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Data Analysis – Advanced Class

Original price was: $200.00.Current price is: $180.00. VAT

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Class Outline:

Review of Intermediate Concepts:

  1. Recap of Key Intermediate Topics:
    • Ensure a solid understanding of advanced topics building upon intermediate concepts.

Advanced Statistical Methods:

  1. Bayesian Statistics:
    • Introduction to Bayesian inference.
    • Bayesian modeling and applications.
  2. Non-parametric Statistics:
    • Wilcoxon signed-rank test, Mann-Whitney U test.
    • Kernel density estimation.

Advanced Machine Learning:

  1. Ensemble Learning:
    • Random Forests, Gradient Boosting.
    • Stacking and blending models.
  2. Deep Learning Fundamentals:
    • Neural networks architecture.
    • Training deep learning models.

Advanced Data Visualization:

  1. Interactive Dashboards:
    • Introduction to tools like Tableau or Plotly Dash.
    • Creating dynamic and interactive visualizations.
  2. Geospatial Data Visualization:
    • Mapping and spatial analysis.
    • Geographic Information Systems (GIS) applications.

Text and Sentiment Analysis:

  1. Natural Language Processing (NLP):
    • Tokenization, stemming, and lemmatization.
    • Sentiment analysis using NLP.
  2. Text Mining and Topic Modeling:
    • Extracting insights from large text datasets.
    • Latent Dirichlet Allocation (LDA) for topic modeling.

Advanced Data Analysis Tools:

  1. R for Data Analysis:
    • Advanced data manipulation and analysis in R.
    • Integration with R libraries for statistical modeling.
  2. Advanced SQL:
    • Window functions and advanced SQL queries.
    • Recursive queries and common table expressions.

Big Data and Advanced Analytics Platforms:

  1. Apache Spark and PySpark:
    • Introduction to distributed computing.
    • Big data processing using Spark.
  2. Advanced Analytics Platforms (e.g., Databricks):
    • Cloud-based analytics platforms.
    • Collaborative and scalable data analysis.

Ethical and Responsible Data Analysis:

  1. Ethics in Data Analysis:
    • Addressing bias in data.
    • Privacy considerations.
  2. Interpreting and Communicating Complex Results:
    • Effectively communicating findings to non-technical stakeholders.
    • Dealing with uncertainty and ambiguity in data.

Capstone Project:

  1. Advanced Data Analysis Project:
    • Independent or group projects applying advanced techniques.
    • Presentation and discussion of project findings.

Emerging Trends and Future Directions:

  1. Current Trends in Data Analysis:
    • AI-driven analytics.
    • Edge computing and real-time analytics.
  2. Continuous Learning and Professional Development:
    • Resources for staying updated in the field.
    • Networking and collaboration opportunities

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