Duration: Hours

Julia is a high-level and general-purpose language that can be used to write code that is fast to execute and easy to implement for scientific calculations. Julia is faster when loading data, which is very important for data scientists.

Training Mode: Online

Description

Objectives :

a). Get to grips with the basic data structures in Julia and learn about different development environments
b). Organize your code by writing Lisp-style macros and using modules
c). Manage, analyze, and work in depth with statistical datasets using the powerful DataFrames package
d). Perform statistical computations on data from different sources and visualize those using plotting packages
e). Apply different algorithms from decision trees and other packages to extract meaningful information from the Iris dataset
f). Gain some valuable insights into interfacing Julia with an R application
g). Uncover the concepts of meta programming in Julia.
h). Conduct statistical analysis with StatsBase.jl and Distributions.jl

a). Julia for Data Science :

  1. Installing a Julia Working Environment
  2. Working with Variables and Basic Types
  3. Controlling the Flow
  4. Using Functions
  5. Using Tuples, Sets, and Dictionaries
  6. Working with Matrices for Data Storage and Calculations
  7. Using Types and parameters methods.
  8. Optimizing Your Code by Using and Writing Macros
  9. Organizing Your Code in Modules
  10. Working with the Package Ecosystem
  11. Reading and writing Data Files and Julia Data
  12. Using DataArrays and DataFrames
  13. The Power of DataFrames
  14. Interacting with Relational Databases Like SQL Server
  15. Interacting with NoSQL Databases Like MongoDB
  16. Exploring and understanding a Dataset Statiscally.
  17. An Overview of the Plotting Techniques in Julia
  18. Visualizing Data with Scatterplots, Histograms, and Box Plots
  19. Distributions and Hypothesis Testing
  20. Interfacing with R
  21. Basic machine Learning Techniques
  22. Classification Using Decision Trees and Rules
  23. Training and Testing a Decision Tree Model
  24. Applying a Generalized Linear Model with GLM
  25. Working with Support Vector Machines

b). Julia solutions :

  1. Handling Data with CSV Files
  2. Handling Data with TSV Files
  3. Interacting with the Web
  4. Representation of Julia programs
  5. Symbols
  6. Quoting
  7. Interpolation
  8. The eval Function
  9. Macros
  10. Metaprogramming with Data Frames
  11. Basic Statistics concepts
  12. Descriptive Statistics
  13. Deviation Metrics
  14. Sampling
  15. Correlation Analysis
  16. Dimensionality Reduction
  17. Data Preprocessing
  18. Linear Regression
  19. Classification
  20. Performance Evaluation and Model Selection
  21. Cross Validation
  22. Distances
  23. Distributions
  24. Time Series Analysis

c). Plotting functions

  1. Exploratory Data Analytics Through Plots
  2. Line Plots
  3. Scatter Plots
  4. Histograms
  5. Aesthetic Customizations
  6. Basic Concepts of Parallel Computing
  7. Data Movement
  8. Parallel Maps and Loop Operations
  9. Channels

Requirements

Although knowing the basic concepts of data science will give you a head-start, it is not a mandatory requirement. With no previous knowledge in data science as well, you will find the pace of the Learning Path quite comfortable and easy to follow.

 

For more inputs on Explore Data Science with Julia you can connect here.
Contact the L&D Specialist at Locus IT.

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