Advanced Data Analysis Techniques

Course Info

Length: 1 Week

City: Dubai

Type: In Classroom

Available Dates

  • Dec-23-2024

    Dubai

  • Jan-20-2025

    Dubai

  • Apr-28-2025

    Dubai

  • June-23-2025

    Dubai

  • July-21-2025

    Dubai

  • Oct-06-2025

    Dubai

  • Dec-01-2025

    Dubai

Dates in Other Venues

  • Dec-23-2024

    Paris

  • Dec-30-2024

    Singapore

  • Dec-30-2024

    Barcelona

  • Jan-20-2025

    London

  • Jan-27-2025

    Kuala Lumpur

  • Jan-27-2025

    Istanbul

  • Jan-27-2025

    Paris

  • Jan-27-2025

    Barcelona

  • Jan-27-2025

    Amsterdam

  • Jan-27-2025

    Singapore

  • Apr-28-2025

    Kuala Lumpur

  • Apr-28-2025

    Barcelona

  • Apr-28-2025

    London

  • Apr-28-2025

    Singapore

  • Apr-28-2025

    Amsterdam

  • Apr-28-2025

    Paris

  • Apr-28-2025

    Istanbul

  • June-23-2025

    London

  • July-21-2025

    London

  • July-28-2025

    Paris

  • July-28-2025

    Amsterdam

  • July-28-2025

    Barcelona

  • July-28-2025

    Istanbul

  • July-28-2025

    Singapore

  • July-28-2025

    Kuala Lumpur

  • Oct-06-2025

    London

  • Oct-27-2025

    Istanbul

  • Oct-27-2025

    Barcelona

  • Oct-27-2025

    Singapore

  • Oct-27-2025

    Amsterdam

  • Oct-27-2025

    Paris

  • Oct-27-2025

    Kuala Lumpur

  • Dec-01-2025

    London

Course Details

Course Outline

5 days course

Exploratory Data Analysis

  • Introduction to exploratory data analysis (EDA)
  • Techniques for data visualization and exploration
  • Measures of central tendency, dispersion, and correlation
  • Outlier detection and treatment
  • Hand-on examples

Machine Learning for Data Analysis

  • Introduction to machine learning (ML) algorithms
  • Supervised learning: regression, classification, decision trees, random forests
  • Unsupervised learning: clustering, dimensionality reduction
  • Cross-validation and hyperparameter tuning
  • Hand-on examples

Time Series Analysis

 

  • Introduction to time series data
  • Time series decomposition and trend analysis
  • Seasonality and periodicity analysis
  • Autoregressive Integrated Moving Average (ARIMA) models
  • Hand-on examples

Bayesian Data Analysis

  • Introduction to Bayesian statistics
  • Bayes' theorem and probability distributions
  • Bayesian modeling and inference
  • Markov Chain Monte Carlo (MCMC) methods
  • Hand-on examples

 

Big Data Analytics

 

  • Introduction to big data and distributed computing
  • MapReduce and Hadoop ecosystem
  • Apache Spark and Spark SQL
  • Machine learning on big data: Spark MLlib
  • Hand-on examples

Course Video