Data Science and Artificial Intelligence Guided Lab

Applied Data Science Specialization

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Certificate of completion
  • Introduction to Data Science
  • Statistics and Mathematics for Data Science
  • Programming and Data Manipulation
  • Data Visualization
  • Machine Learning
  • Big Data Technologies
  • Deep Learning
  • Natural Language Processing (NLP)
  • Feature Engineering and Model Deployment
  • Real-world Projects and Case Studies
  • Ethics and Best Practices
  • Practical Tools and Technologies

Course Curriculum & Resources

  • Overview of data science and its applications
  • Understanding the data science workflow

  • Descriptive and inferential statistics
  • Probability and distributions
  • Linear algebra and calculus for machine learning

  • Introduction to programming languages like Python
  • Data cleaning, wrangling, and manipulation techniques

  • Creating effective visualizations using tools like Matplotlib, Seaborn, or ggplot2
  • Communicating insights through charts and graphs

  • Supervised learning (regression, classification)
  • Unsupervised learning (clustering, dimensionality reduction)
  • Model evaluation and hyperparameter tuning

  • Introduction to big data platforms (e.g., Hadoop, Spark)
  • Handling large datasets and distributed computing

  • Neural networks architecture
  • Deep learning frameworks (e.g., TensorFlow, PyTorch)
  • Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

  • Text processing and analysis
  • Building NLP models for sentiment analysis, text classification, etc.

  • Extracting meaningful features from data
  • Deploying machine learning models in production

  • Applying data science skills to solve real-world problems
  • Working on projects to build a portfolio

  • Ethical considerations in data science
  • Best practices for data handling, model evaluation, and communication of results

  • Using data science libraries and tools (e.g., Pandas, Scikit-learn, Jupyter Notebooks)
  • Version control systems (e.g., Git) and collaboration tools

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    Applied Data Science Specialization

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