What you'll learn
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Manipulate numeric and textual data types using tidyverse in RStudio
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Read, write, and save data files from SPSS, STATA, Excel, csv, SAS and scrape web pages using R.
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Define and manipulate data structures, including vectors, factors, lists, and data frames.
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Use of 5 verbs : select, mutate, summarize, filter and arrange to deal with 80% data wrangling
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Dealing with dirty excel and other data formats
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Data wrangling, visualization and basic regression analysis
Course Description
The R language plays a critical role in data analysis, machine learning, quarto documentation and is a common programming language when working in the field of data science & analytics. This course will introduce you to R language fundamentals with the primary goal being to learn R in a way that allows you to practically tackle a problem at hand.Â
This is not a course on just R coding. You will learn how to work data types, techniques for manipulation, and how to implement fundamental programming tasks.
This course emphasizes learning by doing as data science is not something to learn about but to do about. You will learn to write a project in Quarto in Posit to produce data-driven insights.
Anyone working with data, analytics and machine learning should join this course for improving one’s productivity, career growth and increasing earning opportunities through free-lancing.
The course is beginner friendly, with no previous experience with R or in programming required. This course will enable you to start your data analytics journey or will enhance your skills if you are already working with data.
R is a programming language and environment designed mainly for the purposes of data mining, statistical analysis, data visualization, and machine learning. R is widely popular with Data scientists and data analysts because of its wonderful capacity to visualize data.Â
When sharing findings with non-technical teams, having graphs, charts, and images helps make data easier to understand - making it a versatile and in-demand tool.
Course Content
Week 1: Introduction to R
Introduction to R, Rstudio, Rstudio Cloud, R-packages, Libraries, reading and cleaning data. Main objective is to learn R environment, data formats, and how to start working with data
- Installing R studio
- Navigating R Studio interface
- Learning about data formats, and how to start working with dataÂ
Week 2: Learning Data Wrangling and Cleaning
This module will teach students how to detect outliers, use sources from the open web, and create mapsÂ
- Learning 5 verbs of data
- Select
- Â Mutate
- Â Filter
- Summarize
- Arrange, slice
- Working with real world datasets, including Alif Ailan, Labour Force Survey and built in R data sets
Week 3: Data handling
This module will introduce students to Data Analysis Expression (DAX) and cover the most commonly used DAX formulas in Power BI.
- Joining/Merging Data Sets
- Data summary by groups
- Constructing awesome report tables
Week 4: Advanced/More Topics in R
Using Ggplot2 to create awesome graphs along the likes of the type you would see in The Economist, Wall Street Journal, and BBC.
- Ggplot2 basics
- Annotating graphs
- Making sense of data
Week 5: Fundamentals of modeling in R
Basic regression analysis, having tables of various models in nice tabular format with one click.
- OLS/MLR
- Time Series Analysis
Trainer
Dr Zahid Asghar
I am Professor at the School of Economics, Quaid-i-Azam University, Islamabad. I have extensive background working with applied econometrics, development issues (food security, urban and health) and using advanced analytics to enable data stakeholders to make informed decisions. I have also served as Director, School of Economics, Registrar Quaid-i-Azam University, Islamabad and I am also member of Research Advisory Committee (RAC) of RASTA Competitive Grants Program for Policy-oriented Research.
Frequently Asked Questions
No. This is a progressive learning course, so one-week enrollment is not allowed.
Learning R is one of the most important computer skills for researchers, free-lancers, data scientists, data analytics, machine learners, data scrapers among many others. This course will be supplemented by a case study on 200,000 properties listed on zameen.com. It will give business data science perspective in practice.
No prior knowledge of R, or programming is required.
Yes, but there are a few conditions. You must complete all the assignments, have at least 75% attendance, and have scored reasonably well on your quizzes & final course project. This is just for us to ensure that you can grasp the fundamentals of Power BI before we certify you.