How good do you need to be in mathematics and statistics to become a data scientist?
Let me tell you my story. Everyone starts from somewhere. I am a mechanical Engineer. No CS Degree. No formal Math/Stat education. Got a business analyst job. For the first three years did a lot of grunt reporting work including Excel/SQL , Spotfire.Then the opportunity presented itself and my manager asked me to his office:Manager: I want you to Learn some Python.Me: But I have never done programming in my life. Why now?Manager: There is something called Random Forest Algorithm that is getting famous. Since you are the only non PHP/Javascript developer around I thought you might be able to help us (Business Read: Since you are the only one with no work on his hands, why don’t you do something)So I went to google. Read what the algorithm is used for. Just understood a little about the classification problem statement. Got a blog from someone who had implemented it in Python. Copy Pasted his code. Ran on my local machine. And Voila!!! We used that small piece of code to run on a classification dataset for that company and included it in a product. My manager was impressed.At that point of time I didn’t knew any data science. I didn’t knew about Entropy, decision trees, Cross validation etc. etc. To tell you the truth the model I created might had overfitted as hell. I just know that it was a start. And i consider that start very important.From then on I started trying many models to do the same task. I started up with Kaggle. And when I was not able to compete I started reading more. I took many open MOOCs. I learned about new things. I was open to learning new things. I was open to understand new things. And one day I realized that I was able to understand the math behind if I was willing to put effort.I started up with breadth and eventually got into the depth of things.So yes, get your start. Taste the blood. Get your hands dirty. Run the algorithm. Try to improve it by playing with the parameters and reading up on the net. I am sure in the process you will learn a lot about the inner workings of the algorithms. And maybe then some day you will be motivated enough to understand them fully.You can also try taking a few math classes for data science. One such class which I can recommend is Machine Learning | Coursera course which tackles Maths at a very fundamental level.
Who were the greatest mathematicians of all time?
There's only one unarguable, nobody-informed-could-disagree member of the list: Gauss. He probably did the most important work of all time in each of the fields of abstract algebra/number theory, differential geometry, and statistics. And he did quite a bit of other stuff as well. The next guy on my personal list is Euclid. His geometry wasn't superseded for almost 2000 years. He did a bit of number theory too. Other than Aristotle, Plato, and Socrates, he was probably THE most influential thinker in a several-century period, and certainly the only candidate who was a mathematician. The third spot is a many-way competition. The contenders include Archimedes, Euler, Riemann, and Poincare'.
Statistics as a branch of mathematics?
You will obviously need to do alot more work for an essay than use answers on here. But, of course Statistics is a branch of mathematics. Statistics uses numbers, charts, graphs, interpolation, and much much more. It should be fairly obvious how it relates to math. It can absoutely be applied to Science. Just think of doing a lab report. Many times you will retreive data while doing the lab and will have to use the data to produce something further. Many times you will be doing statistics whether you know it or not. A common example can be a best-fit line for your data that you may have graphed.
Are there any mathematicians who do not like statistics and probability, and if there are, why not?
Well, the questions splits into two questions essentially:Probability is essentially a part of mathematics. Basically it is analysis on a space of integral 1. You have subfields like Probabilistic number theory, Probabilistic method, Random matrix, Random walk or Schramm–Loewner evolution. All of those are rigorous subjects of mathematics. To like or not to like them is a little bit like not liking any other field of mathematics. Personally what I do not like is that the vocabulary is quite different from other fields of mathematics. It makes reading harder for me.Statistics is quite different. Here the point is to try to apply or build probabilistic models to reality. In other words, I see statistics as applied probability. For example the Black–Scholes model used in finance is a rigorous probabilistic model based on Stochastic differential equation. But it is based on a number of assumptions that may or may not be valid. So, use it at your own peril. Some people are turned off by the applied flavor of it.In practice both Probability and Statistics share a common vocabulary. They are often taught in departments separate from the mathematics department. Also graduates in statistics have a better professional future than the ones in pure mathematics.
What job could i get with a BSc Mathematics?
Damned little. Take electives in finance if you like that, or computer science and programming if you like that to get an entry.Newer fields include logistics and reliabilitry engineering. Even with a masters or Phd in mathematics jobs are few and far between, no matter what lies you are told. I know. I spent decades looking for work outside of teaching and my areas of expertise were in very applied mathematics including statistics and what used to be known as operations research (now applied management science). There seems to be an employment bias against those in mathematics. [Perhaps others here have had similar experiences.] Try to label yourself as something else other than a "math major." Actuarial work is another good area, but you'll need advanced degrees to move up. The best of luck to you. Sadly, you'll need it.
Are mathematicians REALLY that rare in the USA?
