Retour à Biostatistique mathématique Cours intensif 1

4.5

étoiles

319 évaluations

•

68 avis

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required....

DH

4 juin 2017

I knew a lot about probability before starting this course, but I didn't know much of anything about frequentist statistics. This course helped me understand some tricky concepts.

PB

11 janv. 2016

Very concise, well-presented course. This was my second time taking it as a refresher. Prof. Caffo does a great job presenting the materials. However, prepare to be challenged.

Filtrer par :

par Omar M B

•29 janv. 2017

For prospective students who are looking to enter the biostatistics/epidemiology field in the future. This course is designed as an in depth fundamentals of biostatistics where Professor Brain Caffo dives deep into some of the key formulas, origins of statistical formulas, and theoretical aspects of statistics.

Great lecture with valuable information, however, due to the lack of engagement within the lectures, it absolutely leave students who have no background of Calculus or Linear Algebra in the dust with no reference to assist them in the course. Once or if a student has a solid foundation for the topics covered in this course, this information is very insightful and understandable.

you might ask yourself, someone who doesn't have a prior knowledge of the material covered in the course if you should even take part in the course, my answer would still be yes, so long as you are willing take notes, save the videos, and return back to them at a later point once you build yourself up by studying calculus, statistics, and/or linear algebra.

par Xavier S

•25 déc. 2019

Interesting topic. You just need a basic high school level mathematic background (derivative, integral, set theory) to succeed.

I learned many things and for that I am grateful and that's why I have given 3 stars, *BUT* it was suffering to follow this course due to the lack of pedagogy (my opinion). Even if the teacher apparently tried to be didactic, he failed in my opinion.

The slides are mostly text and formula, no schemes, no tables, no animations, almost entirely black and white, nothing to help visually. If you are not an auditory memory person, you are in the bad lecture. The content is easy and basic, but the way it is presented is rather harmful. Fery few examples. The homeworks and quizz are pertinent but there is not enough questions, not enough exercises to try our understanding. And the corrections are really minimalist or even inexistent. I did not catch the objectives of most of the lectures, the motivations was not relly explained neither the link between the lectures. I found that the structure was not adequate for this basic level of statistic course.

The interpretations of the claims and results are very poorly explored, that's a shame because when Brian Caffo rarely covered interpretations, it was very interesting because he gives us many details about the different way of interpretation and the strenghts and weakness of each interpretation.

In conclusion, this COULD HAVE BEEN an excellent AND pleasant course, but for that you have to consider the question "How could I understand sufficiently well and present sufficiently well my lectures and each slides and each exercise and each example and each question (... ...) such as someone that never heard about this topic and that does not have my background and experience can understand deeply what I am saying without the need of exterior help?" Especially for a MOOC.

Despite all of these points, I repeat nevertheless that I learned many things and that I am grateful for the content and the initiative and the work that was done to prepare and realize this course (I am totally aware how much work and time it takes).

par Joseph L

•15 juin 2018

This is a very worthful course to SUFFER! I'd like to say, no camp, no gain. In addition to the statistics with math, I experience how important of "hang on there and never give up"!I'm going to the camp 2 to see what's gonna happen.

par Charles M

•8 déc. 2019

Lot of material to cover obviously and bridging theory to practical skills and knowledge is a tall order. But this course finds a way to force the learner to really understand some fundamental statistical concepts through brilliantly designed, albeit very challenging, quiz questions. The lectures are as straight-forward as you could ask for without compromising the integrity of this being a purely statistical course. If you're looking to establish a basic foundation in statistics, I strongly recommend this course without reservation. Recommend students take the math requirements seriously (algebra, integral and differential calculus).

par Tarun

•3 sept. 2020

Excellent course. I thank Prof Caffo for creating this content rich course and explaining it in an utmost lucid manner, that even person with average mathematical background could also grasp the idea without much difficulty.

par Mengyu D

•18 févr. 2017

推荐之前修过统计基础课程的人上，最好还学过一点R。

par Chrys

•11 juil. 2017

I learned a lot in the course. I'm not sure that Dr Caffo is the best explainer ever, and there could be more worked examples. Or maybe extra quizzes?

par KJ B

•27 juil. 2017

There are few quizzes to test skills, and lectures are not interactive. There are better course on this site for statistics learning.

par Mark B

•31 déc. 2016

Undoubtedly the course instructor is very knowledgeable. However I did not take away as much from this course as I would like to. It is mostly theoretical; very limited examples. I also missed the bio in biostatistics. A title like "Mathematical Statistical theory boot camp" would be better suited. I believe having a set of cases that get reused in some form over all lectures would be very beneficial for -at lease mine- understanding the topics. The form of lecturing is not using the benefits that an online platform offers. So instead of short interactive videos, this has slides with lengthy essential spoken word with it; hardly summaries.

par Mitchell L

•20 juin 2016

He terribly prepared us for quizes and gave about 4 examples in the entire course. I filled a notebook with about 40 pages of notes, about 3 of which were useful for the quizes. Though content was good, but i found myself looking things up because of his somewhat neive explanations of difficult concept.

par vibha h

•17 juil. 2019

Lectures are a bit confusing. I watched youtube videos for better explanations.

par Soren S S

•18 juin 2017

I'd previously done stats a few years ago, I came to this course to refresh my knowledge. Practice problems can't be done based on lecture videos alone, and take much longer to do than advertised.

par Mihnea T

•6 janv. 2018

Great course, insuring the basics for statistical inference. Well thought explanations and a good examples. Does get a bit hard to follow with Lecture 14 (last one). Practice exercises and homework are great for applying the covered concepts. Pity there isn't much activity on the forum. Still, this course is a must if you want to have the stats ABC in order and move forward to other data science topics in full understanding of what's happening behind the scenes.

par Mikolaj K

•2 janv. 2019

Very thorough introduction to statistics (Definitely more generic than just "bio"). Some of the homeworks are very mathematical, but the main body of work is understandable without too much of mathematical effort. Extremely well paced and self-contained which is a signature of the best coursers materials.

