6[MEAN F40.2]   BY gender [C] 
  /CATEGORIES VARIABLES= gender ORDER=A KEY=VALUE EMPTY=INCLUDE
  /CRITERIA CILEVEL=95.
---------
　 gender 性别 　
　 男 女
imd1 3.05 3.35
imd2 3.28 3.55
imd3 4.11 4.11
imd4 3.92 3.85
imd5 3.67 3.76
imd6 3.87 3.91

* 包含了卡方检验-------------频数表.
CTABLES
  /VLABELS VARIABLES=nage_range newincome edu gender  DISPLAY=BOTH
  /TABLE nage_range + newincome +  edu   BY gender [C] [COUNT F40.0]
  /CATEGORIES VARIABLES=nage_range newincome edu ORDER=A KEY=VALUE EMPTY=INCLUDE
  /CRITERIA CILEVEL=95
  /SIGTEST TYPE=CHISQUARE ALPHA=0.05 INCLUDEMRSETS=YES CATEGORIES=ALLVISIBLE.
　 　 gender 性别 　
　 　 男 女
年龄段 25岁以下 48 86
　 26~35 31 54
　 36~45 50 50
　 46~55 30 20
　 56岁以上 15 4
 收入段 2000元以下 34 66
　 2001-4000元 32 46
　 4001-6000元 60 50
　 6001-8000元 18 28
　 8000元以上 30 24
受教育程度 初中及以下 18 2
　 高中 17 12
　 大专或高职 30 34
　 本科 75 124
　 硕士研究生 22 30
　 博士研究生 12 12
* 人口统计变量的分组比较——行百分比.
CTABLES
  /VLABELS VARIABLES=nage_range newincome edu gender  DISPLAY=BOTH
  /TABLE nage_range + newincome +  edu   BY gender [C][ROWPCT.COUNT ]
  /CATEGORIES VARIABLES=nage_range newincome edu ORDER=A KEY=VALUE EMPTY=INCLUDE
  /CRITERIA CILEVEL=95
  /SIGTEST TYPE=CHISQUARE ALPHA=0.05 INCLUDEMRSETS=YES CATEGORIES=ALLVISIBLE.
　 　 gender 性别 　
　 　 男 女
　 　 行 N % 行 N %
年龄段 25岁以下 35.8% 64.2%
　 26~35 36.5% 63.5%
　 36~45 50.0% 50.0%
　 46~55 60.0% 40.0%
　 56岁以上 78.9% 21.1%
 收入段 2000元以下 34.0% 66.0%
　 2001-4000元 41.0% 59.0%
　 4001-6000元 54.5% 45.5%
　 6001-8000元 39.1% 60.9%
　 8000元以上 55.6% 44.4%
受教育程度 初中及以下 90.0% 10.0%
　 高中 58.6% 41.4%
　 大专或高职 46.9% 53.1%
　 本科 37.7% 62.3%
　 硕士研究生 42.3% 57.7%
　 博士研究生 50.0% 50.0%
* 人口统计变量的分组比较——列百分比.
CTABLES
  /VLABELS VARIABLES=nage_range newincome edu gender  DISPLAY=BOTH
  /TABLE nage_range + newincome +  edu   BY gender [C][COLPCT.COUNT ]
  /CATEGORIES VARIABLES=nage_range newincome edu ORDER=A KEY=VALUE EMPTY=INCLUDE
  /CRITERIA CILEVEL=95
  /SIGTEST TYPE=CHISQUARE ALPHA=0.05 INCLUDEMRSETS=YES CATEGORIES=ALLVISIBLE.
 
　 　 gender 性别 　
　 　 男 女
　 　 列 N % 列 N %
 年龄段 25岁以下 27.6% 40.2%
　 26~35 17.8% 25.2%
　 36~45 28.7% 23.4%
　 46~55 17.2% 9.3%
　 56岁以上 8.6% 1.9%
 收入段 2000元以下 19.5% 30.8%
　 2001-4000元 18.4% 21.5%
　 4001-6000元 34.5% 23.4%
　 6001-8000元 10.3% 13.1%
　 8000元以上 17.2% 11.2%
 受教育程度 初中及以下 10.3% 0.9%
　 高中 9.8% 5.6%
　 大专或高职 17.2% 15.9%
　 本科 43.1% 57.9%
　 硕士研究生 12.6% 14.0%
　 博士研究生 6.9% 5.6%
* 人口统计变量的分组比较——全表百分比.
CTABLES
  /VLABELS VARIABLES=nage_range newincome edu gender  DISPLAY=BOTH
      /TABLE nage_range + newincome +  edu   BY gender [C][TABLEPCT.COUNT ]
  /CATEGORIES VARIABLES=nage_range newincome edu ORDER=A KEY=VALUE EMPTY=INCLUDE
  /CRITERIA CILEVEL=95
  /SIGTEST TYPE=CHISQUARE ALPHA=0.05 INCLUDEMRSETS=YES CATEGORIES=ALLVISIBLE.
 
