RECCOMENDATION
It is my immense pleasure to recommend one of my best students, Ms. Binderiya Enkhbat, as a graduate student for The Master of Science in Data Science Online program at The University of Texas at Austin. I was impressed about this exciting new Online program from the partnership between the Department of Statistics and Data Sciences and the Department of Computer Science when I first heard of it.
I have been known Ms. Binderiya since 2010 when she took with me Ordinary Differential Equations and Partial Differential Equations courses at the Department of Mathematical Modeling of Economics. Since her second academic year, she participated successfully in many student conferences and competitions organized by departments, universities and student associations. During the last academic year, she directly received a work proposal as Risk analyst in Chinggis Khan Bank.
Four years ago, she contacted me expressing her willingness to start her next level of academic career. At the same time, there was some institutional changes in the National University of Mongolia, and some departments including Mathematical Modeling of Economics joined with the Department of Applied Mathematics. After she enrolled the Master of Science in Applied Mathematics program, I supervised her master’s degree thesis, “Local search based approach for backgrounds of mathematics, statistics and finding cancer-activated sub-networks”. We proposed a Clique set finding (CSF) algorithm based on the local search procedure to identify condition-specific modules on the cancer-activated Multi-Type Interaction (MTI) gene network. The thesis examined the performance of the CSF method on a simulated data set and the application to real data sets of the samples from normal and cancer patients. As a result, we determined a biological meaningful sub-network involved in many breast cancer related pathways and terms. When in the course of writing thesis, she has proved her excellent mathematical packages.
During these years, she also assisted her Department professor to organize “The International Conference on Optimization, Simulation and Control” in Mongolia in 2013 and 2016, and she has shown herself as a good team worker with right mix of interpersonal skills. She is quick learner, perspective, creative and talented young professional who is well-prepared.
The strong quantitative and statistical knowledge she possessed is well suited to this new field of Data Science. I truly believe that this program will lead her into successful contribution to the related areas as well as to the development of young specialist of Mongolia.
- People attend college or university for many different reasons (for example, new experiences, career preparation, increased knowledge) Why do you think people attend college or university? Use specific reasons and examples to support your answer. 83
- RECCOMENDATION 11
- Pets should be treated like family members Do you agree or disagree with this statement Give reasons to support your opinion 75
- Summarize the main points in the lecture and then explain how they cast doubt on the ideas in the reading passage. 3
- Do you agree or disagree with the following statement?At universities and colleges, sports and social activities are just as important as classes and libraries and should receive equal financial support.Use specific reasons and examples to support your an 90
Transition Words or Phrases used:
also, but, first, if, second, so, well, as to, as a result, as well as
Attributes: Values AverageValues Percentages(Values/AverageValues)% => Comments
Performance on Part of Speech:
To be verbs : 7.0 13.1623246493 53% => More to be verbs wanted.
Auxiliary verbs: 1.0 7.85571142285 13% => OK
Conjunction : 15.0 10.4138276553 144% => OK
Relative clauses : 5.0 7.30460921844 68% => More relative clauses wanted.
Pronoun: 36.0 24.0651302605 150% => Less pronouns wanted
Preposition: 60.0 41.998997996 143% => OK
Nominalization: 19.0 8.3376753507 228% => Less nominalizations (nouns with a suffix like: tion ment ence ance) wanted.
Performance on vocabulary words:
No of characters: 2246.0 1615.20841683 139% => OK
No of words: 399.0 315.596192385 126% => OK
Chars per words: 5.6290726817 5.12529762239 110% => OK
Fourth root words length: 4.46933824581 4.20363070211 106% => OK
Word Length SD: 3.35002322926 2.80592935109 119% => OK
Unique words: 231.0 176.041082164 131% => OK
Unique words percentage: 0.578947368421 0.561755894193 103% => OK
syllable_count: 690.3 506.74238477 136% => OK
avg_syllables_per_word: 1.7 1.60771543086 106% => OK
A sentence (or a clause, phrase) starts by:
Pronoun: 13.0 5.43587174349 239% => Less pronouns wanted as sentence beginning.
Article: 2.0 2.52805611222 79% => OK
Subordination: 5.0 2.10420841683 238% => Less adverbial clause wanted.
Conjunction: 2.0 0.809619238477 247% => Less conjunction wanted as sentence beginning.
Preposition: 5.0 4.76152304609 105% => OK
Performance on sentences:
How many sentences: 16.0 16.0721442886 100% => OK
Sentence length: 24.0 20.2975951904 118% => OK
Sentence length SD: 52.1092227322 49.4020404114 105% => OK
Chars per sentence: 140.375 106.682146367 132% => OK
Words per sentence: 24.9375 20.7667163134 120% => OK
Discourse Markers: 4.375 7.06120827912 62% => OK
Paragraphs: 5.0 4.38176352705 114% => OK
Language errors: 0.0 5.01903807615 0% => OK
Sentences with positive sentiment : 9.0 8.67935871743 104% => OK
Sentences with negative sentiment : 3.0 3.9879759519 75% => OK
Sentences with neutral sentiment: 4.0 3.4128256513 117% => OK
What are sentences with positive/Negative/neutral sentiment?
Coherence and Cohesion:
Essay topic to essay body coherence: 0.0 0.244688304435 0% => The similarity between the topic and the content is low.
Sentence topic coherence: 0.0 0.084324248473 0% => Sentence topic similarity is low.
Sentence topic coherence SD: 0.0 0.0667982634062 0% => Sentences are similar to each other.
Paragraph topic coherence: 0.0 0.151304729494 0% => Maybe some paragraphs are off the topic.
Paragraph topic coherence SD: 0.0 0.056905535591 0% => Paragraphs are similar to each other. Some content may get duplicated or it is not exactly right on the topic.
Essay readability:
automated_readability_index: 17.6 13.0946893788 134% => OK
flesch_reading_ease: 38.66 50.2224549098 77% => OK
smog_index: 11.2 7.44779559118 150% => OK
flesch_kincaid_grade: 13.8 11.3001002004 122% => OK
coleman_liau_index: 15.67 12.4159519038 126% => OK
dale_chall_readability_score: 10.13 8.58950901804 118% => OK
difficult_words: 134.0 78.4519038076 171% => OK
linsear_write_formula: 14.5 9.78957915832 148% => OK
gunning_fog: 11.6 10.1190380762 115% => OK
text_standard: 12.0 10.7795591182 111% => OK
What are above readability scores?
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It is not exactly right on the topic in the view of e-grader. Maybe there is a wrong essay topic.
Rates: 11.2359550562 out of 100
Scores by essay e-grader: 0.67 Out of 6
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Note: the e-grader does NOT examine the meaning of words and ideas. VIP users will receive further evaluations by advanced module of e-grader and human graders.