Learn R the Right Way in 5 Steps — Learn Data Science at Dataquest

R is an increasingly popular programming language, particularly in the world of data analysis and data science. But learning R can be a frustrating challenge if you’re not sure how to approach it.

If you’ve struggled to learn R or another programming language in the past, you’re definitely not alone. And it’s not a failure on your part, or some inherent problem with the language. Usually, it’s the result of a mismatch between what’s motivating you to learn and how you’re actually learning.

This mismatch causes big problems when you’re learning any programming language, because it takes you straight to a place we like to call the cliff of boring.

What is the cliff of boring? It’s the mountain of boring coding syntax and dry practice problems you’re generally asked to work through before you can get to the good stuff — the stuff you actually want to do.

Nobody signs up to learn a programming language because they love syntax. Yet many learning resources, from textbooks to online courses, are written with the idea that students need to master all of the key areas of R syntax before they can do any real work with it.

That’s where new learners tend to drop off in droves. You get excited about learning a programming language because you want to do something with it, and but then you’re immediately led to this huge wall of complicated, boring stuff that’s between you and what you actually want to be doing. It’s no surprise that lots of students give up or drop off at points along their climb up this “cliff.”

There’s no way around learning syntax, in R or any other programming language. But there is a way to avoid the cliff of boring.

It’s a shame that so many students drop off at the cliff, because R is absolutely worth learning! In fact, R has some big advantages over other language for anyone who’s interested in learning data science:

• The R tidyverse ecosystem makes all sorts of everyday data science tasks very straightforward.
• Data visualization in R can be both simple and very powerful.
• R was built to perform statistical computing.
• The online R community is one of the friendliest and most inclusive of all programming communities.
• The RStudio integrated development environment (IDE) is a powerful tool for programming with R because all of your code, results, and visualizations are together in one place. With RStudio Cloud you can program in R using RStudio using your web browser.

#learning and motivation #learn r #r #rstats #study

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How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

Why You Should Learn R — Learn Data Science with Dataquest

So you want to learn data skills. That’s great! But we offer tons of data science courses. Why should you learn R programming specifically? Would it be better to learn Python?

If you really want to dig into that question, we’ve demonstrated Python vs. R to show how each language handles common data science tasks. And while the the bottom line is that each language has its own strengths, and both are great choices for data science, R does have unique strengths that are worth considering!

1. R is built for statistics.

R was originally designed by statisticians for doing statistical analysis, and it remains the programming choice of most statisticians today. R’s syntax makes it easy to create complex statistical models with just a few lines of code. Since so many statisticians use and contribute to R packages, you’re likely to be able to find support for any statistical analysis you need to perform.

For related reasons, R is the statistical and data analysis language of course in many academic settings. If you aspire to work in academia — or if you’d just like to read academic papers and then be able to dig into the code behind them — having R programming skills can be a must.

2. R is a popular language for data science at top tech firms

Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. Twitter uses R for data visualization and semantic clustering. Microsoft, Uber, AirBnb, IBM, HP – they all hire data scientists who can program in R.

And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. Even the New York Times uses R!

3. Learning the data science basics is arguably easier in R.

Python may be one of the most beginner-friendly programming languages, but once you get past the syntax, R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

Because of that, learning the core skills of data science – data manipulation, data visualization, and machine learning – can actually be easier in R once you’ve gotten through the basic fundamentals. Check out, for example, how straightforward it is to create these common data visualization styles in R.

And of course, there’s the tidyverse, a group of packages that’s built specifically to make data work in R quicker, easier, and more accessible. In fact, that’s really an advantage in and of itself:

4. Amazing packages that make your life easier.

Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. The dplyr package, for example, makes data manipulation a breeze, and ggplot2 is a fantastic tool for data visualization.

These packages are part of the tidyverse, a growing collection of packages maintained by RStudio, a certifed B-corp that also creates a free-to-use R environment of the same name that’s perfect for data work. These packages are powerful, easy to access, and have great documentation.

#learning and motivation #data cleaning #data science #data visualization #learn r #data analysis

5 Data Science Podcasts To Follow in 2021

If we only learn data science through a rigid curriculum created by universities or online courses from Coursera or Udemy, we may find the learning process too boring. If you ever find yourself losing motivation in this long journey of studying data science, you may just need some podcasts to break the routine and get some inspiration. The major difference between these two approaches of learning is that the former focuses on theory and concepts, whereas the latter introduces more practical experience and projects that add flesh to the bones.
Listening to podcasts is a great way to absorb knowledge while you are commuting or doing the chores. One of the amazing apps that I recommend using is called “Airr” which allows users to highlight the content of the podcast and transcribe the highlight into notes. This tool is especially useful for technical podcasts since information is more easily erased if you are more of a visual learner than an auditory learner. Therefore, putting it into notes somewhere would assist in transforming them into long term memory.
On the other hand, if you are more of a visual person who prefers to learn through reading, then have a read of the Data Science website list that I curated :)

#data-science-inspiration #data-science-podcast #learn-data-science #data-science #data-science-resources

5 stages of learning Data Science

With recruiters listing a myriad of “preferred skills” in their job postings, learning Data Science can get quite overwhelming at times. Dividing the journey up into five chapters can provide a clearer picture of what lies ahead.

#machine-learning #learn-data-science #data-science-training #python-for-data-science #data-science-courses

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition