Description:

This comprehensive course covers Python programming, Data Science fundamentals, Data Analysis, SQL databases, Data Visualization, Machine Learning, Natural Language Processing (NLP), Predictive Analytics, PySpark for big data processing, Artificial Intelligence (AI), GIT version control, MLOps for ML model deployment, Azure Data Factory for data integration, Jupyter Notebooks, SciPy for scientific computing, NumPy for numerical computing, pandas for data manipulation, matplotlib for data plotting, TensorFlow for deep learning, Power BI for business intelligence, Excel for spreadsheet analysis, Spark SQL for big data querying.

Key Highlights:

• Comprehensive coverage of Python, Data Science, SQL, Visualization, ML, NLP
• Hands-on experience with PySpark, AI, GIT, MLOPS, Azure Data Factory
• Practical usage of Jupyter, SciPy, NumPy, pandas, matplotlib, TensorFlow
• Understanding of Power BI, Excel, Spark SQL for data analytics

What you will learn:

• Python Programming
  Basic to advanced Python concepts and application in Data Science
• Data Analysis and Visualization
  Exploratory Data Analysis, Visualization techniques
• Machine Learning and Predictive Analytics
  Building ML models, making predictions, evaluating model performance
• Natural Language Processing (NLP)
  Text processing, sentiment analysis, language modeling
• PySpark and Big Data Processing
  Handling large datasets with PySpark, data preprocessing, analysis
• Artificial Intelligence (AI) and Deep Learning with TensorFlow
  Deep learning principles, neural networks, TensorFlow usage for AI applications
• GIT Version Control and MLOps
  GIT basics, MLOps workflow, model deployment best practices
• Azure Data Factory and Data Integration
  Data pipelines, data transformation, Azure cloud services
• Jupyter Notebooks
  Interactive coding environment, data exploration, documentation
• SciPy, NumPy, pandas, matplotlib
  Data manipulation, numerical computing, scientific computing, data visualization
• Spark SQL and Big Data Querying
  Querying large datasets with Spark SQL, data analysis techniques