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 :
- Installing a Julia Working Environment
- Working with Variables and Basic Types
- Controlling the Flow
- Using Functions
- Using Tuples, Sets, and Dictionaries
- Working with Matrices for Data Storage and Calculations
- Using Types and parameters methods.
- Optimizing Your Code by Using and Writing Macros
- Organizing Your Code in Modules
- Working with the Package Ecosystem
- Reading and writing Data Files and Julia Data
- Using DataArrays and DataFrames
- The Power of DataFrames
- Interacting with Relational Databases Like SQL Server
- Interacting with NoSQL Databases Like MongoDB
- Exploring and understanding a Dataset Statiscally.
- An Overview of the Plotting Techniques in Julia
- Visualizing Data with Scatterplots, Histograms, and Box Plots
- Distributions and Hypothesis Testing
- Interfacing with R
- Basic machine Learning Techniques
- Classification Using Decision Trees and Rules
- Training and Testing a Decision Tree Model
- Applying a Generalized Linear Model with GLM
- Working with Support Vector Machines
b). Julia solutions :
- Handling Data with CSV Files
- Handling Data with TSV Files
- Interacting with the Web
- Representation of Julia programs
- Symbols
- Quoting
- Interpolation
- The eval Function
- Macros
- Metaprogramming with Data Frames
- Basic Statistics concepts
- Descriptive Statistics
- Deviation Metrics
- Sampling
- Correlation Analysis
- Dimensionality Reduction
- Data Preprocessing
- Linear Regression
- Classification
- Performance Evaluation and Model Selection
- Cross Validation
- Distances
- Distributions
- Time Series Analysis
c). Plotting functions
- Exploratory Data Analytics Through Plots
- Line Plots
- Scatter Plots
- Histograms
- Aesthetic Customizations
- Basic Concepts of Parallel Computing
- Data Movement
- Parallel Maps and Loop Operations
- 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.
Locus Academy has more than a decade experience in delivering the training/staffing on Julia for corporates across the globe. The participants for the training/staffing on Julia are extremely satisfied and are able to implement the learnings in their on going projects.
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