Statistical Data Analysis with Julia

Statistical Data Analysis with Julia

Public courses


- Anyone can join the training
- Course outline as presented on the website
- Small groups, 3-10 people

Private courses

Price set individually

- Training workshop just for your team
- You choose date and location of the training
- Course outline tailored to your needs

About the training

The ability to use methods based on principal component analysis enables to perceive data in a new and unique way. By conducting this process by yourself you will be able to enter a multidimensional world. This method enabled physicists to discover and understand the world of quantum mechanics in 20th century; it’s also a basis for the developing big data branch.

The reduction of data and discovering hidden relationships between a big number of variables are the key problems of big data. Julia Environment gives an access to highly efficient libraries which make the processing of huge matrix possible. The training is aimed at expanding knowledge by conducting and presenting the whole process as well as understanding and interpreting the results.

Who is this training for?

  • Analysts, programmers and managers responsible for the analysis and exploitation of a huge set of data in bussiness.

What will I learn?

  • Learn methods of statistical analysis
  • Understand and learn exploiting patterns in data, reducing dimensionality, segmentation using PCA and FA
  • Learn how to use all resources of your CPU with parallel computations
  • Write and debug Julia programs for statistical data analysis
  • Interpret output from statistical procedures and find insight in your data

Course outline

  1. Introduction to Julia environment
    • Installation
    • Packages for Julia
    • Data preparation and Importing
    • Data transformation and wrangling
    • Programming basics – loops, conditionals, functions
    • Sparse arrays
  2. Introduction to Statistical Data Analysis
    • Analysis of relationship between variables
    • Measures of dependence
    • Variance and its importance
    • Covariance vs correlation – importance and interpretation.
    • Data Normalization, standarization and influence on the output
  3. Principal component analysis
    • Calculating correlation and covariance matrix for large data sets
    • Eigenvalues and Eigenvectors calculations
    • Deciding on the number of principal components
    • Calculating Principal Components and building new model
    • Interpreting Principal components
    • Analysis of Principal components and extracting useful information from data
  4. Rotations – different view on Principal Components
  5. Factor Analysis
    • Concept of hidden variable of interest
    • Calculation of Factors
    • Interpretation of factor analysis
    • Extracting useful information for business

Course Curriculum

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