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Deep Data Stat - A Survey Analysis on Impact of Statistics in Data Science for Students

Yıl 2020, Cilt: 3 Sayı: 1, 17 - 22, 22.12.2020

Öz

Data science is the most developing technology in recent years. The need of Data science is most important thing for the development of institutions. It is the process of analyzing, interpreting and decision making of data. There are various methods are included in the analysis of data science. Among those components, Statistics plays an important role. Without the help of statistics, the data cannot be analyzed. The arrangement and visualization of the data are also done with the use of Statistics. This paper explains the basic statistical methods used in the process of analyzing the data in Data science. As the basic terminologies are explained in the beginning, the advanced tools such as Hypothesis testing, Analysis of variance, t test, F test and Chisquare tests are discussed. Then, the interconnection between the Data science and Statistics are explained with the calculations of two tests such as Tukey test and Dunnet test. Finally, the future development and the impact of Statistics in Data sciencehave been explained

Kaynakça

  • Tabii, işte sayıları `[ ]` şeklinde sıralanmış düzeltilmiş versiyon:
  • [1] Weihs C, Ickstadt K. Data Science: The Impact of Statistics. International Journal of Data Science and Analytics. 2018 Nov 1;6(3):189-94.
  • [2] De Veaux RD, Agarwal M, Averett M, Baumer BS, Bray A, Bressoud TC, Bryant L, Cheng LZ, Francis A, Gould R, Kim AY. Curriculum Guidelines for Undergraduate Programs in Data Science. Annual Review of Statistics and Its Application. 2017 Mar 7;4:15-30.
  • [3] Cleveland WS. Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics. International Statistical Review. 2001 Apr;69(1):21-6.
  • [4] Donoho D. 50 Years of Data Science. Journal of Computational and Graphical Statistics. 2017 Oct 2;26(4):745-66.
  • [5] Ostertagova E, Ostertag O. Methodology and Application of One-way ANOVA. American Journal of Mechanical Engineering. 2013 Nov;1(7):25661.
  • [6] Niedoba T, Pieta P. Applications of ANOVA in Mineral Processing. Mining Science. 2016;23.
  • [7] Shafer JP. Multiple Hypothesis Testing. Annual Review of Psychology. 1995 Feb;46(1):561-84.
  • [8] AJPAS A. A Feature Selection Based on One-Way-ANOVA for Microarray Data Classification. AJPAS Journal. 2016;3:1-6.
  • [9] Sow MT. Using ANOVA to Examine the Relationship Between Safety Security and Human Development. Journal of International Business and Economics. 2014 Dec;2(4):101-6.
  • [10] Waller MA, Fawcett SE. Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics. 2013 Jun;34(2):77-84.
  • [11] Savin NE. Multiple Hypothesis Testing. Handbook of Econometrics. 1984 Jan 1;2:827-79.
  • [12] Stoline MR. The Status of Multiple Comparisons: Simultaneous Estimation of All Pair-wise Comparisons in One-way ANOVA Designs. The American Statistician. 1981 Aug 1;35(3):134-41.
  • [13] Park HM. Comparing Group Means: t-tests and One-way ANOVA using Stata, SAS, R, and SPSS.
  • [14] Kim TK. T Test as a Parametric Statistic. Korean Journal of Anesthesiology. 2015 Dec;68(6):540.
  • [15] Moser BK, Stevens GR, Watts CL. The Two-sample t Test versus Satterthwaite's Approximate F Test. Communications in Statistics-Theory and Methods. 1989 Jan 1;18(11):3963-75.
  • [16] McHugh ML. The Chi-square Test of Independence. Biochemia Medica: Biochemia Medica. 2013 Jun 15;23(2):143-9.
  • [17] Plackett RL. Karl Pearson and the Chi-squared Test. International Statistical Review/Revue Internationale de Statistique. 1983 Apr 1:59-72.
  • [18] Abdi H, Williams LJ. Newman-Keuls Test and Tukey Test. Encyclopedia of Research Design. Thousand Oaks, CA: Sage. 2010:1-1.
  • [19] Koenig F, Brannath W, Bretz F, Posch M. Adaptive Dunnett Tests for Treatment Selection. Statistics in Medicine. 2008 May 10;27(10):1612-25.
  • [20] Billingsley P. Probability and Measure. John Wiley Sons; 2008 Aug 4.
  • [21] Peck R, Olsen C, Devore JL. Introduction to Statistics and Data Analysis. Cengage Learning; 2015.
  • [22] Noether GE. Introduction to Statistics: The Nonparametric Way. Springer Science Business Media; 2012 Dec 6.
Yıl 2020, Cilt: 3 Sayı: 1, 17 - 22, 22.12.2020

