Advanced Data Analytics

What Will You Learn?

• Advanced Data Analytics
• Data Analytics Fundamentals:
• Basics of Data Analytics: Understanding data types, data structures, and basic analytics techniques.
• Data Manipulation and Cleaning: Techniques for preparing data for analysis using tools like Python and R.
• Advanced Data Types: Working with time-series, spatial data, and complex data structures.
• Analytics Tools and Platforms:
• Overview of Analytics Evolution: Key changes and advancements in data analytics tools and platforms.
• Modern Tools: Mastering tools like Microsoft Power BI, Tableau, and Google Data Studio for data visualization and business intelligence.
• Tool Integration: Best practices for integrating multiple analytics tools for comprehensive analysis.
• Data Modeling:
• Fundamentals of Data Modeling: Creating and understanding data models for analytical purposes.
• Advanced Data Models: Implementing predictive models, regression analysis, and clustering techniques.
• Real-time Data Modeling: Techniques for handling and modeling real-time data.
• Real-time Data Analytics:
• Integrating Real-time Analytics: Utilizing streaming data and real-time data pipelines with tools like Apache Kafka and Spark.
• Real-time Dashboards: Building interactive dashboards that update in real-time with Power BI and Tableau.
• Real-time Data Visualization: Techniques for visualizing streaming data effectively.
• Data Visualization with Power BI:
• Introduction to Power BI: Setting up and using Microsoft Power BI in analytics projects.
• Customizing Visualizations: Creating and styling custom visualizations in Power BI.
• Advanced Dashboards: Building complex dashboards with drill-down features and real-time data integration.
• Data Analysis Lifecycle:
• Understanding the Data Analysis Process: Data collection, cleaning, exploration, modeling, and interpretation.
• Lifecycle Management: Best practices for managing the entire data analysis lifecycle.
• Data Binding and Transformation:
• Data Transformation Techniques: Advanced techniques for transforming and reshaping data.
• Custom Transformation Pipelines: Creating custom pipelines for specific analytics needs.
• Two-way Data Binding: Implementing data binding techniques in analytics tools.
• Machine Learning and Predictive Analytics:
• Introduction to Machine Learning: Basics of machine learning algorithms and their applications in analytics.
• Advanced Machine Learning Techniques: Using tools like TensorFlow and Scikit-learn for predictive modeling.
• Real-world Applications: Implementing machine learning in complex data analytics projects.
• Data Sharing and Collaboration:
• Collaborative Data Analysis: Using tools like Jupyter Notebooks, Google Colab, and Git for collaborative analytics.
• Service-based Data Sharing: Implementing RESTful APIs and other services for data sharing.
• Data Security and Privacy: Best practices for ensuring data security and compliance in shared environments.
• Formulation of Analytics Strategy:
• Developing an Analytics Strategy: Steps for creating a robust data analytics strategy aligned with business goals.
• Performance Metrics: Defining and measuring key performance indicators (KPIs) in analytics projects.
• Strategic Decision Making: Leveraging analytics to inform business decisions and strategies.
• Power BI and Advanced Visualization Techniques:
• Creating Advanced Reports: Designing complex reports with Power BI’s advanced features.
• Data Storytelling: Techniques for effective data storytelling using visualizations.
• Optimizing Dashboards: Strategies for improving dashboard performance and responsiveness.
• Shared Service Creation and Management:
• Creating Reusable Analytics Services: Best practices for building and managing shared analytics services.
• Scalability in Analytics: Structuring services and pipelines for scalability and maintainability.
• Advanced Dependency Management: Techniques for managing dependencies in complex analytics environments.
• Note: Learning content may be further refined by experts during training.