Julia for Data Science

Dive into Julia’s deep learning framework and build a system using Mocha.jl. An in-depth exploration of Julia's growing ecosystem of packages. Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data sets Apply statistical models in Julia for data-driven decisions.

  • Comprehensive training through 26 video sessions.
  • Understand the process of data munging and data preparation using Julia
  • Explore techniques to visualize data using Julia and D3 based packages
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    Self-Paced

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    Course Features

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    About Julia for Data Science

    Julia is an easy, fast, open source language that if written well performs nearly as well as low-level languages such as C and FORTRAN. Its design is a dance between specialization and abstraction, providing high machine performance without the sacrifice of human convenience. Julia is a fresh approach to technical computing, combining expertise from diverse fields of computational and computer science.

    This video course walks you through all the steps involved in applying the Julia ecosystem to your own data science projects. We start with the basics and show you how to design and implement some of the general purpose features of Julia. Is fast development and fast execution possible at the same time? Julia provides the best of both worlds with its wide range of types, and our course covers this in depth. You will have organized and readable code by the end of the course by learning how to write Lisp style macros and modules.

    The course demonstrates the power of the DataFrames package to manage, organize, and analyze data. It enables you to work with data from various sources, perform statistical calculations on them, and visualize their relationships in different kinds of plots through live demonstrations.

    Course Objectives
    • Write R code that can be executed outside RStudio
    • Get data from numerous sources such as files, databases, and even Twitter
    • Clean data before the analysis phase begins
    • Load libraries into RStudio for use within the analysis phase
    • Perform data cleaning on a dataset
    • Create a codebook so that the data can be presented in a summary
    Curriculum
    Module 1:

    Getting Comfortable With The Basic Structures In Julia

    • The Course Overview
    • Installing a Julia Working Environment
    • Working with Variables and Basic Types
    • Controlling the Flow
    Module 2:

    Diving Deeper Into Julia

    • Using Types and Parameterized Methods
    • Optimizing Your Code by Using and Writing Macros
    • Organizing Your Code in Modules
    • Working with the Package Ecosystem
    Module 3:

    Working With Data In Julia

    • Reading and Writing Data Files and Julia Data
    • Using DataArrays and DataFrames
    • The Power of DataFrames
    Module 4:

    Statistics With Julia

    • Exploring and Understanding a Dataset Statistically
    • An Overview of the Plotting Techniques in Julia
    • Visualizing Data with Scatterplots, Histograms, and Box Plots
    Module 5:

    Machine Learning Techniques With Julia

    • Basic Machine Learning Techniques
    • Classification Using Decision Trees and Rules
    • Training and Testing a Decision Tree Model
    Instructor

    Ivo Balbaert is currently a web programming and databases lecturer at CVO Antwerpen (www.cvoantwerpen.be), a community college in Belgium. He received a PhD in applied physics in 1986 from the University of Antwerp. He worked for 20 years in the software industry as a developer and consultant in several companies, and, for 10 years, as a project manager at the University Hospital of Antwerp. In 2000, he switched over to partly teach and partly develop software (KHM Mechelen, CVO Antwerp).

    He also wrote Programmeren met Ruby en Rails, an introductory book in Dutch about developing in Ruby and Rails, by Van Duuren Media.

    In 2012, he authored The Way To Go, a book on the Go programming language by IUniverse.

    In 2014, he wrote Learning Dart (in collaboration with Dzenan Ridzanovic) and Dart Cookbook, both by Packt Publishing.

    Finally, in 2015, he wrote Getting started with Julia and Rust Essentials, both by Packt Publishing.

    Certification

    A test will be conducted at the end of the course. On completion of the test with a minimum of 70% marks, training.com will issue a certificate of successful completion from NIIT.

    Five re-attempts will be provided in case the candidate scores less than 70%.

    A Participation certificate will be issued if the candidate does not score 70% after five attempts.

    Pre-requisites

    Basic knowledge of data analysitcs and basic programming background with Math as one of the subjects.

    FAQs

    Who should go for this Course?

    Anyone aspring a great career in Data Science involving complex data analytics and data mining techniques would find this course a must to pursue. Professionals working on Data Mining Projects can also pursue this course to help them gain an extra edge over complex data mining algorithms development using Julia.

    Where can I find my session schedule?

    The session schedule will be available in the training.com Student portal - Learning Plan section. You can login to your training.com account to view the same.

    What is your refund policy?

    Upon registering for the course, if for some reason you are unable or unwilling to participate in the course further, you can apply for a refund. You can initiate the refund any time before start of the second session of the course by sending an email to support@training.com , with your enrolment details and bank account details (where you want the amount to be transferred). Once you initiate a refund request, you will receive the amount within 21 days after confirmation and verification by our team. This is provided if you have not downloaded any courseware after registration.

    Why is it called Self Paced course?

    Self Paced courses are comprised of several learning videos into a course structure broken down into Learning Modules and Sessions. The learner is required to go through the videos topic-wise in the structure sequence of the course to learn the concepts. Being Self Paced, there is no intervention of any external faculty or additional mentor in learning.

    Being a self paced course, how will my attendance be tracked and marked?

    you login into your training.com account to watch the videos, attendance for it will be marked automatically.

    How will the assessment be conducted for my certification?

    After each module, a multiple choice questions type online assessment will be conducted. 5 Attempts will be allowed for the assessment to be completed. The minimum pass percentage for each assessment is 70%. On successfully clearing the assessment, a verified certificate from NIIT shall be awarded otherwise the certificate of participation will be issued.

    What are the minimum system requirements to attend the course?

      Minimum system requirements for accessing the courses are:

    • Personal computer or Laptop with web camera
    • Headphone with Mic
    • Minimum 4 Mbps broadband connection

    Is there an official support desk for technical guidance during the training program?

    Yes.For immediate technical support during the live online classroom sessions, you can call 91-9717992809 or 0124-4917203 between 9:00 AM and 8:00 PM IST. You can write to support@training.com for all other queries and our team will be happy to help you.

    Course Features

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