　 　 gender 性别 　
　 　 男 女
　 　 表 N % 表 N %
 年龄段 25岁以下 12.4% 22.2%
　 26~35 8.0% 13.9%
　 36~45 12.9% 12.9%
　 46~55 7.7% 5.2%
　 56岁以上 3.9% 1.0%
收入段 2000元以下 8.8% 17.0%
　 2001-4000元 8.2% 11.9%
　 4001-6000元 15.5% 12.9%
　 6001-8000元 4.6% 7.2%
　 8000元以上 7.7% 6.2%
受教育程度 初中及以下 4.6% 0.5%
　 高中 4.4% 3.1%
　 大专或高职 7.7% 8.8%
　 本科 19.3% 32.0%
　 硕士研究生 5.7% 7.7%
　 博士研究生 3.1% 3.1%
Function
Description
Default Label*
Default Format
COUNT
Number of cases in each category. This is the default for categorical and multiple response variables.
Count
Count
ROWPCT.COUNT
Row percentage based on cell counts. Computed within subtable.
Row %
Percent
COLPCT.COUNT
Column percentage based on cell counts. Computed within subtable.
Column %
Percent
TABLEPCT.COUNT
Table percentage based on cell counts.
Table %
Percent
SUBTABLEPCT.COUNT
Subtable percentage based on cell counts.
Subtable %
Percent
LAYERPCT.COUNT
Layer percentage based on cell counts. Same as table percentage if no layers are defined.
Layer %
Percent
LAYERROWPCT.COUNT
Row percentage based on cell counts. Percentages sum to 100% across the entire row (that is, across subtables).
Layer Row %
Percent
LAYERCOLPCT.COUNT
Column percentage based on cell counts. Percentages sum to 100% across the entire column (that is, across subtables).
Layer Column %
Percent
ROWPCT.VALIDN
Row percentage based on valid count.
Row Valid N %
Percent
COLPCT.VALIDN
Column percentage based on valid count.
Column Valid N %
Percent
TABLEPCT.VALIDN
Table percentage based on valid count.
Table Valid N %
Percent
SUBTABLEPCT.VALIDN
Subtable percentage based on valid count.
Subtable Valid N %
Percent
LAYERPCT.VALIDN
Layer percentage based on valid count.
Layer Valid N %
Percent
LAYERROWPCT.VALIDN
Row percentage based on valid count. Percentages sum to 100% across the entire row.
Layer Row Valid N %
Percent
LAYERCOLPCT.VALIDN
Column percentage based on valid count. Percentages sum to 100% across the entire column.
Layer Column Valid N %
Percent
ROWPCT.TOTALN
Row percentage based on total count, including user-missing and system-missing values.
Row Total N %
Percent
COLPCT.TOTALN
Column percentage based on total count, including user-missing and system-missing values.
Column Total N %
Percent
TABLEPCT.TOTALN
Table percentage based on total count, including user-missing and system-missing values.
Table Total N %
Percent
SUBTABLEPCT.TOTALN
Subtable percentage based on total count, including user-missing and system-missing values.
Subtable Total N %
Percent
LAYERPCT.TOTALN
Layer percentage based on total count, including user-missing and system-missing values.
Layer Total N %
Percent
LAYERROWPCT.TOTALN
Row percentage based on total count, including user-missing and system-missing values. Percentages sum to 100% across the entire row.
Layer Row Total N %
Percent
LAYERCOLPCT.TOTALN
Column percentage based on total count, including user-missing and system-missing values. Percentages sum to 100% across the entire column.
Layer Column Total N %
Percent
 
*This is the default on a U.S.-English system.
The.COUNTsuffix can be omitted from percentages that are based on cell counts. Thus,ROWPCTis equivalent toROWPCT.COUNT.
 
