Mastering Time Series Analysis and Forecasting with Python
Sulekha Aloorravi, 2024

🖥 Top Programming Languages to learn in 2025 - [Part 1] 🖥


1. JavaScript
- learnjavascript.online
- t.iss.one/javascript_courses/4
- learn-js.org

2. Java
- learnjavaonline.org
- javatpoint.com

3. C#
- learncs.org
- w3schools.com

4. TypeScript
- Typescriptlang.org
- learntypescript.dev

5. Rust
- rust-lang.org
- t.iss.one/crackingthecodinginterview/724
- exercism.org

🖥 Top Programming Languages to learn in 2025 - [Part 2] 🖥


6. Go PRogramming
- go.dev
- learn-golang.org

7. Kotlin
- kotlinlang.org
- w3schools.com/KOTLIN

8. Python
- learnpython.org
- t.iss.one/pythonanalyst

9. SQL
- learnsql.com
- t.iss.one/sqlanalyst

10. R Programming
- w3schools.com/r/
- r-coder.com

Three different learning styles in machine learning algorithms:

1. Supervised Learning

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.

Example problems are classification and regression.

Example algorithms include: Logistic Regression and the Back Propagation Neural Network.

2. Unsupervised Learning

Input data is not labeled and does not have a known result.

A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, dimensionality reduction and association rule learning.

Example algorithms include: the Apriori algorithm and K-Means.

3. Semi-Supervised Learning

Input data is a mixture of labeled and unlabelled examples.

There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

Example problems are classification and regression.

Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

Tips for solving leetcode codings interview problems

If input array is sorted then
- Binary search
- Two pointers

If asked for all permutations/subsets then
- Backtracking

If given a tree then
- DFS
- BFS

If given a graph then
- DFS
- BFS

If given a linked list then
- Two pointers

If recursion is banned then
- Stack

If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer

If asked for maximum/minimum subarray/subset/options then
- Dynamic programming

If asked for top/least K items then
- Heap

If asked for common strings then
- Map
- Trie

Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space

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📚 Useful Resources for the Programmers

Data Analyst Roadmap
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Free C course from Microsoft
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Oops in Java
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ENJOY LEARNING 👍👍