Since most colleges only offere general computer Science as a course, how can you use that later when applying to more specific technical jobs to gain knowledge about computer science deeply like analytics?

Just speaking from personal experience and observation, the general computer science knowledge that you absorb in college will give you a broad foundation on which to build. Given this general understanding, it is much easier to learn additional specific skills. Once you enter the workforce, your employer will most likely train you for even more additional skills, based on their specific needs. For example, when I was hired by my current employer, I spent several weeks in training about our platform, tools, proprietary programming, etc.. Having my base knowledge made it much easier to add on these additional skills. Like many things, the more you practice, the better you get. This includes learning new software and new programming languages. Once you've learned one or two, it becomes much easier to learn three, four, and so on.

As a side note, <span style="color: rgb(93, 103, 106);"> many colleges do offer more in-depth courses on subjects such as AI, robotics, data science, etc. as well once you've completed some of the basic required courses.</span>

Studying computer science is mostly about learning the fundamental building blocks that you'll use later in your career to self serve and learn new things. Technology moves so fast that is not realistic to expect colleges to keep up with it, and the best professionals that I've ever met are great at self-teaching new things.

With time and experience you'll be able to become an expert in the field of your interest, like analytics, and can ever support that with advanced courses later on, but I would not worry about having a general view at college level.

Computer courses help you master the basics of computer science. Think of it as if you learning to drive a car. And once learn to drive a car you should be able to handle most of the car with little or more knowledge. Basically, once you are good at basics it will not be difficult for you to learn and adapt to a specific technical job.

'Analytics' is a fairly broad term that includes operational reporting, data science and machine learning. The distinction between each of these sub-fields is whether they answer questions like "what happened?", "why did it happen?" or "what will happen?".

You don't necessarily need an education in computer science to be really good at analytics, in fact a lot of my amazing current and previous co-workers who are data scientists, do not have a background in computer science. They come from all fields ranging from statistics, electrical engineering, operations research, experimental physics and bioinformatics. With a formal education in computer science however, you will understand the basics of data structures that may come in handy while analyzing datasets, the complexities of algorithms which will help you scale your analysis to larger datasets and you'll learn about programming and software engineering, which will help you write custom code to analyze data when off-the-shelf tools don't fit your needs.

A computer science education will give you with the necessary tools to start exploring datasets on your own. If your university offers a course on Python programming, a practical introduction to database systems and SQL and an introduction to statistics, I'd encourage you to enroll in them. If not, there are also plenty of resources online like Udemy (https://www.udemy.com) or Coursera (https://www.coursera.org/) that offer good introductory and advanced courses on these topics. If you're specifically <span style="color: rgb(93, 103, 106);">interested</span> in 'predictive analytics', I'd also encourage you to enroll in a course on linear algebra and machine learning. If your university doesn't offer courses in these areas, there are plenty of resources online like Prof. Gilbert Strang's course on Linear Algebra offered through the MIT open courseware (https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) and machine learning by Prof. Andrew Ng offered on Coursera (https://www.coursera.org/learn/machine-learning). There are also some excellent slides on various topics in machine learning by Prof. Andrew Moore from CMU (http://www.cs.cmu.edu/~./awm/tutorials/index.html).

Once you are armed with the above basics, I'd encourage you to participate in or solve previous Kaggle competitions. I'd also encourage you to read through the discussion board to learn about how other students are approaching the same problem. The advantage with Kaggle is that it provides you a dataset and a well defined problem on which you could train your models on. I'd also encourage you to sign-up for free on HackerRank (https://www.hackerrank.com) as they too have a variety of problem sets in programming, database applications and data science that you can work on. In the real world, in the industry however, often the hardest part of analytics might be defining the problem or picking the right problem and in putting together a dataset to solve that problem. Some of these skills will come with experience on the job (you can gain some of this by applying for analytics/data science internships) and others come by talking to business experts who know their data the best. As with any other skill, there is no substitute for practice, so the more problems you solve, the better will you get at it.

i did cs as we (a while back). Since data science is new, to learn it I entered ds competitions (eg) kaggle. They give well defined problems with datasets you can analyze. Gives you close to real life experience without having through it in real life. I didn’t win anything but learned a lot!