Function
Description
Default Label
Default Format
MAXIMUM
Largest value.
Maximum
General
MEAN
Arithmetic mean. The default for scale variables.
Mean
General
MEDIAN
50th percentile.
Median
General
MINIMUM
Smallest value.
Minimum
General
MISSING
Count of missing values (both user-missing and system-missing).
Missing
General
MODE
Most frequent value. If there is a tie, the smallest value is shown.
Mode
General
PTILE
Percentile. Takes a numeric value between 0 and 100 as a required parameter. PTILE is computed the same way as APTILE in the TABLES command. Note that in the TABLES command, the default percentile method was HPTILE.
Percentile ####.##
General
RANGE
Difference between maximum and minimum values.
Range
General
SEMEAN
Standard error of the mean.
Std Error of Mean
General
STDDEV
Standard deviation.
Std Deviation
General
SUM
Sum of values.
Sum
General
TOTALN
Count of nonmissing, user-missing, and system-missing values. The count excludes valid values hidden via the CATEGORIES subcommand.
Total N
Count
VALIDN
Count of nonmissing values.
Valid N
Count
VARIANCE
Variance.
Variance
General
ROWPCT.SUM
Row percentage based on sums.
Row Sum %
Percent
COLPCT.SUM
Column percentage based on sums.
Column Sum %
Percent
TABLEPCT.SUM
Table percentage based on sums.
Table Sum %
Percent
SUBTABLEPCT.SUM
Subtable percentage based on sums.
Subtable Sum %
Percent
LAYERPCT.SUM
Layer percentage based on sums.
Layer Sum %
Percent
LAYERROWPCT.SUM
Row percentage based on sums. Percentages sum to 100% across the entire row.
Layer Row Sum %
Percent
LAYERCOLPCT.SUM
Column percentage based on sums. Percentages sum to 100% across the entire column.
Layer Column Sum %
Percent
 
 
Function
Description
Default Label
Default Format
RESPONSES
Count of responses.
Responses
Count
ROWPCT.RESPONSES
Row percentage based on responses. Total number of responses is the denominator.
Row Responses %
Percent
COLPCT.RESPONSES
Column percentage based on responses. Total number of responses is the denominator.
Column Responses %
Percent
TABLEPCT.RESPONSES
Table percentage based on responses. Total number of responses is the denominator.
Table Responses %
Percent
SUBTABLEPCT.RESPONSES
Subtable percentage based on responses. Total number of responses is the denominator.
Subtable Responses %
Percent
LAYERPCT.RESPONSES
Layer percentage based on responses. Total number of responses is the denominator.
Layer Responses %
Percent
LAYERROWPCT.RESPONSES
Row percentage based on responses. Total number of responses is the denominator. Percentages sum to 100% across the entire row (that is, across subtables).
Layer Row Responses %
Percent
LAYERCOLPCT.RESPONSES
Column percentage based on responses. Total number of responses is the denominator. Percentages sum to 100% across the entire column (that is, across subtables).
Layer Column Responses %
Percent
ROWPCT.RESPONSES.COUNT
Row percentage: Responses are the numerator, and total count is the denominator.
Row Responses % (Base: Count)
Percent
COLPCT.RESPONSES.COUNT
Column percentage: Responses are the numerator, and total count is the denominator.
Column Responses % (Base: Count)
Percent
TABLEPCT.RESPONSES.COUNT
Table percentage: Responses are the numerator, and total count is the denominator.
Table Responses % (Base: Count)
Percent
SUBTABLEPCT.RESPONSES.COUNT
Subtable percentage: Responses are the numerator, and total count is the denominator.
Subtable Responses % (Base: Count)
Percent
LAYERPCT.RESPONSES.COUNT
Layer percentage: Responses are the numerator, and total count is the denominator.
Layer Responses % (Base: Count)
Percent
LAYERROWPCT.RESPONSES.COUNT
Row percentage: Responses are the numerator, and total count is the denominator. Percentages sum to 100% across the entire row (that is, across subtables).
Layer Row Responses % (Base: Count)
Percent
LAYERCOLPCT.RESPONSES.COUNT
Column percentage: Responses are the numerator, and total count is the denominator. Percentages sum to 100% across the entire column (that is, across subtables).
Layer Column Responses % (Base: Count)
Percent
ROWPCT.COUNT.RESPONSES
Row percentage: Count is the numerator, and total responses are the denominator.
Row Count % (Base: Responses)
Percent
COLPCT.COUNT.RESPONSES
Column percentage: Count is the numerator, and total responses are the denominator.
Column Count % (Base: Responses)
Percent
TABLEPCT.COUNT.RESPONSES
Table percentage: Count is the numerator, and total responses are the denominator.
Table Count % (Base: Responses)
Percent
SUBTABLEPCT.COUNT. RESPONSES
Subtable percentage: Count is the numerator, and total responses are the denominator.
Subtable Count % (Base: Responses)
Percent
LAYERPCT.COUNT. RESPONSES
Layer percentage: Count is the numerator, and total responses are the denominator.
Layer Count % (Base: Responses)
Percent
LAYERROWPCT.COUNT.RESPONSES
Row percentage: Count is the numerator, and total responses are the denominator. Percentages sum to 100% across the entire row (that is, across subtables).
Layer Row Count % (Base: Responses)
Percent
LAYERCOLPCT.COUNT.RESPONSES
Row percentage: Count is the numerator, and total responses are the denominator. Percentages sum to 100% across the entire column (that is, across subtables).
Layer Column Count % (Base: Responses)
Percent
 

