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دسته بندی دوره ها

Data Science Innovations: Advanced Methods, Interactive Labs

سرفصل های دوره

Data Analysis, ML Algorithms, Python Programming, Real-World Projects. Perfect for beginners and aspiring professionals.


1. Introduction to Advanced Data Science
  • 1. Introduction.html
  • 2. The Evolution of Data Science Unveiling the Historical Journey and Contemporary.html
  • 3. Mastering Advanced Data Science Essential Skills and Techniques.html
  • 4. Real-World Data Science Case Studies and Transformative Insights.html
  • 5. Exploring the Data-Driven World Challenges, Opportunities, and Future Growth in.html
  • 6. Foundations of Data Science Essential Concepts and Techniques for Beginners.html
  • 7.1 Commands to install the jupyter-notebook in the linux.txt
  • 7. Install the Jupyter-notebook in the linux from Basics
  • 8. install jupyter with python in linux with the help of pip command
  • 9. Jupyter-notebook introduction how to use it, how to run it from basics
  • 10. What is Variable in the python from Basics
  • 11. Reflecting the Data Science Odyssey Milestones and Paradigm Shifts.html
  • 12. Data Odyssey Unveiling Proficiency in Essential Skills & Techniques.html
  • 13. Data Insights Unveiled Unraveling Real-World Challenges.html
  • 14. Variable & Data Types Basic Operations with Integers, Floats, and Strings.html
  • 15. Arithmetic Operations in Python.html
  • 16. Python Data Structures and Syntax.html
  • 17. Conditional Operations with If Statements.html
  • 18. Master the art of iterating over lists, tuples, and dictionaries using both for and while loops..html
  • 19. Mastering Conditional Expressions Ternary Operator.html
  • 20. Function Mastery & File Manipulation Parameterized Operations, Return Values, and File IO.html

  • 2. Mastering Data Analysis Techniques
  • 1. Advanced Statistical Analysis for Data Science Unleash the Power of Data Insigh.html
  • 2. Data Science Essentials Master Descriptive Statistics for Powerful Data Analysi.html
  • 3. Advanced Inferential Statistics for Data Analysis and Decision Making.html
  • 4. Mastering Advanced EDA Techniques for Deeper Data Insights.html
  • 5. Advanced Pattern Recognition Techniques for Data Analysis and Anomaly Detection.html
  • 6. CSV File Data Reading & Loading in Python.html

  • 3. Advanced Data Visualization
  • 1. Mastering Data Visualization Unlocking Insights through Effective Visual Commun.html
  • 2. Mastering Data Visualization Choosing the Right Chart for Effective Communicati.html
  • 3. Interactive Data Visualization with Python Creating Engaging Visualizations Usi.html
  • 4. Mastering Geographic Data Visualization Unleash the Power of Spatial Insights.html
  • 5. Data Visualization and Storytelling Crafting Compelling Narratives for Insights.html

  • 4. Essential Machine Learning Algorithms
  • 1. Mastering Machine Learning Algorithms A Comprehensive Guide for Data Science Pr.html
  • 2. Supervised Learning Building Predictive Models for Data Analysis.html
  • 3. Unsupervised Learning Exploring Patterns in Unlabeled Data.html
  • 4. Ensemble Learning Boosting and Bagging for Improved Predictions.html
  • 5. Mastering Classification and Regression in Machine Learning Advanced Techniques.html

  • 5. Model Evaluation and Optimization
  • 1. Evaluating Model Performance Metrics and Strategies for Machine Learning.html
  • 2. Advanced Hyperparameter Tuning for Enhanced Model Performance in Data Science.html
  • 3. Robust Model Evaluation with Cross-Validation Techniques in Data Science.html
  • 4. Understanding the Bias-Variance Tradeoff in Data Science Achieving Optimal Mode.html
  • 5. Advanced Model Optimization Fine-tuning Machine Learning Models for Optimal Per.html

  • 6. Advanced Feature Engineering
  • 1. Advanced Feature Importance Techniques for Machine Learning Models.html
  • 2. Advanced Dimensionality Reduction Techniques in Data Science.html
  • 3. Handling Categorical Data in Machine Learning Strategies and Techniques for Dat.html
  • 4. Advanced Feature Scaling Techniques for Unbiased Machine Learning.html
  • 5. Time Series Feature Engineering Enhancing Machine Learning Models with Temporal.html

