Data Science With Julia (Part I)

Best programming language for data analysis, data science and machine learning

What you'll learn

Having a strong grasp of data frames in Julia

Importing data with Julia

Analyzing and manipulating data with Julia

Data visualization with Julia

Requirements

I did my best to make this course self-contained, but still I strongly recommend studying the basics of Julia before enrolling. You can take my 'Programming with Julia' course or explore any other online training or book that suits your preferences.

Description

Do you want to learn data analysis, data science, machine learning, deep learning, and AI, but you are not sure about the programming language to choose? Or perhaps you are using Python and R, but you are tired of their slow performance.You can accomplish everything, and even more, with Julia compared to what you can do with Python or R, all with the same level of ease. Moreover, Julia offers significantly greater speed than both of them.Julia is a modern programming language developed for data science, machine learning, AI, and numerical computing. It is a dynamically typed language that is easy to learn and use and moreover has the speed of C.Julia combines the best features of dynamic languages like Python and R with low-level languages like C, C#, and Java. You can develop a machine learning model or an algorithm in Julia and use that code in a production environment. You don't have to use different languages for development and production.This is my second course about Julia. In this course, you will learn how to accomplish essential data science tasks with Julia: importing, analyzing, manipulating, and visualizing data. Having these foundations you will be ready for machine learning and deep learning with Julia which will be in my upcoming lectures. Please stay tuned.

Overview

Section 1: Introduction

Lecture 1 Why use Julia for Data Science?

Lecture 2 Two-Language Problem

Lecture 3 Julia is Fast: Why Does it Matter?

Lecture 4 Is Julia Really Fast?

Lecture 5 Julia Data Ecosystem

Lecture 6 Codes and Resources

Section 2: Working with Data Frames

Lecture 7 Creating Data Frames

Lecture 8 Indexing and Slicing Data Frames

Lecture 9 Conditional Filtering

Lecture 10 Selecting and Transforming Columns I

Lecture 11 Selecting and Transforming Columns II

Lecture 12 Summarizing Data with Split Apply Combine Strategy

Lecture 13 Joining Data Frames

Lecture 14 DataFrames: Additional Resources

Section 3: Importing Data

Lecture 15 Introduction

Lecture 16 Flat Files

Lecture 17 Delimited Files

Lecture 18 Spreadsheets

Lecture 19 HDF5 Files

Lecture 20 JSON Files

Lecture 21 XML Files

Lecture 22 Relational Databases

Lecture 23 Statistical Programs

Lecture 24 Web Scraping

Section 4: Data Analysis & Manipulation

Lecture 25 Introduction

Lecture 26 Project Description

Lecture 27 Import Project Data

Lecture 28 Remove Duplicates

Lecture 29 Merge Input & Output Data

Lecture 30 Summarize Data

Lecture 31 Nonnumerical Data

Lecture 32 Missing Data

Lecture 33 Outliers

Lecture 34 Standardization & Scaling

Lecture 35 Correlation Analysis

Lecture 36 Creating Categorical Variables from Numbers (Optional)

Section 5: Data Visualization

Lecture 37 Introduction

Lecture 38 Preparing Data

Lecture 39 Line Plot

Lecture 40 Scatter Plot

Lecture 41 Bar Plot

Lecture 42 Histogram

Lecture 43 Box, Dot, Violin Plots

Lecture 44 Three Dimensional Plots

Lecture 45 Interactive Statsplot

Lecture 46 Makie Package

Lecture 47 Dashboards with Makie

Lecture 48 Observables

Lecture 49 Interactive Dashboards with Makie

You may be an adept data scientist well-versed in Python or R, or you might be embarking on your learning journey, grappling with the choice of a programming language. I will try to convince you that, you can accomplish everything, and even more, with Julia compared to what you can do with Python or R, all with the same level of ease. Moreover, Julia offers significantly greater speed than both of them.