Computational Thinking and Big Data Training

Live Online & Classroom Enterprise Training

Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets.

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What is Computational Thinking and Big Data Training about?

In this course, part of the Big Data MicroMasters program, you will learn how to apply computational thinking in data science. You will learn core computational thinking concepts, including decomposition, pattern recognition, abstraction, and algorithmic thinking.

You will also learn about data representation and analysis and the processes of cleaning, presenting, and visualising data. You will develop skills in data-driven problem design and algorithms for big data.

What are the objectives of Computational Thinking and Big Data Training ?

Understand and apply advanced core computational thinking concepts to large-scale data sets
Use industry-level tools for data preparation and visualisation, such as R and Java
Apply methods for data preparation to large data sets
Understand mathematical and statistical techniques for attracting information from large data sets and illuminating relationships between data sets

Who is Computational Thinking and Big Data Training for?

Network Operation Managers
Financial Managers
CRM Managers
Top IT Managers in Telco Office
Business Analysts in Telco
QA Managers

What are the prerequisites for Computational Thinking and Big Data Training?

None

Available Training Modes

Live Online Training

60 Hours

Self-Paced Training

60 Hours

Course Outline Expand All

Expand All

Module 1: Data in R

Identify the components of RStudio; Identify the subjects and types of variables in R; Summarise and visualise univariate data, including histograms and box plots.

Module 2: Visualising relationships

Produce plots in ggplot2 in R to illustrate the relationship between pairs of variables; Understand which type of plot to use for different variables; Identify methods to deal with large datasets.

Module 3: Manipulating and joining data

Organise different data types, including strings, dates and times; Filter subjects in a data frame, select individual variables, group data by variables and calculate summary statistics; Join separate data frames into a single data frame; Learn how to implement these methods in MapReduce.

Module 4: Transforming data and dimension reduction

Transform data so it is more appropriate for modelling; Use various methods to transform variables, including q-q plots and Box-Cox transformation, so that they are distributed normally Reduce the number of variables using PCA; Learn how to implement these techniques into modelling data with linear models.

Module 5: Summarising data

Estimate model parameters, both point and interval estimates; Differentiate between the statistical concepts or parameters and statistics; Use statistical summaries to infer population characteristics; Utilise strings; Learn about k-mers in genomics and their relationship to perfect hash functions as an example of text manipulation.

Module 6: Introduction to Java

Use complex data structures; Implement your own data structures to organise data; Explain the differences between classes and objects; Motivate object orientation.

Module 7: Graphs

Encode directed and undirected graphs in different data structures, such as matrices and adjacency lists; Execute basic algorithms, such as depth-first and breadth-first search.

Module 8: Probability

Determine the probability of events occurring when the probability distribution is discrete; How to approximate.

Module 9: Hashing

Apply hash functions on basic data structures in Java; Implement your hash functions and execute these as well as built-in ones; Differentiate suitable from bad hash functions based on the concept of collisions.

Module 10: Bringing it all together

Understand the context of big data in programming.

Who is the instructor for this training?

The trainer for this Computational Thinking and Big Data Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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