• Length:
    10 Weeks
  • Effort:
    8–10 hours per week
  • Price:

    FREE
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  • Institution
  • Subject:
  • Level:
    Intermediate
  • Language:
    English
  • Video Transcript:
    English

Associated Programs:

Prerequisites

Candidates pursuing the MicroMasters program are advised to complete Programming for Data ScienceComputational Thinking and Big Data & Big Data Fundamentals before undertaking this course.

About this course

Gain essential skills in today’s digital age to store, process and analyse data to inform business decisions.

In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Apache Spark and R.

Topics covered in this course include:

  • cloud-based big data analysis;
  • predictive analytics, including probabilistic and statistical models;
  • application of large-scale data analysis;
  • analysis of problem space and data needs.

By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative.

What you'll learn

  • How to develop algorithms for the statistical analysis of big data;
  • Knowledge of big data applications;
  • How to use fundamental principles used in predictive analytics;
  • Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems.
Section 1: Simple linear regression
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.

Section 2: Modelling data
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.

Section 3: Many models
Manipulate nested dataframes in R; Use R to apply simultaneous linear models to large data frames by stratifying the data; Interpret the output of learner models.

Section 4: Classification
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.

Section 5: Prediction using models
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.

Section 6: Getting bigger
Set up and apply sparklyr; Use logical verbs in R by applying native sparklyr versions of the verbs.

Section 7: Supervised machine learning with sparklyr
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.

Section 8: Deep learning
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.

Section 9: Deep learning applications and scaling up
Understand some of the ways in which massive amounts of unlabelled data, and partially labelled data, is 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.

Section 10: Bringing it all together
Consolidate your understanding of relationships between the methodologies presented in this course, theirrelative strengths, weaknesses and range of applicability of these methods.

Meet your instructors

Lewis Mitchell
Lecturer in Applied Mathematics
University of Adelaide
Simon Tuke
Lecturer in Statistics
University of Adelaide
David Suter
Professor of Computer Science
University of Adelaide

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Frequently asked questions

Question: This course is self-paced, but is there a course end date? 
Answer: Yes. The first course release started on May 15, 2017 and ends on December 1, 2018. 
The new release of the course starts on December 1, 2018 and ends on December 1, 2020. 

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.