Statistics

Graduates of Columbia University's MS in Computer Science degree can anticipate a variety of employment options in a number of industries. The Department of Computer Science works in conjunction with the Graduate Career Placement Team and the CCE to host numerous career-related events each semester, including employer information sessions, tech talks, alumni panels, and much more. According to the Computer Science placement data of December 2023- Average salary of CS graduates- $148,274. Median Salary- $140,000.Top industries- Automative, Software and Technology, Research, E-Commerce, Fintech/Financial Service.Top Recruiters- Microsoft, Meta, Google, Stripe, TikTok, Bloomberg, CapitalOne and many more.  

Hi Sagnik, To start preparing for the M.Sc. Statistics through CUET PG, follow these steps:Understand the Syllabus: Focus on core subjects like probability, algebra, calculus, and statistical methods. Review the CUET PG Statistics syllabus thoroughly.Create a Study Plan: Organize your study schedule, dedicating time to each topic based on their weightage in the exam.Use Reliable Study Materials: Refer to standard textbooks and study guides for Statistics. You can also use online resources and video lectures to understand complex topics better.Practice Regularly: Solve previous years' question papers and take mock tests to get familiar with the exam format.Focus on Basics: Strengthen your foundation in Mathematics and Statistics, as they are crucial for understanding higher-level concepts.Good luck with your preparation!

Hi!After completing M.Sc in Statistics, for UGC NET Paper 2, the most logical choice would be to select "Statistics" as your subject. Here's why:Expertise: Since you've specialised in Statistics, this will allow you to leverage your knowledge and skills, making it easier for you to perform well in the exam.Relevant Content: The syllabus for UGC NET in Statistics aligns with what you've learned in your M.Sc program, so you'll be comfortable with most of the topics.Career Focus: Choosing Statistics for UGC NET can open doors for teaching opportunities in universities or research institutes, focusing on statistics and data analysis. Best of luck with your preparation! 

Hello!M.Sc. Business Statistics and M.Sc. Data Science are related but not exactly the same. Business Statistics focuses more on the application of statistical methods and tools in business contexts, including data analysis, forecasting, and decision-making. On the other hand, M.Sc. Data Science is broader and encompasses not just statistics, but also programming, machine learning, data visualization, and big data technologies.While an M.Sc. in Business Statistics may not directly prepare you for an AI Engineer role (which requires strong programming and machine learning expertise), you can certainly pursue AI roles with additional skills or training in programming languages (like Python, R) and machine learning techniques. For AI Engineer roles, you would typically need to understand deep learning, neural networks, and advanced algorithms. Good luck with your career path!

The choice between a PG Diploma in Applied Statistics and an M.Sc in Applied Statistics largely depends on your career goals, the depth of knowledge you wish to gain, and the time you're willing to commit.PG Diploma in Applied Statistics: This is typically a shorter, more focused programme that provides practical skills in statistics. It's ideal if you want to enter the job market quickly or enhance your existing knowledge without committing to a full-time, long-duration course. It's generally more affordable and may suit those looking to upgrade their skills for specific roles in data analysis or related fields.M.Sc in Applied Statistics: An M.Sc is a comprehensive and in-depth program, providing a strong foundation in both theory and application. It opens up higher-level career opportunities, including roles in research, academia, or advanced data analytics. The course duration is longer, typically two years, and it also offers a deeper understanding of statistical concepts and methodologies.