I am American and I hear how STEM is in demand, but math fields make a lot of money? I always hear my friends say they are "not good in math" and I just realized is that same mindset really as widespread as I think? Is it really that bad where EVERYONE thinks they're bad so there is this huge void? I'm not good in math either but I do plan to get better because I personally want to be educated and confident. But beyond that is it easy to get a math career that pays well?
Is Statistics math?
My answer, which is "no," is explained more fully in Michael Hochster's answer to What do pure mathematicians and statisticians think of each other?Also see: Joe Blitzstein's answer to What is the major difference between statistics and math major students? As in primary goals/why statistics vs math.
Is it better to major in statistics, mathematics, or computer science?
It depends on whatever you are good at and whatever you enjoy. Selecting computer science over mathematics or statistics purely for the job prospects is a bad decision if you empirically like mathematics more. While the market for mathematics majors and Master's in mathematics may be smaller than that for computer science, selecting to devote four years of study to a subject that you do not enjoy is a far worse decision. In addition, if you are planning a masters in mathematics, be aware that computer science does not generally gear students well towards a career in mathematics. At most universities (with the exception of top schools like Stanford and MIT), an undergraduate computer science degree doesn't go farther into mathematics than linear algebra. This leaves out the majority of higher-level mathematics you'll see with the math major, such as multivariable calculus, ordinary and partial differential equations, vector and tensor Calculus, complex variables, or number theory; all of which are vitally important to the study of any graduate mathematics. If job prospects are a concern, understand that while CS has a larger job market, it also has a very large pool of prospective applicants. Mathematics is a growing field, and here are some links that may alleviate your concern. https://www.math.cornell.edu/m/U...U.S. Bureau of Labor Statistics
Consumer Mathematics Problem?
Oh, yes, here's the step by step explanation. Step 1. Price in 2006 = 428000. ( I am ignoring the unit of price for the time being) Step 2. Percentage increase from 2005 = 7.88% Step 3. Now let x be the price in 2005. (Yu are at liberty to assume any variable for this, and actually no such assumption is required once you are familiar with the proceedings) This x increased by 7.88% or in other words, 7.88/100 parts of x increased. Step 4. After the increase, how much is the value with respect to x. The increase is (x)*7.88/100 and you originally had x. So, if you add these values, you get the total value after increase. The total value after increase is x + x*7.88/100 = x(1+7.88/100) = x{ (100+7.88) /100} = x{ 107.88/100) This means that you had 100% initially and after an increase of 7.88%, the total is 107.88%. Once you understand this principle, the above substitution of variable x is nor necessary. Always assume the initial value (datum value) to be 100% and for any increase or decrease or both in percentage, do the corresponding addition or subtraction or both in the initial percentage. Step 5. Now you know that afte the increase you have 107.88% of the original value, you have to find the original value. Here, 107.88% = 428000 Divide both sides by 107.88, you get 1% = 428000/107.88 Step 6. got the value of 1% of original value. To find 100% of original value, just multiply this by 100. So, original value = (428000*100)/107.88, you can do the calculation. After a lot of practice and after understandying the underlying principle, you will be able to write the last step of (100/100+increase)*new value straight away and find the result in less than a minute for all such questions.
How is the B.Math program at Indian Statistical Institute?
The B.Math programme is offered by Bangalore center of ISI only.When it comes to Mathematics, hands down there are two best places tied at the same level, that have produced brilliant students capable of competing nationally and internationally, and they are ISI Bangalore and CMI (Chennai Mathematical Institute).I would like to answer right now a question that everyone asks: which one is better - ISI or CMI?Well, the most honest answer is, (and believe me, it is true) that they are equal, the difference only being that ISI Bang. treats Statistics as the second most important subject (after all, it is the "Statistical" institute) while CMI treats Computer Programming as the most important subject below Mathematics. Having said that Stats and Comp. Sc. are innately related to each other, it thus becomes meaningless to compare.Now to your question:The standard of the programme is extremely high, in comparison to any other MATHEMATICS course in the nation, with CMI as an able and equal competitor.If you go through the syllabus of the BMath course (Welcome to Indian Statistical Institute, Kolkata) I think you can judge for yourself.Google yourself. Sucharit Sarkar (IMO Gold Medallist) rejected IIT (he ranked in the top 10 nationally in IIT Entrance Exam) and studied at ISI. Besides, some of the greatest mathematicians of India have come from ISI Bang. and given the wealth of ability and talent of professors who are teaching there, you can only have one description - they are suited to teach any day in any of America's famed universities. You can count on that.As I near the end, let me tell you something that I have been telling everyone: you need sophistication (intellectually, of course) to be able to be in an ISI. The faculty is so down to earth and always helpful. The students are some of the brightest minds and more importantly, some of the most trained minds of India.The ISI is a rich source of prestige and pride among academically inclined people like me.