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SV Infotech Offers Data Science Training in Dilsukhnagar Hyderabad.Data Scientist Course training institute in Dilsukhnagar Hyderabad Telangana

Module 1: Introduction to Data Science

1.1 Definition and Scope

  • Understanding the role of data science in solving real-world problems
  • Historical context and evolution of data science

1.2 Key Concepts

  • Data, information, and knowledge
  • The data science lifecycle

1.3 Data Science Roles and Responsibilities

  • Data Scientist, Data Engineer, Machine Learning Engineer, etc.
  • Collaborative aspects and team structures

Module 2: Data Collection and Preprocessing

2.1 Data Collection

  • Sources of data: structured, unstructured, and semi-structured data
  • Web scraping, APIs, surveys, and more

2.2 Data Cleaning and Preprocessing

  • Handling missing data
  • Dealing with outliers
  • Standardization and normalization

2.3 Exploratory Data Analysis (EDA)

  • Descriptive statistics
  • Data visualization using tools like Matplotlib and Seaborn

Module 3: Data Storage and Retrieval

3.1 Databases

  • SQL and NoSQL databases
  • Relational databases (e.g., MySQL, PostgreSQL) vs. non-relational databases (e.g., MongoDB)

3.2 Big Data Technologies

  • Hadoop and MapReduce
  • Spark for distributed computing

3.3 Data Warehousing

  • Concepts of data warehousing
  • Data lakes vs. data warehouses

Module 4: Feature Engineering

4.1 Importance of Feature Engineering

  • Feature selection vs. feature extraction
  • Techniques for handling categorical data

4.2 Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

Module 5: Machine Learning Basics

5.1 Introduction to Machine Learning

  • Supervised vs. unsupervised learning
  • Classification, regression, clustering

5.2 Model Training and Evaluation

  • Splitting data into training and testing sets
  • Cross-validation and model evaluation metrics

5.3 Model Selection

  • Decision trees, random forests, support vector machines, k-nearest neighbors, etc.

Module 6: Advanced Machine Learning

6.1 Deep Learning

  • Neural networks and deep neural networks
  • Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

6.2 Model Optimization

  • Hyperparameter tuning
  • Regularization techniques

6.3 Model Deployment

  • Deploying models in production
  • Model monitoring and maintenance

Module 7: Data Ethics and Privacy

7.1 Ethical Considerations

  • Bias in data and models
  • Fairness and accountability

7.2 Privacy Concerns

  • GDPR and other regulations
  • Anonymization and encryption techniques

Module 8: Real-world Projects and Case Studies

8.1 Capstone Project

  • Applying data science concepts to solve a real-world problem
  • Presenting findings and insights

8.2 Industry Applications

  • Case studies from various industries (healthcare, finance, marketing, etc.)
  • Guest lectures from industry professionals

Module 9: Emerging Trends in Data Science

9.1 Artificial Intelligence and Machine Learning

  • Reinforcement learning
  • Generative Adversarial Networks (GANs)

9.2 Big Data and Cloud Computing

  • Cloud platforms (AWS, Azure, Google Cloud)
  • Serverless computing

Module 10: Continuous Learning and Resources

10.1 Stay Updated

  • Keeping up with the latest developments
  • Online courses, blogs, and conferences

10.2 Networking and Community

  • Joining data science communities
  • Collaborating with peers and professionals

This course outline provides a structured approach to cover fundamental and advanced concepts in data science. Adjustments can be made based on the audience’s background and the intended duration of the course.