I too am in the same situation, I study for a masters in Software Engineering.
I recently did a course on Algorithmics, since this is not directly coding or learning of specific technology it might seem a bit theoretical. However that said thinking about different ways to solve Algorithmic problems in general gives us:
Divide and Conquer.
Dynamic programming.
Greedy Approaches.
This gives us ways of looking a problem and potential ways in which we can get good solutions.
Then we have ways to compare different algorithms in terms of time and space complexity using Algorithmic Complexity and the big O notification. Then a debate about P and NP complexity, the NP complete problem is something that I'm not going to work on myself, but it's very good to have an awareness of what problems might be NP complete. And why they are computational hard.
Also some of the stuff might be theoretical looking now, but in the years to come might be more applicable.
Example:
At University (12 years ago) I did a module on Neural Networks, we learnt what a preceptron is, how it works mathematically, and how to create multi-layer perceptron to solve problems that are non-linearly separable, what activation functions might be most useful, sigmoid or linear the pros and cons of each. We then developed a multi-layer preceptron program from scratch and taught it to solve a noughts and crosses game. 9 inputs, 4 layers of 10 nodes and 9 outputs, it took ages to learn and do the back propagation properly, even with momentum.
Point being:
Fast forward 12 years and Data Mining and AI are the hottest topics across the world and people are using them to solve lot's of problems in real world applications. I don't work in AI or Data mining yet I know the math and the ideas behind Nerual Networks, which 12 years ago was a bit of a pipe dream, but with the increase in CPU power we can now do things like this. Yet how many people using NN know the theory behind them or do they use use them to do a job like a black box ?
Knowledge is power, even if it's theoretical. Good luck.
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I too am in the same situation, I study for a masters in Software Engineering.
I recently did a course on Algorithmics, since this is not directly coding or learning of specific technology it might seem a bit theoretical. However that said thinking about different ways to solve Algorithmic problems in general gives us:
Divide and Conquer.
Dynamic programming.
Greedy Approaches.
This gives us ways of looking a problem and potential ways in which we can get good solutions.
Then we have ways to compare different algorithms in terms of time and space complexity using Algorithmic Complexity and the big O notification. Then a debate about P and NP complexity, the NP complete problem is something that I'm not going to work on myself, but it's very good to have an awareness of what problems might be NP complete. And why they are computational hard.
Also some of the stuff might be theoretical looking now, but in the years to come might be more applicable.
Example:
At University (12 years ago) I did a module on Neural Networks, we learnt what a preceptron is, how it works mathematically, and how to create multi-layer perceptron to solve problems that are non-linearly separable, what activation functions might be most useful, sigmoid or linear the pros and cons of each. We then developed a multi-layer preceptron program from scratch and taught it to solve a noughts and crosses game. 9 inputs, 4 layers of 10 nodes and 9 outputs, it took ages to learn and do the back propagation properly, even with momentum.
Point being:
Fast forward 12 years and Data Mining and AI are the hottest topics across the world and people are using them to solve lot's of problems in real world applications. I don't work in AI or Data mining yet I know the math and the ideas behind Nerual Networks, which 12 years ago was a bit of a pipe dream, but with the increase in CPU power we can now do things like this. Yet how many people using NN know the theory behind them or do they use use them to do a job like a black box ?
Knowledge is power, even if it's theoretical. Good luck.