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In the biopic ‘Sarabjit’, wherever his wife Sukhpreet played by Richa Chadda or sister Dalbir Kaur played by Aishwarya Rai are seen holding photographs or posters of Sarabjit.They are all made with Sarabjit’s real pictures and not Randeep Hooda’s pictures. This details stands out in the movie a lot of times and adds the much needed feel.In the introductory scene of the movie ‘Masaan’ there is a spectacular detail. Vicky’s character Deepak Choudhry, belongs to a lower middle class family and they earn money by burning corpses.In the scene here, he goes to a ghat and lights up a twig and later he is shown lighting up the gas in the kitchen with the same twig. Words wouldn’t suffice as to what this one scene has given to the movie.In the Movie ‘Tamasha’ Ranbir Kapoor’s character, Ved is an avid story lover and when Deepika is with him in Corsica, she finds a book in his room.Implying that Ved might have just started reading the book. A few scenes later when it’s time for them to bid adieu to their little adventure, look closer at the red circle in the picture below.An inclination to such small details shows the director’s love for the character as by strategically placing the book there is absolutely nothing that has been stolen from Ved’s personality.In the song ‘Emotional Attyachar’ from the movie ‘Dev D’, there is a line where the leads sing “1, 2, 3, 4,….6” and skip 5. Well, it wasn’t just supposed to be funny.Anuragh Kashyap’s works on stories that are usually out of the box and do not agree with generic mass who likes predictable plots. And this one line here in particular is meant to be a satire. One of Kashyap’s films called, “Paanch”, was not approved by the censor board and remains unreleased till date. That’s why they eat up five in the lyrics, seems legit.Source :Images : https://www.google.co.in/amp/s/www.storypick.com/bollywood-movies-details/amp//

I am a first year student in the NYU MS in Data Science program. I think it’s fantastic for a various number of reasons.The courses are comprehensiveThere are a lot of newer data science programs showing up nowadays, so it can be hard to judge whether certain programs are legitimate or not. The introductory course gave a very good high level overview of the considerations one must make while approaching data science in business and how to break down seemingly complex problems into well-known smaller problems in a structured fashion. The Statistics and Probability course was like an undergraduate degree in Statistics compressed into one semester. I am currently taking the Machine Learning class, and it really pushes you to understand the topic from all perspectives (theoretical, mathematical, and practical).There are many opportunities to work on projects that you can speak about with prospective employers. The capstone project is a required course at the end of the program, but nearly every class has a final term project required. The requirements for the projects are very open - you can choose nearly any data science problem that interests you, and you can go as deep into them as you’d like.The program is very flexible with excellent electivesAll of the classes are scheduled at nighttime (my earliest class is at 5 PM), so if you are looking to complete the program while working full-time, it is very possible. I know many other students who are already working as analysts that are pursuing this degree part-time.If you believe that certain classes are unnecessary given your skills and experience, you can easily opt out of courses in favor of more advanced courses. For example, I know several students that waived out of the Statistics and Probability class.Half of the required coursework is electives. This means you can choose to specialize your degree with classes that suit your personal career goals. You can choose to take classes at Stern if you want to go into finance or business. You can takes classes in bioinformatics, neuroscience, psychology, political science, and computer science - it really depends on what you’re studying data science for! A full list of the pre-approved elective courses can be found here: Pre-Approved Elective Courses - NYU Center for Data Science. It seems to be fairly easy to obtain approval for other classes that are not on the pre-approved list, as well.The data science electives are great. While I just mentioned how you can customize your degree through a wide range of electives, most students end up choosing the data science electives themselves. I am currently taking a course on Deep Learning from Yann LeCun, the head of Facebook AI Research. You can also take Natural Language Processing from Kyunghyun Cho. Carlos Fernandez-Granda and Afonso Bandeira also offer excellent courses in Optimization.The resources provided are very goodThe one resource I am most grateful for is the big, modern open space for studying that is provided to Center of Data Science students. When I got my undergraduate degree at a large public school, there was absolutely no guarantee that you could go to the library and find other people next to you studying the same thing. We have two floors on 5th Ave with gigantic windows, desks with in-built power outlets, whiteboards, and small office spaces. A lot of collaboration and exchange of ideas happens in this space!There are company information sessions and talks hosted nearly every week. Looking through my inbox, recently there has been talks on “Methods to Discover Underlying Linguistic Structure,” “Future State: Big Data and Analytics for Better Governance,“ and “Data science @Tumblr.” CDS also hosts its own career fair twice a year for companies specifically looking for data scientists, so you don’t need to go stand in line at the huge campus-wide career fair. Past companies that have come to the career fair include: Audible, Ebay, Medidata, iHeartMedia, and Capital One Labs. There are plenty of networking opportunities, and they really make it easy for you to interact with many different members of the data science community.However, you should also consider this as well:It is extremely rigorousAs the other answer mentioned, a few classes expect you to come in with a very good understanding of the subject already. If you are not strong in programming or theoretical math, you will need to self-study to catch up. Homework takes around 60 hours a week, usually more. Luckily, I have found most professors to be very helpful and available if you are really struggling to understand the material. The introductory courses in the first semester for the Data Science program are designed to help you catch up on these subjects. But if you are a CS student and taking these classes as electives, you may find it very challenging.If you are looking for a more high level, “easy” path into analytics, this may not be for you. It is very heavy on math, statistics, and programming. A fellow student once told me they wished they pursued a Business Analytics degree instead. However, to be a successful data scientist, you must learn the math and theory behind the algorithms!

Starting out with generic advice:Choose courses based on the professor, not the course description. The more you like the professor, the more you'll enjoy the class and the more you'll learn. The requirements are usually flexible so you can arrange your schedule to take the courses you need with the professors you like.Attend the first and the last class. They are often the most information-dense, so you'll regret skipping them. (I am not saying it's ok to sleep through all the lectures in between... :)Ask questions—during lectures, discussion sections, office hours. You are not taking an online course, so make the most out of the "face to face" experience.Make sure the professors know who you are. If you decide to go to grad school, you'll need letters of recommendation. The best way to get them is to make an impression during class, or do research.Use the opportunity to make friends—social life is a huge part of the experience. Most of my close friendships started during Berkeley days; a large part of my professional network is Berkeley-centric as well.More Berkeley-specific:Take courses outside your major. L&S breadth requirements force you to do it, but it's a good idea anyway. One of the unique aspects of UC Berkeley is that academically it's world-class across all the departments, in humanities and engineering and science. I greatly enjoyed many courses that were outside of CS.Enroll in a seminar. They compliment large introductory courses. See http://fss.berkeley.edu/Take a class with an instructor who received a Distinguished Teaching Award. These are the best of the best teachers. See http://teaching.berkeley.edu/dta.htmlLive within walking distance of campus. Yes, you can save money by living further away, but in my opinion being close to the center of both social and academic life justifies the incremental cost increase.Rules are meant to be bent. There is a large number of rules you have to deal with at Berkeley, governing the requirements for graduation, who can get into what class and when, etc. What they don't advertise is that if you are persistent enough you can often get an exception, especially if you have a good reason for it. If you keep showing up for a class that is full, they may let you in if someone drops it. Prerequisites can be dropped at professor's discretion, some requirements can be waived with the approval of department head.Learn the history of UC Berkeley. Make sure you know what FSM stands for. There are many reasons to be proud of Cal irrespective of how the football team is doing this season!