Applied Data Science with R - 6-week tutor-led online course
|Mind Project Ltd|
Simon Walkowiak MSc, MBPsS
04/02/2021 - 11/03/2021
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Mind Project Ltd
1. Course description.
During the “Applied Data Science with R” open-to-public online training course you will learn how to apply the R programming language to carry out essential data management, wrangling and processing activities.
This course will introduce you to all basic concepts of data processing and analysis in R environment. More specifically, you will learn to understand different types of data and common data structures available in R language, prepare, transform and manage datasets and their variables, export/import data from various file formats (Excel spreadsheets, csv, tab, txt etc.), create simple graphical representations of the data (bar plots, histograms, box plots etc.), obtain summaries, data aggregations, cross-tabulations, frequency and pivot tables, and run and explain results of basic statistical tests e.g. correlations, t-tests etc. The course will also provide an introduction to modelling using multiple linear regression methods and will introduce you to data visualisation techniques available in R for data reporting and research communication.
The course will cover modern approaches in applied data science using R language and its rich ecosystem of external libraries including tidyverse family of packages e.g. dplyr, ggplot2, tidyr, readr, tibble and other essential R libraries for data wrangling and statistics.
2. Course programme.
This instructor-led course duration is planned over 6 teaching weeks.
In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills by watching pre-recorded instruction videos via our Mind Project Learning Platform and working through set tasks (e.g. quizzes) as well as homework coding exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment is 40-50 hours over 6 teaching weeks.
Start date: Thursday, 4th of February 2021 @10:00am London (UK) time
Week 1: First step with R language
Week 2: Data wrangling with R
Week 3: Exploratory data analysis with R
Week 4: Inferential statistics and hypothesis testing with R - Part 1
Week 5: Inferential statistics and hypothesis testing with R - Part 2
Week 6: Linear and non-linear models with R
3. Course pre-requisites and further instructions
4. Your course instructor.
Your instructor for this course will be Simon Walkowiak. Simon is a director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 150 in-house or open-to-public statistical training courses in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse etc.), research and academia (GSMA, CERN, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).
Should you have any questions please contact Mind Project Ltd at email@example.com or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/applied-data-science-with-r-tutor-led-online-course-feb21/.
Entry (no or almost no prior knowledge)
By 10th of January 2021 (Early Bird offer):
Website and registration
Descriptive Statistics, Correlation, Effect size , Statistical Theory and Methods of Inference, Parametric statistics, Non-parametric statistics, Regression Methods, Ordinary least squares (OLS), ANOVA, ANCOVA, Linear regression, R, Data Visualisation
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