Dr. Suat AtanPhD

AÇIK ARŞİV ·

Data Science Learning Library

· Yazan · 5 dk okuma

Data Science Learning Library

I am curating resources for data science and sometimes about data engineering, following an ultralearning curriculum approach.

LinkedIn Saved Posts: https://www.linkedin.com/my-items/saved-posts/ (Personal Collection)

📚 Completed Courses

✅ MIT Free Course: Introduction To Probability And Statistics

Source: MIT OpenCourseWare
Status: Completed
Topics: Probability theory, statistical inference, hypothesis testing

✅ DeepLearning AI: Serverless LLM Apps with Amazon Bedrock

Source: DeepLearning.AI
Status: Completed
Topics: Large Language Models, AWS Bedrock, Serverless architecture

🍁 Google Cloud Skills Boost

Source: Google Cloud Skills
Status: Ongoing
Topics: Cloud computing, data engineering, machine learning on GCP

💡 Featured Learning Resources

Prompt Engineering Mastery

✅ Youssef Hosni’s Prompt Engineering Course
Repository: GitHub - Prompt Engineering for Instruction-Tuned LLM
Status: Completed

📖 Essential Articles & Papers

Neural Networks Innovation

“Kolmogorov-Arnold Networks: the latest advance in Neural Networks, simply explained”
Source: Towards Data Science
Topic: Advanced neural network architectures

LLM Deployment Comprehensive Guide

“Deploying LLMs: Top Learning & Educational Resources to Get Started”
Source: LinkedIn Article

1. Local Deployment

  • 5 Free Tools to Run LLMs Locally: Guide

2. Demo Deployment

3. Server Deployment

4. Edge Deployment

🛠️ Core Libraries & Tools

AutoGluon (Amazon’s AutoML)

Homepage: https://auto.gluon.ai/stable/index.html

Key Features:

  • Quick Prototyping: Build ML solutions on raw data in few lines of code
  • State-of-the-art Techniques: Automatically utilize SOTA models without expert knowledge
  • Easy Deployment: Move from experimentation to production with cloud predictors
  • Customizable: Extensible with custom feature processing, models, and metrics

Use Cases:

  • Rapid ML prototyping
  • Automated model selection
  • Production-ready deployments
  • Custom ML pipelines

🎯 Learning Paths

Beginner Track

Perfect for those starting their data science journey with no prior experience.

Phase 1: Foundations (4-6 weeks)

  • Statistics Fundamentals
    • Descriptive statistics
    • Probability distributions
    • Hypothesis testing
    • Confidence intervals
  • Python Programming Basics
    • Variables and data types
    • Control structures
    • Functions and modules
    • File handling

Phase 2: Data Manipulation (6-8 weeks)

  • Pandas Library
    • DataFrames and Series
    • Data cleaning techniques
    • Grouping and aggregation
  • NumPy for Numerical Computing
    • Array operations
    • Mathematical functions
    • Broadcasting

Intermediate Track

For learners with basic programming knowledge and statistics background.

Machine Learning Fundamentals (8-10 weeks)

  • Supervised Learning
    • Linear and logistic regression
    • Decision trees and random forests
    • Support vector machines
  • Unsupervised Learning
    • K-means clustering
    • Hierarchical clustering
    • Principal component analysis

Advanced Track

Designed for experienced practitioners seeking specialization.

Deep Learning & NLP (10-12 weeks)

  • Deep Learning with TensorFlow/Keras
    • Neural network architectures
    • Convolutional neural networks
    • Recurrent neural networks
  • Natural Language Processing
    • Text preprocessing
    • Sentiment analysis
    • Transformer models

📊 Practical Projects

Beginner Projects

  1. Exploratory Data Analysis
    • Dataset: Titanic survival data
    • Skills: Pandas, Matplotlib, Seaborn
  2. Predictive Modeling
    • Dataset: House prices
    • Skills: Regression, feature engineering

Intermediate Projects

  1. Image Classification
    • Dataset: CIFAR-10
    • Skills: CNNs, transfer learning
  2. Natural Language Processing
    • Dataset: Movie reviews sentiment
    • Skills: Text processing, classification

Advanced Projects

  1. Recommendation System
    • Dataset: MovieLens
    • Skills: Collaborative filtering, matrix factorization
  2. Time Series Forecasting
    • Dataset: Stock prices or weather data
    • Skills: ARIMA, LSTM, Prophet

🎓 Career Guidance

Job Roles in Data Science

  1. Data Analyst
    • Entry level position
    • Skills: SQL, Excel, basic Python/R
  2. Data Scientist
    • Mid-level position
    • Skills: ML algorithms, statistics, programming
  3. Machine Learning Engineer
    • Technical role
    • Skills: Software engineering, MLOps, cloud
  4. Data Engineer
    • Infrastructure focus
    • Skills: Big data tools, cloud platforms

🤝 Community & Resources

Online Communities

  • Reddit: r/MachineLearning, r/datascience
  • Discord: Data Science Community servers
  • Stack Overflow: Programming help
  • Kaggle Forums: Competition discussions
  • Coursera: Machine Learning Specialization (Stanford)
  • edX: MIT Introduction to Data Science
  • Kaggle Learn: Free micro-courses

This library is continuously updated with new resources, tutorials, and industry trends. Check back regularly for the latest content and learning opportunities.

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