Mathematics Eligibility

You can apply for an M.Sc. in data Science after doing a B.Sc. In Maths. M.Sc. Data Science or M.Sc. Data Analytics are near equal degrees. Data Science is a field where Mathematics and Statistics are visually coming alive on scientific decision making based on analyzing any data, be it business, industrial, social, healthcare, pharma, media and so on. You will love to admire your Mathematics and Statistics that you hated while doing them at your B.Sc. Mathematics/Statistics level. You will feel proud that you have chosen the right course at M.Sc. Level in Data Science that will propel you as Data Scientist to which very few contestants are there in the final.
Is it good to do an M.Sc. In data Science after a B.Sc. In Statistics to get jobs? I think a lot of that depends on what hard technical skills you already have. While many people think that more Statistics you know the better, most of what you do in data Science is not straight up Statistics or algorithm creation. Instead there are typically building and deploying models using known and existing algorithms. There are things that you will get out of a data Science programme that you will not get out of a Statistics programme (although this is changing to some extent because of the growth of data Science). .

You can take admission in BCA as math is not compulsory. Other eligibility requirements, however, must be MET. The programme requires a pass in 10+2 (with English or equivalent with a minimum of 50% aggregate marks. NASSCOM has placed LPU's School of Computer Application among the top four Computer Applications Institutes in North India. The curriculum introduces students to the principles of information technology and gives knowledge of network tools and procedures through a grasp of existing wired and wireless networking models and protocols. The Open Systems Interconnection communication model, local area networks, bridges, routers, and gateways, addressing, and local and remote processes are among the topics covered in the course. Physical layer fundamentals, network protocol methods, error handling, flow management, multihop routing network reliability, timing, sophisticated network protocols, and infrastructure, as well as high-performance network applications are all simulated. Hope this information is helpful!