  • 7. Exploring Deep Learning
  • 1. Deep Learning Fundamentals Exploring Concepts and Architectures.html
  • 2. Deep Dive into Neural Networks Building Blocks of Deep Learning Models.html
  • 3. Advanced Image Processing and Recognition Using Convolutional Neural Networks (C.html
  • 4. Advanced Recurrent Neural Networks (RNNs) for Sequential Data Modeling.html
  • 5. Efficient Deep Learning with Transfer Learning Unlocking the Power of Pre-Train.html

  • 8. Natural Language Processing (NLP)
  • 1. Advanced Natural Language Processing Foundations and Concepts for Data Science.html
  • 2. Text Preprocessing in NLP Clean and Transform Text Data Effectively.html
  • 3. Advanced Named Entity Recognition (NER) for Text Analysis.html
  • 4. Sentiment Analysis Extracting Emotions from Textual Data.html
  • 5. Advanced Named Entity Recognition (NER) for Text Analysis.html

  • 9. Recommendation Systems
  • 1. Mastering Recommendation Systems Personalized Content Delivery.html
  • 2. Advanced Collaborative Filtering Techniques for Recommendation Systems.html
  • 3. Advanced Content-Based Filtering for Personalized Recommendations in Data Scienc.html
  • 4. Advanced Matrix Factorization Models for Recommendation Systems.html
  • 5. Evaluating Recommendation Systems Metrics for Performance Assessment.html

  • 10. Ethical Considerations in Data Science
  • 1. Advanced Techniques for Deploying Machine Learning Models in Production.html
  • 2. Scalability and Performance Optimization for Machine Learning Models.html
  • 3. Advanced Techniques for Monitoring and Maintaining Deployed Machine Learning Mod.html
  • 4. Ethical Considerations in Model Deployment Ensuring Fairness, Transparency, and.html
  • 5. Real-World Model Deployment Case Studies Challenges, Solutions, and Lessons.html

  • 11. Deploying Machine Learning Models
  • 1. Advanced Fraud Detection Machine Learning Techniques for Fraud Identification.html
  • 2. Advanced Healthcare Analytics Predictive Modeling and Optimization for Data-Dri.html
  • 3. Financial Forecasting with Data Science Time Series Analysis, Risk Assessment,.html
  • 4. Advanced Techniques in Image and Video Analysis with Machine Learning.html
  • 5. Genomic Data Analysis Unleashing the Power of Data Science in Genomics.html

  • 12. Career Guidance and Job Placement
  • 1. Data Science Capstone Project Kickoff Guided Initiation and Scope.html
  • 2. Advanced Data Science Capstone Project Implementation.html
  • 3. Data Analysis and Visualization A Comprehensive Guide for Aspiring Data Science.html
  • 4. Comprehensive Capstone Project Documentation Methodologies, Findings, and Insig.html
  • 5. Mastering Presentation Skills for Effective Data Science Communication.html
  • 6. Data Science Capstone Project Peer Review and Feedback.html

  • 13. Continuous Learning Opportunities
  • 1. Exploring AI and Machine Learning Trends Unveiling the Future of Data Science.html
  • 2. Advanced Big Data Analytics Mastering Tools and Technologies for Data Science.html
  • 3. Exploring Explainable AI Enhancing Model Interpretability and Transparency.html
  • 4. Edge Computing in Data Science Enhancing Real-Time Data Processing and Analytic.html
  • 5. Exploring the Synergy of Quantum Computing and Data Science Algorithms, Applica.html

  • 14. Building a Data Project Portfolio
  • 1. Building a Data Science Portfolio Showcasing Your Skills and Achievements.html
  • 2. Mastering Data Science Job Search Strategies Networking, Resumes, and Interview.html
  • 3. Navigating Career Transitions in Data Science Specialization, Upskilling, and I.html
  • 4. Industry Certifications Boost Your Data Science Career with Credibility.html
  • 5. Embracing Continuous Learning in Data Science Stay Ahead of the Curve.html

  • 15. Lifelong Learning and Growth Mindset
  • 1. Emerging Technologies in Data Science Exploring the Future of AI, Machine Learn.html
  • 2. Data Science in 2030 Exploring the Future of Data-driven Insights.html
  • 3. Future-proofing Data Science Navigating Ethical Considerations in the Age of Da.html
  • 4. Data Science for Sustainability Leveraging Data-driven Approaches for Environme.html

  • 16. Section 15 Course Conclusion and Certification
  • 1. Python Coding Challenges Enhance Your Data Science Skills with Challenging Exer.html
  • 2. Python Data Manipulation Exercises Master Data Cleaning, Transformation, and An.html
  • 3. Machine Learning Algorithm Practice Hands-on Coding for Data Science.html
  • 4. Advanced Visualization Coding Interactive Data Visualizations with Matplotlib a.html

