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Data Science

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Data Science
Looking for the best data science course in Delhi? Join our institute for expert training. Learn through live projects, gain skills for a successful Career.
  1. Introduction to Data Science:

    • Overview of data science and its applications
    • Understanding the data science workflow
    • Introduction to data exploration and visualization
  2. Python for Data Science:

    • Python fundamentals for data manipulation
    • Working with data structures (e.g., NumPy, Pandas)
    • Data cleaning and preprocessing techniques
  3. Data Analysis and Visualization:

    • Exploratory data analysis (EDA)
    • Statistical analysis and hypothesis testing
    • Data visualization using libraries (e.g., Matplotlib, Seaborn)
  4. Machine Learning:

    • Introduction to machine learning concepts
    • Supervised learning algorithms (e.g., linear regression, decision trees)
    • Unsupervised learning algorithms (e.g., clustering, dimensionality reduction)
  5. Model Evaluation and Validation:

    • Model evaluation metrics and techniques
    • Cross-validation and overfitting prevention
    • Hyperparameter tuning and model selection
  6. Deep Learning and Neural Networks:

    • Introduction to neural networks and deep learning
    • Building and training neural networks (e.g., TensorFlow, Keras)
    • Convolutional neural networks (CNN) and recurrent neural networks (RNN)
  7. Natural Language Processing (NLP):

    • Introduction to NLP and its applications
    • Text preprocessing and feature extraction
    • Building NLP models (e.g., sentiment analysis, text classification)
  8. Big Data and Distributed Computing:

    • Introduction to big data concepts and technologies (e.g., Hadoop, Spark)
    • Processing and analyzing large datasets
    • Distributed computing frameworks (e.g., Apache Spark)
  9. Data Science in Practice:

    • Real-world case studies and projects
    • Data storytelling and communication
    • Ethical considerations in data science
  10. Data Science Tools and Libraries:

    • Utilizing data science libraries (e.g., Scikit-learn, TensorFlow)
    • Data manipulation and analysis tools (e.g., SQL, Tableau)
    • Version control and collaboration (e.g., Git, Jupyter Notebooks)

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