 

 

说明
分类：kaiwu
点击数：625

 现在网络公开课很多,例如网易公开课（open.163.com）、新浪公开课（open.sina.com.cn/）等，中国教育和科研计算机网CERNET推出的一个网络公开课导航专题（www.edu.cn/html/opencourse），国内著名的平台还有：超星（http://i.mooc.chaoxing.com/）、智慧树（https://www.zhihuishu.com/）、雨课堂（https://www.yuketang.cn/）等。

国外专业公开课网站如下：

online education2020

（1）Coursera

 https://www.coursera.org/ 

2012年由斯坦福计算机科学教授Andrew Ng 和 Daphne Koller 共同创立，可以选择免费学习，也可以选择购买一个certificate以证明你学过这门课。

（2）edX

 https://www.edx.org/ 

edX 于2012年5月由麻省理工和哈佛大学联合创办，致力于提供学生和其他社会机构尖端的科技教育和严谨的课程学习。

（3）Study

http://study.com/ 

该网站除了可以通过课程内容、学校筛选课程，还可以通过学历等级（难度）来筛选。

（4）khan academy

https://www.khanacademy.org/

（5）udemy

https://www.udemy.com/

（6）Udacity

 https://www.udacity.com/

（7）P2PU

https://www.p2pu.org/en/

（8）lynda

https://www.lynda.com/

（9）linkedin Learning

https://www.linkedin.com/learning/

（10）TEDEd

https://ed.ted.com/educator

（11）codeacademy

https://www.codecademy.com/

（12）Datacamp

https://www.datacamp.com/

 https://ibleducation.com/the-lms-market-doesnt-grow-and-continues-to-be-dominated-by-the-big-four/

the big four: canvas, skai, moodle and blackboard

phil LMS2019

 

 

说明
分类：kaiwu
点击数：1503

 

1.Decision Tree for selecting statistical Procedures

Ritchey, F. (2007). The Statistical Imagination:  Elementary Statistics for the Social Sciences. McGraw-Hill Education. Retrieved from https://book.douban.com/subject/2891895/

cover the statistical imagination

Decision Tree for selecting statistical Procedures

 

2.Levels of Measurement and Statistical Test

Levels of Measurement and Choosing the Correct Statistical Test

http://web.pdx.edu/~newsomj/uvclass/ho_levels.pdf

Level of measurement and Statistical test

 

 

 3. Online tools for selecting statistical procedures

Statistical Analysis Decision Tree. (n.d.). Retrieved November 10, 2019, from Statistics Solutions website: https://www.statisticssolutions.com/choosing-your-statistical-analysis/
https://www.statisticssolutions.com/how-decision-trees-can-help-you-select-the-appropriate-statistical-analysis/
 
logo statistics solutions

 Neal Van Eck.(2014). The Decision Tree for Statistics. Retrieved November 10, 2019, from : https://www.microsiris.com/Statistical%20Decision%20Tree/

 


 4. Other resources
 
Parab, S., & Bhalerao, S. (2010). Choosing Statistical Test. International Journal of Ayurveda Research, 1(3), 187–191. https://doi.org/10.4103/0974-7788.72494
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996580/
 
Statistics – Understanding the Levels of Measurement. (n.d.). Retrieved November 10, 2019, from KDnuggets website: https://www.kdnuggets.com/statistics-–-understanding-the-levels-of-measurement.html/
 
statswithcats. (2010, August 28). The Right Tool for the Job. Retrieved November 10, 2019, from Stats With Cats Blog website: https://statswithcats.wordpress.com/2010/08/27/the-right-tool-for-the-job/
https://statswithcats.files.wordpress.com/2010/08/selection-methods-8-21-2010.png
 