Good starting point for all scientist eager to truly understand the tools we all use. I'm looking now for a follow-up course from professor Brian.

par Brett B

•27 mai 2018

Does this really need to be called mathematical BIOstatistics? The content itself is regular mathematical statistics, just with examples from biology. Add in a couple of examples from elsewhere and the scope of the class is broadened. Still, as someone with a STEM background but not in biology, I found this course to be excellent.

par John M G

•5 nov. 2020

Honestly, the course gave me a hard time. I'm sure that I was not able to get all the concepts completely but I'm thankful I get to use this course as a way to expose myself to these advanced concepts. Besides, it takes time and practice to fully understand these concepts. Thank you so much!

par Martin M

•9 juin 2018

One of the greatest introductory courses on statistics available for free in the internet. If you are interested in the subject you should really go for it, but realize that indeed it is very difficult and you will need to do a lot of extra research, especially at the exercises. Great couse.

par Joseph V M

•18 juil. 2019

This course was informative. The strongest aspect of the course were the exercises; they were challenging, requiring me to think about the material and apply the material in a way that was not necessarily demonstrated in the lectures. I would have preferred more exercises, in fact.

par Do H L

•2 juil. 2016

This course should be part of the Data Science specialization. Actually, you can supplement the Statistical Inference course with these two Boot camp courses really well!

A great revision of statistics, very rigorous and thorough cover of all distributions and hypothesis tests.

par Bizovi M

•25 déc. 2017

A very rare course which approaches introductory statistics rigorously. The stat courses I had at university introduced all these concepts without any justification, so I enjoyed the critical approach used here.

par Daniel H

•5 juin 2017

I knew a lot about probability before starting this course, but I didn't know much of anything about frequentist statistics. This course helped me understand some tricky concepts.

par Phillip A B

•11 janv. 2016

Very concise, well-presented course. This was my second time taking it as a refresher. Prof. Caffo does a great job presenting the materials. However, prepare to be challenged.

par Jeremy B

•9 juil. 2017

Great course, though a little difficult in parts, particularly the first week. Worth working through though for a better understanding of probability and statistics.

par Blake T E

•5 avr. 2018

This course is phenomenally well developed with great curriculum and materials for building astrong base to enter into statistics with a strong base of knowledge.

par Junzhi ( Y

•19 oct. 2015

Topics presented well for comprehension, good lecture quality.

These topics are covered in grad level statistics courses at certain universities.

- Recherche d'un but et d'un sens à la vie
- Comprendre la recherche médicale
- Le japonais pour les débutants
- Introduction au Cloud Computing
- Les bases de la pleine conscience
- Les fondamentaux de la finance
- Apprentissage automatique
- Apprentissage automatique à l'aide de SAS Viya
- La science du bien-être
- Recherche des contacts COVID-19
- L'IA pour tous
- Marchés financiers
- Introduction à la psychologie
- Initiation à AWS
- Marketing international
- C++
- Analyses prédictives & Exploration de données
- Apprendre à apprendre de l'UCSD
- La programmation pour tous de Michigan
- La programmation en R de JHU
- Formation Google CBRS CPI

- Traitement automatique du langage naturel (NLP)
- IA pour la médecine
- Doué avec les mots : écrire & éditer
- Modélisation des maladies infectieuses
- La prononciation de l'anglais américain
- Automatisation de test de logiciels
- Deep Learning
- Le Python pour tous
- Science des données
- Bases de la gestion d'entreprise
- Compétences Excel pour l'entreprise
- Sciences des données avec Python
- La finance pour tous
- Compétences en communication pour les ingénieurs
- Formation à la vente
- Gestion de marques de carrières
- Business Analytics de Wharton
- La psychologie positive de Penn
- Apprentissage automatique de Washington
- CalArts conception graphique

- Certificats Professionnels
- Certificats MasterTrack
- Google IT Support
- Science des données IBM
- Ingénierie des données Google Cloud
- IA appliqué à IBM
- Architecture Google Cloud
- Analyste de cybersécurité d'IBM
- Automatisation informatique Google avec Python
- Utilisation des mainframes IBM z/OS
- Gestion de projet appliquée de l'UCI
- Certificat stratégie de mise en forme
- Certificat Génie et gestion de la construction
- Certificat Big Data
- Certificat d'apprentissage automatique pour l'analytique
- Certificat en gestion d'innovation et entrepreneuriat
- Certificat en développement et durabilité
- Certificat en travail social
- Certificat d'IA et d'apprentissage automatique
- Certificat d'analyse et de visualisation de données spatiales

- Diplômes en informatique
- Diplômes commerciaux
- Diplômes de santé publique
- Diplômes en science des données
- Licences
- Licence d'informatique
- MS en Génie électrique
- Licence terminée
- MS en gestion
- MS en informatique
- MPH
- Master de comptabilité
- MCIT
- MBA en ligne
- Master Science des données appliquée
- Global MBA
- Masters en innovation & entrepreneuriat
- MCS science de données
- Master en informatique
- Master en santé publique