In this blog, I will explain the hype around the new Nvidia Ampere 3000 series GPUs, how they compare to the Turing 2000 series cards, and the current benchmarks available today for each RTX generation. I will also go over rumors for upcoming variants of the 3080, as well as the reports from users saying the cards they received were defective. At the bottom of the article, I will go over my opinion and the advice I have for anyone trying to get them, regardless if it’s for gaming, data science, or flexing on the latter two.
Nvidia has always been a winner within the GPU space, but it’s for a variety of reasons and not just their “power”. When you want to compare computational power or the amount of TeraFlops (TF) between Nvidia and AMD GPUs, there is actually no big difference- and often, AMD comes out on top in this regard. For example, when comparing the AMD Radeon RX Vega 64 ($~400) and the Nvidia 2080 (~$700), you see the cracks in the argument that Nvidia is the _most powerful. _When comparing the TeraFlop performance between these cards, the AMD Radeon 64 comes out on top in every aspect (FP16, FP32, and FP64). So if AMD offers cards that are more powerful (in terms of TF) at a much lower cost, then why is Nvidia regarded as the best manufacturer of GPUs? The real answer comes when looking at the bells and whistles that Nvidia offers, and how they target multiple different types of customers ranging from Graphic Designers, Data Scientists, and most importantly, Gamers. When it comes to games, the amount of TF does not equate to the gaming experience they provide. Looking at the Vega 64 and RTX 2080, the RTX 2080 crushed the Vega in every single gaming benchmark, averaging a 20%-50% increase in Frames Per Second(FPS) [Vega 64 vs 2080 Benchmarks]. I know some might be thinking, why on earth did you use the comparison between the Vega64 and RX 2080? I know these cards are completely different in terms of their usage, but I wanted to illustrate that just saying “more powerful” is only half the story. There are multiple reasons why Nvidia is better than AMD in terms of GPUs, and only some of these reasons are specific to the way you plan to use the hardware. I will go into what I mean in more detail throughout the rest of the blog post.
#deep-learning #nvidia #gaming #amd #data-science
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
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
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
Around once a month, I get emailed by a student of some type asking how to get into Data Science, I’ve answered it enough that I decided to write it out here so I can link people to it. So if you’re one of those students, welcome!
I’ll segment this into basic advice, which can be found quite easily if you just google ‘how to get into data science’ and advice that is less common, but advice that I’ve found very useful over the years. I’ll start with the latter, and move on to basic advice. Obviously take this with a grain of salt as all advice comes with a bit of survivorship bias.
#big data & cloud #data science #data scientist #statistics #aspiring data scientist #advice for aspiring data scientists
According to a recent study on analytics and data science jobs, the number of vacancies for data science-related jobs in India has increased by 53 per cent, since India eased the lockdown restrictions. Moreover, India’s share of open data science jobs in the world has seen a steep rise from 7.2 per cent in January to 9.8 per cent in August.
Here is a list of 5 such companies, in no particular order, in India that are currently recruiting Data Scientists in bulk.
#careers #data science #data science career #data science jobs #data science recruitment #data scientist #data scientist jobs