 
Burrus, S. (2016, October 11). Selecting the Right Statistical Analysis Tool for Your Research. Retrieved November 10, 2019, from https://research.phoenix.edu/blog/selecting-right-statistical-analysis-tool-your-research
 
 
Dias2010b 1
 
 

 

http://www.simonqueenborough.info/R/intro/index.html

http://www.simonqueenborough.info/R/statistics/which-test.html

 

 

https://stats.idre.ucla.edu/other/mult-pkg/whatstat/

Number of Dependent VariablesNature of Independent VariablesNature of Dependent Variable(s)*Test(s)How to SASHow to StataHow to SPSSHow to R
10 IVs (1 population) interval & normal one-sample t-test SAS Stata SPSS R
ordinal or interval one-sample median SAS Stata SPSS R
categorical (2 categories) binomial test SAS Stata SPSS R
categorical Chi-square goodness-of-fit SAS Stata SPSS R
1 IV with 2 levels (independent groups) interval & normal 2 independent sample t-test SAS Stata SPSS R
ordinal or interval Wilcoxon-Mann Whitney test SAS Stata SPSS R
categorical Chi-square test SAS Stata SPSS R
Fisher’s exact test SAS Stata SPSS R
1 IV with 2 or more levels (independent groups) interval & normal one-way ANOVA SAS Stata SPSS R
ordinal or interval Kruskal Wallis SAS Stata SPSS R
categorical Chi-square test SAS Stata SPSS R
1 IV with 2 levels (dependent/matched groups) interval & normal paired t-test SAS Stata SPSS R
ordinal or interval Wilcoxon signed ranks test SAS Stata SPSS R
categorical McNemar SAS Stata SPSS R
1 IV with 2 or more levels (dependent/matched groups) interval & normal one-way repeated measures ANOVA SAS Stata SPSS R
ordinal or interval Friedman test SAS Stata SPSS R
categorical (2 categories) repeated measures logistic regression SAS Stata SPSS R
2 or more IVs (independent groups) interval & normal factorial ANOVA SAS Stata SPSS R
ordinal or interval ordered logistic regression SAS Stata SPSS R
categorical (2 categories) factorial logistic regression SAS Stata SPSS R
1 interval IV interval & normal correlation SAS Stata SPSS R
interval & normal simple linear regression SAS Stata SPSS R
ordinal or interval non-parametric correlation SAS Stata SPSS R
categorical simple logistic regression SAS Stata SPSS R
1 or more interval IVs and/or 1 or more categorical IVsinterval & normal multiple regression SAS Stata SPSS R
analysis of covariance SAS Stata SPSS R
categorical multiple logistic regression SAS Stata SPSS R
discriminant analysis SAS Stata SPSS R
2+1 IV with 2 or more levels (independent groups) interval & normal one-way MANOVA SAS Stata SPSS R
2+ interval & normal multivariate multiple linear regression SAS Stata SPSS R
0 interval & normal factor analysis SAS Stata SPSS R
2 sets of 2+0 interval & normal canonical correlation SAS Stata SPSS R
        

 

说明
分类：kaiwu
点击数：743

Julia是2009开始的一个动态编程语言（dynamic programming language），在数值运算方面，特别是平行计算（Parallel_computing）、云计算（Cloud computing）方面非常突出。

https://julialang.org/

https://en.wikipedia.org/wiki/Julia_%28programming_language%29

https://baike.baidu.com/item/Julia/10423675#viewPageContent

Julia computing, https://juliacomputing.com/

logo julia

 open textbook：Perla, J., & Stachurski, T. J. S. and J. (2020). Quantitative Economics with Julia. https://julia.quantecon.org/
McNicholas, P. D., & Tait, P. A. (2018). Data science with Julia. Boca Raton: Taylor & Francis, CRC Press.
Joshi, A. (2016). Julia for Data Science. Packt Publishing.
Retrieved from https://book.douban.com/subject/26886807/
Voulgaris, Z. (2016). Julia for data science. Basking Ridge, NJ: Technics Publications.
Retrieved from https://book.douban.com/subject/26886806/
扎卡赖亚斯·弗格里斯. (2018). Julia数据科学应用. 人民邮电出版社.
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Sherrington, M. (2015). Mastering Julia: Develop your analytical and programming skills further in Julia to solve complex data processing problems. Birmingham: Packt Publishing.
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