Course Info

Length:

5 Days

Type:

In Classroom

Available Dates

2025-03-31

Barcelona

2025-03-31

Paris

2025-03-31

Amsterdam

2025-03-31

Singapore

2025-04-21

London

2025-05-19

Dubai

2025-06-30

Kuala Lumpur

2025-06-30

Barcelona

2025-06-30

Paris

2025-06-30

Istanbul

2025-06-30

Amsterdam

2025-06-30

Singapore

2025-06-30

London

2025-07-21

Dubai

2025-08-18

London

2025-09-29

Singapore

2025-09-29

Amsterdam

2025-09-29

Kuala Lumpur

2025-09-29

Istanbul

2025-09-29

Dubai

2025-09-29

Paris

2025-09-29

Barcelona

2025-10-20

London

2025-11-17

Dubai

2025-12-29

Amsterdam

2025-12-29

Barcelona

2025-12-29

Paris

2025-12-29

Istanbul

2025-12-29

Kuala Lumpur

2025-12-29

Singapore

2025-12-29

London

Course Details

  • Introduction
  • Objective
  • Who should attend

In today's world, data is being generated at an unprecedented rate, and extracting valuable insights from this data has become a critical skill for businesses and organizations. This course is designed to help you develop the necessary skills and knowledge to tackle advanced data analysis challenges, ranging from exploratory data analysis to machine learning, time series analysis, Bayesian data analysis, and big data analytics.

Over the course of five days, you will learn about various techniques and tools used in the data analysis process, including data visualization, data modelling, and machine learning algorithms. You will also learn how to work with time series data, understand Bayesian statistics, and tackle big data analytics challenges using distributed computing tools such as Hadoop and Spark.

Throughout the course, you will have the opportunity to work on hands-on exercises using popular programming languages such as Python and libraries such as Scikit-learn, Statsmodels, and Spark MLlib. By the end of this course, you will have a strong foundation in advanced data analysis techniques that will enable you to solve complex data analysis challenges with confidence.

 

Course Outline

5 days course
  • Day 1
  • Day 2
  • Day 3
  • Day 4
  • Day 5

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
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