GET STARTED ON YOUR DATA SCIENCE JOURNEY!

Data science has become the central approach to tackling big data problems in both business and academia. This 3-day certificate course offers an exceptional opportunity to learn how data science is done in the wild, with a focus on data acquisition, cleaning, and aggregation, exploratory data analysis and visualization, using R. Participants will use the R platform to work through real-world examples that illustrate these concepts.

R is rapidly becoming the leading language in data science and statistics. Today, R is the tool of choice for data science professionals in every industry and field. Introduction to R for Data Science course will help you master the basics of R.



“Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the defacto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world.”

Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

What is R?

R is the world’s most powerful programming language for data science, statistical computing, machine learning and graphics as well as a thriving global community of users, developers and contributors.
  •    It's the # 1 choice of data scientists.
  •    R is free, open-source software distributed and maintained by the R-project.
  •    R is taught in universities and deployed in mission critical business applications
logo for R

19-21 SEPTEMBER 2016, DUBAI

INTRODUCTION TO R

FOR DATA SCIENCE

MEDIA ROTANA

HESSA ST - DUBAI

UNITED ARAB EMIRATES

PH :+971 4 435 0000

Learning Outcomes and Goals


By the end of this three-day course, we can guarantee you will have learned the basics of R’s syntax and grammar, and you’ll have started building an effective R vocabulary for visualizing, transforming, and modelling data. You will also learn how to load, save, and transform data as well as how to write functions, generate beautiful graphs, and fit basic statistical models to your data- not bad for a 3 day gig? We will also give you a conceptual framework to help you understand the data science lifecycle, but remember, our focus is on practical tools that you can use as soon as you get out there in the jungle of data!

  • Navigate the RStudio IDE and R environment, find and install the R packages that best suit their needs;
  • Conduct Exploratory Data Analysis in R, clean-up and prepare data sets, and run the most frequently used nonparametric tests;
  • Work with probability density, mass, and distribution functions in R, as well as generate random numbers from them;
  • Work comfortably within the basics of the Linear Model in R, conduct variance analysis, run and interpret simple and multiple linear regression models;
  • Visualize simple data sets and produce standard visualizations from the Linear Model.

AGENDA

After we have discovered the types of information creatures that run through the R jungle, we begin to cut our way through it by learning how R spells its control flow, like: if, else, ifelse, switch, for, while, repeat, break… In other words, we’re already making first steps in R programming, statement by statement, loop by loop. And, yes: functions in R.
  • Assignments, relational operators, and code blocks
  • Conditional statements (if, else, ifelse, switch)
  • Loops (for, repeat, while, break)
  • Functions and code vectorization (function, apply, lapply)

Data Science is sometimes delineated from similar fields by exaggerating the role of data structuring. Many data sets originate from unstructured sources - like the majority of those found online. Knowing how to deal with strings is thus a condicio sine qua non for a serious data scientist. We’ll introduce two important packages for string manipulation - stringr and stringi.

  • Base functions that operate on strings
  • Stringr and stringi packages
  • On encodings in R

One of the most time consuming steps in any data analysis is cleaning the data and getting it into a format amenable for analysis. Base R functions provide good tools to do cleaning, subsetting and other data manipulation. To show that this can be done in more efficient way we also discuss two powerful libraries - dplyr and tidyr.

  • Base functions for cleaning the data
  • Subsetting and data aggregation
  • Advanced data manipulation with dplyr and tidyr

How to get to an intuition about a potentially huge data set loaded in R and staring right at you? There are steps to be taken - kindly advised by our colleagues in mathematical statistics - before one starts considering the modelling of any data set, be it small or big. These steps are encompassed by exploratory data analysis.

  • Summarizing data
  • Tables, frequencies, and histograms
  • Eyeballing probability distributions with Q-Q plots
  • Box plots and essential non-parametric statistics

Many problems that one might face as a Data Scientist can be handled by thinking in terms of factorial designs: there are groups of observations that need to be compared. Simple designs are assessed by t-tests; more complex ones by various ANOVA designs. All in R, of course.

  • Understanding the difference between within-subjects and between-subjects designs
  • Paired and independent samples t-tests
  • One-way and two-way between-subjects ANOVA
  • An example of a repeated-measures ANOVA

Can we predict future events based on past data? This session will discuss the linear model function in R and provide a detailed guide through the process of linear regression - the most basic and the best known prediction tool. We cautiously dive into the ocean of prediction models from a solid background on the assumptions of linear modeling.

  • Theoretical background of linear regression
  • Linear model function in R
  • Predictions from the linear model
  • Understanding residuals, confidence intervals, and influential cases

Who Should take this course



Have you tried learning data science and R from books or online, but have been discouraged? If so, this is the course for you .

Introduction to R for Data Science course is aimed at Business and Technology Professionals, Journalists, Developers, Architects, Managers, Executives, Data Analysts, Performance Engineers, Project Managers, Teaching Staff and all those who wish to begin using R for data science and predictive analytics for the first time. This is also a great opportunity for recent university graduates who would like to explore data science as a career possibility.

