Skip to main content

How do I prepare for Msc in datascience at CMI in 2020 within one year?


Introduction:

I have been seeing people ask this question many times in quora as well as in conversations too. So I thought I will take time and I will answer this question outright in a blog post. So, two of my friends from ISI went through this exam this year. It depends on your background. I will suggest you prepare your statistics, probability, and general bachelor's level mathematics properly.


For basic level probability, I will suggest you go through Introduction to probability by Sheldon ross; read the concepts, solve the examples and proceed. This book is good enough to prepare you for the probability questions you will face.

Now, let’s come to the statistics part. For statistics, I will suggest reading through Casella Berger and CR Rao. See, these books are pretty rigorous, and maybe you can find it really hard for you. But, You can skip some of the hard theoretical parts, and follow the flow basically. That will give you a good knowledge of what can come.
Now, once all this is done, start with the question papers. I don’t know whether there is an interview part for this exam for all the candidates. In that case, you will have to go through some blogs or sites mentioning the questions asked in the interview. But prepare well on the previous year's questions; try to fetch similar questions from the above-mentioned books again. It may also help to try m.stat question papers from ISI websites which have solutions available on the internet also. But that is if you are done with all these previous steps.

Some advices and best of luck!

And above all, please try some data science projects hands-on before you end up doing a master's on it. You could be a karate master but maybe you will not like kicking people on a daily basis. A similar concept apply to data science. It may look fancy, but needs a lot of reading and researching along with programming skills to attain a bit of expertise even. So, look before you leap. Just do not run behind “data scientist is the hottest job of 21st century”. Maybe you are well determined and all; but I think it must be mentioned for people aspiring to become data analysts or data-science people.
So! best of luck for your exam!

Comments

Popular posts from this blog

Tinder bio generation with OpenAI GPT-3 API

Introduction: Recently I got access to OpenAI API beta. After a few simple experiments, I set on creating a simple test project. In this project, I will try to create good tinder bio for a specific person.  The abc of openai API playground: In the OpenAI API playground, you get a prompt, and then you can write instructions or specific text to trigger a response from the gpt-3 models. There are also a number of preset templates which loads a specific kind of prompt and let's you generate pre-prepared results. What are the models available? There are 4 models which are stable. These are: (1) curie (2) babbage (3) ada (4) da-vinci da-vinci is the strongest of them all and can perform all downstream tasks which other models can do. There are 2 other new models which openai introduced this year (2021) named da-vinci-instruct-beta and curie-instruct-beta. These instruction models are specifically built for taking in instructions. As OpenAI blog explains and also you will see in our

Can we write codes automatically with GPT-3?

 Introduction: OpenAI created and released the first versions of GPT-3 back in 2021 beginning. We wrote a few text generation articles that time and tested how to create tinder bio using GPT-3 . If you are interested to know more on what is GPT-3 or what is openai, how the server look, then read the tinder bio article. In this article, we will explore Code generation with OpenAI models.  It has been noted already in multiple blogs and exploration work, that GPT-3 can even solve leetcode problems. We will try to explore how good the OpenAI model can "code" and whether prompt tuning will improve or change those performances. Basic coding: We will try to see a few data structure coding performance by GPT-3. (a) Merge sort with python:  First with 200 words limit, it couldn't complete the Write sample code for merge sort in python.   def merge(arr, l, m, r):     n1 = m - l + 1     n2 = r- m       # create temp arrays     L = [0] * (n1)     R = [0] * (n

What is Bort?

 Introduction: Bort, is the new and more optimized version of BERT; which came out this october from amazon science. I came to know about it today while parsing amazon science's news on facebook about bort. So Bort is the newest addition to the long list of great LM models with extra-ordinary achievements.  Why is Bort important? Bort, is a model of 5.5% effective and 16% total size of the original BERT model; and is 20x faster than BERT, while being able to surpass the BERT model in 20 out of 23 tasks; to quote the abstract of the paper,  ' it obtains performance improvements of between 0 . 3% and 31%, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks. ' So what made this achievement possible? The main idea behind creation of Bort is to go beyond the shallow depth of weight pruning, connection deletion or merely factoring the NN into different matrix factorizations and thus distilling it. While methods like knowle