• Duración:
10 semanas
• Dedicación:
8–10 horas por semana
• Precio:

GRATIS

• Institución
• Tema:
• Nivel:
Introductory
• Idioma:
English
• Transcripción de video:
English

## Prerrequisitos

None

### Sobre este curso

Omitir Sobre este curso

Computational thinking is an invaluable skill that can be used across every industry, as it allows you to formulate a problem and express a solution in such a way that a computer can effectively carry it out.

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 visualizing data. You will develop skills in data-driven problem design and algorithms for big data.

The course will also explain mathematical representations, probabilistic and statistical models, dimension reduction and Bayesian models.

You will use tools such as R and Java data processing libraries in associated language environments.

### Lo que aprenderás

Omitir Lo que aprenderás
• 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

### Plan de estudios

Omitir Plan de estudios
Section 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.

Section 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.

Section 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 dataframes into a single dataframe; Learn how to implement these methods in mapReduce.

Section 4: Transforming data and dimension reduction
Transform data so that 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.

Section 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.

Section 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.

Section 7: Graphs
Encode directed and undirected graphs in different data structures, such as matrices and adjacency lists; Execute basic algorithms, such as depth-first search and breadth-first search.

Section 8: Probability
Determine the probability of events occurring when the probability distribution is discrete; How to approximate.

Section 9: Hashing
Apply hash functions on basic data structures in Java; Implement your own hash functions and execute, these as well as built-in ones; Differentiate good from bad hash functions based on the concept of collisions.

Section 10: Bringing it all together
Understand the context of big data in programming.

## Conoce a tus instructores

Lewis Mitchell
Lecturer in Applied Mathematics
Markus Wagner
Senior Lecturer, School of Computer Science
Simon Tuke
Lecturer in Statistics
Gavin Meredith
Research Associate, School of Computer Science

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## Preguntas frecuentes

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 ended on December 1, 2018.
The second release of the course started on December 1, 2018 and ends on December 1, 2020.
The third release of the course starts on March 1, 2019 and ends on December 1, 2020.

### ¿Quién puede hacer este curso?

Lamentablemente, las personas de uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.