⭐️6 Tips to Study Coding Effectively⭐️
by UFV Academic Success Centre

1. Don’t just read the code example—Type it out and then create a similar one
🔹 A code sample is the representation of the idea or program.
🔹 Type it in your own words to understand how the five components are working together.
🔹 Create a similar sample to understand the abstract of the program.
🔹 Try some code challenges from some well-known websites, such as leetcode, codewars, and
topcoders.

2. Practice and keep track of what you have learned
🔹 Practice makes perfect.
🔹 As a programmer, you often can have some repetitive tasks. Keeping track of what you learn will
help you quickly refer back to the tasks you have done.
🔹 Document what you have learned. Documentation is a good resource to help you look up the
algorithm/solution and repetitive tasks easily and increase your productivity rapidly.

3. Try to create, then build your own program
🔹 Apply what you have learned to a real-life example.
🔹 Building your own program brings you to the next level of program abstract and will help you feel
satisfied and accomplished with what you have learned.
🔹 When you come up with a solution, try a different approach. There is more than one right way to
do something, and searching for different solutions will help you develop your problem solving
skills.

4. Learn how to research and solve problems
🔹 Search for topics by specific keywords.
🔹 Learn how to research your problem when you get stuck. Some websites may help, such as
stackoverflow, stackexchange, github, and forums.
🔹 If you find a solution online, make sure you understand every line of code. You will learn more this
way rather than just copying and pasting it into your project.

5. Take a break while debugging
🔹 Consider taking break to clear your mind when you encounter difficult bug.
🔹 Stepping away for a few hours will allow you to return with a fresh perspective.

6. Things to avoid
🔹 Perfection: As a beginner, improving your coding skills and problem solving are more important
than making your code perfect. Seeking perfection will cause you to procrastinate instead of
progress. Remember that mistakes are opportunities to learn.
🔹 Comparison: Never compare your code style/knowledge with anyone else. You will end up being
disappointed and demotivated. Practice and trust yourself.
🔹 Complexity: Learn how to break a problem into smaller problems, so you can conquer it more
easily.
A good programmer is able to make a program simpler and less complex. Make it work first, then
make it right, finally make it fast. “Simplicity is the ultimate sophistication,” said Leonardo Da Vinci.

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Some important questions to crack data science interview

Q. Describe how Gradient Boosting works.

A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.


Q. Describe the decision tree model.

A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.


Q. What is a neural network?

A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.


Q. Explain the Bias-Variance Tradeoff

A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.


Q. What’s the difference between L1 and L2 regularization?

A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.

ENJOY LEARNING 👍👍

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