Data Analytics with Tidier.jl and Julia

Programme Syllabus

Julia Workshop

Course Title: Data Analytics with Tidier.jl and Julia

Course Description:

This course introduces students to the principles and techniques of data analytics using Julia, with a special focus on the Tidier.jl package for data manipulation. Students will learn to manage, transform, and visualize data, as well as handle text and date operations effectively.

Prerequisites:

  • Basic understanding of programming concepts

  • Introductory course in statistics or equivalent knowledge

Course Objectives:

By the end of this course, students will be able to: 1. Use Tidier.jl for efficient data manipulation in Julia. 2. Perform complex data transformations and cleaning. 3. Work with text and dates in data analysis. 4. Create meaningful data visualizations using Julia’s plotting libraries. 5. Apply data analytics techniques to solve real-world problems.

Course Outline:

Week 1: Introduction to Data Analytics

  • Overview of data analytics and its importance

  • Introduction to Julia programming language

  • Setting up the Julia environment

Week 2: Introduction to DataFrames.jl and Tidier.jl

  • Overview of DataFrames.jl for data handling

  • Introduction to Tidier.jl for data manipulation

  • Basic data operations: loading, inspecting, and summarizing data

Week 3: Data Import and Export

  • Reading and writing data in Julia

  • Handling various data formats (CSV, JSON, etc.)

Week 4: Data Transformation with Tidier.jl

  • Filtering, selecting, and arranging data

  • Grouped operations and summarization

  • Mutating data frames with new columns

Week 5: Advanced Data Manipulation

  • Reshaping data: pivoting and unpivoting

  • Handling missing values and data imputation

  • Joins and relational data operations

Week 6: Working with Text Data

  • String operations in Julia

  • Regular expressions and pattern matching

  • Text analysis and manipulation

Week 7: Working with Dates and Times

  • Date and time handling in Julia

  • Parsing and formatting dates

  • Date-time arithmetic and operations

Week 8: Introduction to Data Visualization

  • Overview of visualization libraries in Julia

  • Basics of plotting with Plots.jl and Gadfly.jl

Week 9: Advanced Visualization Techniques

  • Customizing plots

  • Creating interactive visualizations

  • Best practices in data visualization

Week 10: Statistical Analysis and Exploratory Data Analysis (EDA)

  • Descriptive statistics

  • Hypothesis testing

  • Exploratory data analysis techniques

Week 11: Machine Learning Basics

  • Introduction to machine learning concepts

  • Implementing basic machine learning models in Julia

Week 12: Applied Data Projects

  • Real-world data analysis project using Tidier.jl

  • Real-world data visualization project in Julia

Week 13: Performance Optimization and Best Practices

  • Code optimization techniques in Julia

  • Best practices for data analysis and project management

Week 14: Course Wrap-up and Final Projects

  • Review of key concepts

  • Final project presentations

Assessment:

  • Weekly assignments (30%)

  • Midterm project (20%)

  • Final project (30%)

  • Quizzes and class participation (20%)

Instructor:

  • Name: [Instructor’s Name]

  • Office Hours: [Office Hours]

  • Contact: [Email]