I will talk about some good resources to learn the R programming language that will give you the right approach to making things easier.
The popularity of the R programming language is increasing, especially in data science and analytics.
As it provides better data visualization techniques, R programming plays a crucial role in statistics.
However, learning this language could be frustrating if you do not have a clear path to approach it. In the past, you might have struggled a lot while learning R or any other language.
Trust me; you are not alone!
Don’t blame it on yourself entirely or the language; the issue might have been in your approach. The way you learn something has a massive hand in delivering the final result.
If you have a clear strategy of how and why you should learn a particular language, the chance of your being proficient in it amplifies. Similarly, if your goal and strategy do not align, you might get bored with the language and end up quitting it midway.
It is analogous to learning a spoken language.
So, when you are ready to learn R, get your motive clear in the first place, be it expanding your knowledge or finding a career in data science. Next, prepare a strategy and align it with your goal.
…And start learning.
In this article, I will talk about some good resources to learn the R programming language that will give you the right approach to making things easier.
But first things first,
#r #r-programming #data-science #machine-learning
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
Want to know how to become a data scientist from scratch? This comprehensive guide will take you through every necessary step to become a successful data scientist.
A Complete Career Guide on How to Become a Data Scientist
Data science has become the hottest career option for students. It’s become one of the fastest-growing career paths. In this high-tech world, every business and organization needs data scientists to leverage their data to the fullest extent. This provides ongoing opportunities for those who want to get hired into a data scientist role. This blog post will take you through all the necessary steps you need to know to become a successful data scientist.
#learn-data-science #data-science #data-science-skills #become-a-data-scientist #data-scientist
Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In this article, we list down 50 latest job openings in data science that opened just last week.
(The jobs are sorted according to the years of experience r
Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.
Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.
Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.
Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.
**Location: **Bibinagar, Telangana
Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.
#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india
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!
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.
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!
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:
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
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management