INDUSTRIES, GOVERNMENT, PUBLIC SERVICES, STARTUPS

  Government Services & Private Sector

  Telecommunications

  Retail

  Media & Entertainment

  Tourism & Hospitality

  Banking & FInancial

  Healthcare

  Power & Utilities

  Others


Governments and organizations across the world are hiring data scientists to help them glean insights from the terabytes of data that they collect everyday. While the amount of data produced and stored is growing exponentially, there is a severe shortage of talent to analyze this data and extract valuable insights from it.

INTRODUCTION TO R FOR DATA SCIENCE

3 day certificate course by the School of Data Science equips you with understanding and practical experience needed to be a skilled professional in the fast emerging field of Data Science.


Instructors and Data Scientists



Masterclass trainers and facilitators are passionate about meeting each participants learning needs. They have been chosen both for their extensive practical industry experience and for their ability to educate and interact with natural empathy:

Goran

Goran S. Milovanovic

Data and Cognitive Scientist

Goran S. Milovanovic is a Data and Cognitive Scientist who studies behavioral decision theory, perception of risk and probability, statistical learning theory, and psychological semantics. He has studied mathematics, philosophy, and psychology at the University of Belgrade, and graduated from the Department of Psychology in 2004. He began his PhD studies at the Doctoral Program in Cognition and Perception, New York University, USA, 2005 - 2007, while defending a doctoral thesis on the rationality of cognition at the Faculty of Philosophy, University of Belgrade, in 2013.

Goran has lectured cognitive psychology and worked as a teaching assistant at many courses, in Serbia and USA. He co-edited and co-authored several books on quantitative, empirical studies of Internet behavior, attitudes towards the new information technologies, ICT adoption, and the development of Information Society. He managed research projects on Internet Governance in cooperation with DiploFoundation and stakeholders such as AT&T and the Commonwealth IGF Initiative. He worked as a Head of Research for a major international market research agency in Serbia, conducting customer satisfaction and evaluation studies for various stakeholders (from FMCG to banking sectors), and provided research and data analysis consulting services to various stakeholders (e.g. UNICEF) in the public health sector. Goran currently works as a Data Scientist for DiploFoundation, conducting text-mining and analytical studies of Internet Governance in the programming language R.

Branko Kovac

Data Analyst and Software Engineer

Branko Kovacis a Data Analyst and Software Engineer who studies Machine Learning and Artificial Intelligence. Branko is currently completing his MA at the University of Belgrade, School of Electrical Engineering, where he has already earned a B.A. in Computer Science and Software Engineering. He is a founding member of Data Science Serbia, a non-profit initiative for collaboration and education of Data Science professionals and enthusiasts, and a founder of the Institute of Contemporary Sciences in Belgrade, Serbia. He currently works as Data Analyst for CUBE Risk Management Solutions, a leading business intelligence company in Serbia, where he specializes in working with government open data and R programming that provides data solutions for more than 15 banks operating in Serbia.

Ali Syed

Chief Data Scientist and Strategist
Persontyle, UK and European Data Science Academy, EU

As a global data and analytics thought leader, Ali is collaborating with thinkers, researchers, designers, makers, doers, and business leaders. He has more than 16 years of professional experience and success assisting public and commercial organisations in using data analytics, insights and machine intelligence as a value amplifier. He works with people to understand and translate their aspirations into data and analytical solutions that enhance their ability to make choices, better decisions, realise performance gains and uncover opportunities. Before founding Persontyle, Ali worked with some of the leading technology and consulting organisations of the world namely PwC, KPMG BearingPoint, Sapient, EMC, UBS, NHS UK, and Capgemini.

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Contact Nitesh Marwaha for your dedicated sponsorship representative today.

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Be part of this amazing event to stand out from your competition and gain valuable brand exposure. Sponsoring the Everati events gives you following advantages:

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Organizers



Everati is a business and technology innovation media company specializing in enterprise transformation. We bring our exceptional cross-industry knowledge to the government, industry and the professionals through a combination of events, training, open meetings, hackathons, unconferences , and summits, providing cutting-edge insights to drive growth in digital economy.

Data Science Middle East (DSME) has been started with the vision to create regional collaboration on digital skills and data talent development that brings together business and technology professionals, researchers, experts, practitioners, and industry leaders to promote data science literacy, research and development through open learning projects for digital literacy, capacity building, and community engagements.

The School of Data Science is an education initiative to help meet the world’s demand for professionals and leaders skilled in developing and utilizing automated and intelligent methods of using data a strategic resource. We aim to bring accessible, affordable, practical, and interactive data science and engineering education to the world. We offer training programs and tailored corporate learning solutions to cover the concepts, technology and applied practices you'll need throughout the entire lifecycle, from asking the relevant questions to making predictions using machine learning models and visualizing results.

Persontyle is a global consulting and machine intelligence products company, providing a broad range of services and solutions in strategy, digital transformation, data science, machine learning, IoT, and digital talent development. Persontyle brings together deeply experienced business specialists, programmers, data scientists, machine learning experts, data analysts, UX designers and data engineers with diverse backgrounds to tackle important business innovation challenges. We are a team of creative, passionate and honest people who band together on challenges from data engineering to machine learning. Persontyle is headquartered in London, UK.

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