  • 17. Advanced Feature Engineering
  • 1. Mastering Feature Engineering Unlocking the Power of Data Insights.html
  • 2. Advanced Feature Engineering Strategies Unlocking Complex Patterns in Data.html
  • 3. Optimizing Model Performance The Role of Feature Engineering for Data Science P.html
  • 4. Mastering Time Series Feature Engineering for Accurate Predictive Analytics.html
  • 5. Ethical Considerations in Feature Engineering Bias, Fairness, and Transparency.html

  • 18. Time Series Analysis and Forecasting
  • 1. Mastering Time Series Analysis Foundations, Fundamentals, and Predictive Modeli.html
  • 2. Mastering Advanced Forecasting Techniques for Enhanced Data Insights.html
  • 3. Enhancing Time Series Forecasting Advanced Optimization Strategies.html
  • 4. Temporal Anomaly Detection Identifying Outliers in Time Series Data.html
  • 5. Real-World Applications of Time Series Forecasting Case Studies and Insights.html

  • 19. Anomaly Detection
  • 1. Unmasking Anomaly Detection Principles and Techniques.html
  • 2. Advanced Fraud Detection with Anomaly Detection Techniques Strategies and Best.html
  • 3. Optimizing Anomaly Detection Models Fine-Tuning for Precision.html
  • 4. Temporal Anomaly Detection Monitoring Unusual Patterns Over Time.html
  • 5. Beyond Security Anomaly Detection in Diverse Data Domains.html

  • 20. Bayesian Methods in Data Science
  • 1. Exploring Bayesian Statistics A Practical Guide for Data Scientists.html
  • 2. Bayesian Inference Making Informed Decisions with Data Uncertainty.html
  • 3. Optimizing Model Uncertainty Bayesian Approaches for Enhanced Predictions.html
  • 4. Bayesian Machine Learning Integrating Probabilistic Models.html
  • 5. Ethical Considerations in Bayesian Data Analysis Transparency and Fairness.html

  • 21. Reinforcement Learning
  • 1. Essentials of Reinforcement Learning An Introduction for Beginners.html
  • 2. Reinforcement Learning Markov Decision Processes for Sequential Decision-Making.html
  • 3. Deep Reinforcement Learning Navigating Sequential Decision-Making.html
  • 4. Policy Gradient Methods Training Agents for Optimal Decision Strategies.html
  • 5. Unlocking Success Real-World Applications of Reinforcement Learning.html

  • 22. Big Data Analytics with Spark
  • 1. code example for a data science exercise, along with enhanced features, clear ex.html
  • 2. Efficient Data Processing with Spark Optimizing Large-Scale Analytics.html
  • 3. Simplifying Big Data Querying and Analysis with Spark SQL and DataFrames.html
  • 4. Machine Learning with Spark Harnessing Big Data for Predictive Analytics.html
  • 5. Real-Time Big Data Analytics Unleashing Insights with Spark Streaming.html

  • 23. Explainable AI (XAI)
  • 1. Demystifying Explainable AI Principles and Importance.html
  • 2. Interpretable Models in Machine Learning A Comprehensive Overview.html
  • 3. Demystifying Black Box Models Techniques for Explainability.html
  • 4. Applications of Explainable AI Real-World Impact and Success Stories.html
  • 5. Ethical Considerations in Explainable AI Balancing Transparency and Complexity.html

  • 24. Meta-Learning and AutoML
  • 1. Meta-Learning Fundamentals Building Adaptive Models for Efficient Learning.html
  • 2. Demystifying AutoML Streamlining Model Building with Automated Machine Learning.html
  • 3. Optimizing Models Across Tasks Hyperparameter Tuning in Meta-Learning.html
  • 4. Accelerating Model Performance with Transfer Learning in AutoML.html
  • 5. Exploring Meta-Learning and AutoML Challenges, Opportunities, and Future Implic.html

  • 25. Data Science Coding Challenges Mastering Concepts through Hands-On Exercises
  • 1. Excel Mastery Unraveling Data Insights.html
  • 2. Advanced Excel Data Analysis Mastering Techniques for In-Depth Insights & more+.html
  • 3. Understanding Data Science Essentials (On practical Advanced level's).html

  • 26. Multi-Language Data Access Bridging Python, R, and SQL (Only for Data Science)
  • 1. Data Science Essentials Mastering Variables and Data Types.html
  • 2. Mastering Data Science Advanced Operator and Expression Techniques.html
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    تاریخ انتشار: ۲۸ فروردین ۱۴۰۳
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