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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

Big Data Analytics is an online programme available on edX that falls under the subject of Computer Science. The programme is designed and administered by AdelaideX, which is the massive open online course (MOOC) platform of the University of Adelaide providing free online courses to students worldwide.

The Big Data Analytics Certification is designed to train the participants on major tools and techniques to come up with important business information and make business decisions by storing, processing, and analysing large-scale data such as R and Apache Spark. By joining the Big Data Analytics Certification by AdelaideX offered by edX, the participants will develop the potential to creatively approach the major issues and challenges of data science.

The Big Data Analytics Certification Syllabus will help the students build a detailed understanding of big data analytics, cloud-based big data analysis, predictive analytics, etc, and sharpen their programming and mathematical skills. There are two modes to take the self-paced programme; verified and audit-free modes. In the free mode, the learners will get only limited duration and in the verified track, they can get unlimited access and a certificate of completion.

The Highlights

Available on edX
Offered by AdelaideX
10 Week-long Course
Complete Online Course
Audit and Verified Tracks Available
Shareable Certificate Upon Completion
Self-paced Programme

Programme Offerings

Graded Assignments and Exams
edX support
English medium
intermediate level course
Video transcript in English

Courses and Certificate Fees

Fees Informations	Certificate Availability	Certificate Providing Authority
INR 16571	yes	The University of Adelaide, Adelaide

Big Data Analytics Certification Fees

Course Title	Total Fee in INR
Big Data Analytics (Verified Tracks)	INR 16,571

There are two options of enrolment for the students who look forward to joining the Big Data Analytics Online Course. The first option is Audit Track and the course will be fully free of cost and the participants will be given only limited access to the study materials. The second option of enrolment is Verified Track which levies the above-mentioned Big Data Analytics Certification Fees and the participants will be given unlimited access to the learning materials.

Eligibility Criteria

Academic Qualifications

The candidates who want to explore big data analytics can join the Big Data Analytics Certification Course and the course does not stipulate any kind of prerequisites.

What you will learn

Mathematical skillProgramming skillsKnowledge of Big Data

After the completion of Big Data Analytics Training, the learners will have a solid understanding of the following:

Big data applications
Application of large-scale data analysis
Analysis of problem space and data needs
Probabilistic and statistical models

Who it is for

Data AnalystData ScientistData Architect

The Big Data Analytics Classes is open for enrolment for any learner who is passionate about studying big data analytics except the learners from the countries of Iran, Cuba, and the Crimea region of Ukraine. But, this course is highly recommended for professionals like Data Scientists, Data Architects, Data Analysts, Developers, etc.

Admission Details

Step 1 - First, the candidates will have to register in edX on https://www.edx.org/ to avail the AdelaideX course on edX.

Step 2 - After activating the edX account, click on SCHOOLS & PARTNERS ‘Category’ given on the home page of edX to find courses from AdelaideX and click on the AdelaideX logo.

Step 3 - Scroll down and Search for Big Data Analytics on the page on AdelaideX-provided courses.

Step 4 - Then Click on the course.

Step 5 - Choose the tab ‘Enroll’

The Syllabus

Fit a simple linear regression between two variables in R; Interpret output from R; Use models to predict a response variable; Validate the assumptions of the model.

Adapt the simple linear regression model in R to deal with multiple variables; Incorporate continuous and categorical variables in their models; Select the best-fitting model by inspecting the R output.

Manipulate nested data frames in R; Use R to apply simultaneous linear models to large data frames by stratifying the data; Interpret the output of learner models.

Adapt linear models to take into account when the response is a categorical variable; Implement Logistic regression (LR) in R; Implement Generalised linear models (GLMs) in R; Implement Linear discriminant analysis (LDA) in R.

Implement the principles of building a model to do prediction using classification; Split data into training and test sets, perform cross-validation and model evaluation metrics; Use model selection for explaining data with models; Analyse the overfitting and bias-variance trade-off in prediction problems.

Set up and apply sparklyr; Use logical verbs in R by applying native sparklyr versions of the verbs.

Apply sparklyr to machine learning regression and classification models; Use machine learning models for prediction; Illustrate how distributed computing techniques can be used for “bigger” problems.

Use massive amounts of data to train multi-layer networks for classification; Understand some of the guiding principles behind training deep networks, including the use of autoencoders, dropout, regularization, and early termination; Use sparklyr and H2O to train deep networks.

Understand some of the ways in which massive amounts of unlabelled data, and partially labeled data, are used to train neural network models; Leverage existing trained networks for targeting new applications; Implement architectures for object classification and object detection and assess their effectiveness.

Consolidate your understanding of relationships between the methodologies presented in this course, their relative strengths, weaknesses and range of applicability of these methods.

Instructors

The University of Adelaide, Adelaide Frequently Asked Questions (FAQ's)

1: How much time do the candidates need to complete the Big Data Analytics Online Course?

The estimated completion time of the programme is 10 weeks.

2: What is the number of hours the learners will have to devote to the Big Data Analytics Online Certification per week?

The learners will have to devote 8–10 hours per week to this course.

3: Who are the instructors of the course?

The instructors are the faculties of the University of Adelaide, namely, Lewis Mitchel, Lecturer in Applied Mathematics, Simon Tuke, Lecturer in Statistics, and David Suter, Professor of Computer Science.

4: In which mode is the programme offered?

The course is offered completely in online mode.

5: Will the candidates be given placement support after the programme?

The candidates will not be given placement support after the programme.