Öz

Kaynakça

  • Tabii, işte sayıları `[ ]` şeklinde sıralanmış düzeltilmiş versiyon:
  • [1] Weihs C, Ickstadt K. Data Science: The Impact of Statistics. International Journal of Data Science and Analytics. 2018 Nov 1;6(3):189-94.
  • [2] De Veaux RD, Agarwal M, Averett M, Baumer BS, Bray A, Bressoud TC, Bryant L, Cheng LZ, Francis A, Gould R, Kim AY. Curriculum Guidelines for Undergraduate Programs in Data Science. Annual Review of Statistics and Its Application. 2017 Mar 7;4:15-30.
  • [3] Cleveland WS. Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics. International Statistical Review. 2001 Apr;69(1):21-6.
  • [4] Donoho D. 50 Years of Data Science. Journal of Computational and Graphical Statistics. 2017 Oct 2;26(4):745-66.
  • [5] Ostertagova E, Ostertag O. Methodology and Application of One-way ANOVA. American Journal of Mechanical Engineering. 2013 Nov;1(7):25661.
  • [6] Niedoba T, Pieta P. Applications of ANOVA in Mineral Processing. Mining Science. 2016;23.
  • [7] Shafer JP. Multiple Hypothesis Testing. Annual Review of Psychology. 1995 Feb;46(1):561-84.
  • [8] AJPAS A. A Feature Selection Based on One-Way-ANOVA for Microarray Data Classification. AJPAS Journal. 2016;3:1-6.
  • [9] Sow MT. Using ANOVA to Examine the Relationship Between Safety Security and Human Development. Journal of International Business and Economics. 2014 Dec;2(4):101-6.
  • [10] Waller MA, Fawcett SE. Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics. 2013 Jun;34(2):77-84.
  • [11] Savin NE. Multiple Hypothesis Testing. Handbook of Econometrics. 1984 Jan 1;2:827-79.
  • [12] Stoline MR. The Status of Multiple Comparisons: Simultaneous Estimation of All Pair-wise Comparisons in One-way ANOVA Designs. The American Statistician. 1981 Aug 1;35(3):134-41.
  • [13] Park HM. Comparing Group Means: t-tests and One-way ANOVA using Stata, SAS, R, and SPSS.
  • [14] Kim TK. T Test as a Parametric Statistic. Korean Journal of Anesthesiology. 2015 Dec;68(6):540.
  • [15] Moser BK, Stevens GR, Watts CL. The Two-sample t Test versus Satterthwaite's Approximate F Test. Communications in Statistics-Theory and Methods. 1989 Jan 1;18(11):3963-75.
  • [16] McHugh ML. The Chi-square Test of Independence. Biochemia Medica: Biochemia Medica. 2013 Jun 15;23(2):143-9.
  • [17] Plackett RL. Karl Pearson and the Chi-squared Test. International Statistical Review/Revue Internationale de Statistique. 1983 Apr 1:59-72.
  • [18] Abdi H, Williams LJ. Newman-Keuls Test and Tukey Test. Encyclopedia of Research Design. Thousand Oaks, CA: Sage. 2010:1-1.
  • [19] Koenig F, Brannath W, Bretz F, Posch M. Adaptive Dunnett Tests for Treatment Selection. Statistics in Medicine. 2008 May 10;27(10):1612-25.
  • [20] Billingsley P. Probability and Measure. John Wiley Sons; 2008 Aug 4.
  • [21] Peck R, Olsen C, Devore JL. Introduction to Statistics and Data Analysis. Cengage Learning; 2015.
  • [22] Noether GE. Introduction to Statistics: The Nonparametric Way. Springer Science Business Media; 2012 Dec 6.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Yaşam ve Karmaşık Uyarlanabilir Sistemler
Bölüm Reviews
Yazarlar

A. Mohammed Harun Babu R Bu kişi benim

B.shebana M Bu kişi benim

Yayımlanma Tarihi 22 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 3 Sayı: 1

Kaynak Göster

IEEE A. M. H. Babu R ve B. M, “Deep Data Stat - A Survey Analysis on Impact of Statistics in Data Science for Students”, International Journal of Data Science and Applications, c. 3, sy. 1, ss. 17–22, 2020.

AI Research and Application Center, Sakarya University of Applied Sciences, Sakarya, Türkiye.