9 Types of API Testing

800+ Data Science Interview Questions – A Must-Have Resource for Every Aspirant

Breaking into the data science field is challenging—not because of a lack of opportunities, but because of how thoroughly you need to prepare.

This document, curated by Steve Nouri, is a goldmine of 800+ real-world interview questions covering:

-Statistics
-Data Science Fundamentals
-Data Analysis
-Machine Learning
-Deep Learning
-Python & R
-Model Evaluation & Optimization
-Deployment Strategies
…and much more!

🚀 Required Skills for a data scientist

🎯Statistics and Probability
🎯Mathematics
🎯Python, R, SAS and Scala or other.
🎯Data visualisation
🎯Big data
🎯Data inquisitiveness
🎯Business expertise
🎯Critical thinking
🎯Machine learning, deep learning and AI
🎯Communication skills
🎯Teamwork

ML Algorithms 💪

NLP techniques every Data Science professional should know!

1. Tokenization
2. Stop words removal
3. Stemming and Lemmatization
4. Named Entity Recognition
5. TF-IDF
6. Bag of Words

List of Top 12 Coding Channels on WhatsApp:

1. Python Programming:
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2. Coding Resources:
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5. Java Programming:
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6. Javascript:
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7. Web Development:
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8. Artificial Intelligence:
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9. Data Science:
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10. Machine Learning:
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11. SQL:
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12. GitHub:
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ENJOY LEARNING 👍👍

Key trends shaping the future of web development 👇👇

1. Progressive Web Apps (PWAs): PWAs are becoming more popular as they combine the best of web and mobile apps, offering a seamless experience across platforms without needing app stores.

2. WebAssembly (Wasm): WebAssembly allows developers to run code written in different languages (C++, Rust) on the web with near-native performance, enhancing web application speed and capabilities.

3. AI-Powered Web Development: Artificial Intelligence (AI) and Machine Learning (ML) will become more integrated into web development, enabling features like chatbots, personalized content, predictive search, and automated design processes.

4. Voice Search Optimization: As voice search continues to grow, web developers will focus on optimizing websites for voice-activated queries, leading to changes in search engine optimization (SEO) practices and user experience design.

5. Serverless Architecture: Serverless computing allows developers to build and deploy applications without managing infrastructure. This reduces costs, enhances scalability, and enables faster development cycles.

6. Motion UI: Animation and micro-interactions will play a bigger role in web design. Motion UI helps create engaging, interactive experiences that can improve user engagement and satisfaction.

7. 5G and Enhanced Connectivity: With the rollout of 5G, faster internet speeds and lower latency will enable more complex, real-time applications, especially in areas like augmented reality (AR), virtual reality (VR), and IoT.

8. Blockchain Integration: Web development could integrate blockchain technology for decentralized applications (dApps), offering enhanced security, transparency, and user control over data.

9. Edge Computing: By bringing computing closer to the source of data, edge computing will reduce latency and improve the performance of web applications, especially for IoT and real-time data processing.

10. Cybersecurity Focus: As web applications handle more sensitive data, the importance of robust security practices, such as multi-factor authentication (MFA), encryption, and secure development frameworks, will grow.

Top 10 Data Science Concepts You Should Know 🧠

1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.

2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.

3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.

4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.

5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.

6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.

7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.

8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!

9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.

10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

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