Data Science is the resolution of business/organization problems through mathematics, programming and the scientific method that involves the creation of hypotheses, experiments and tests through data analysis and the generation of predictive models. If you are looking to hire a company for data science development, indeed you have come to the right place. Here are the top 10 data science developers in the USA.
Zazz is a team of creative designers and developers building great digital products in Seattle and San Francisco. Our collective experience in the technology industry includes mobile app development, IOT application development, blockchain development with a design first approach to product development.
Appstudio is a full service Mobile App Design & Development Company offering services in Native iOS Development (Swift 3.0), Native Android Development (Java), React Native Development & Unity Game Development. They have collaborated with Fortune 500 companies, Startups and Mid Sized firms across a spectrum of industries, ranging from Health Care & Finance to On-Demand Services, to create Mobile apps that are actively being used by Millions of users across the globe.
SCI-BI works with some of the best Architects in the Information Technology domain, who not only design the system, but also act as Advisory for constant improvement of your IT practice. So when you choose to work with SCI-BI or one of its associates, you can be rest assured that your project is in the safest hands and the clearest minds who know how to get the job done.
We are a young and spirited team of IT specialists with a mission to reach the altitudes of professional excellence. Over the course of time, we expanded in forte and resources. We are individually specialized in the services that we offer which make us unique among the others in the market.
We are a product engineering services company specializing in big data engineering and visualization. We have a strong technical expertise in building highly scalable analytics on multiple cloud service providers. We have developed analytics based products in the telecom, logistics, agriculture, ad-tech, tourism, education, healthcare, IoT, and digital signages/kiosks industries.
We are HatchWorks Technologies, a software solutions, and technology transformation company. HatchWorks software solutions fuel your digital transformation. We collaborate with companies ready to reinvent through technology. Our team of strategists, inventors, and advisors builds with you to disrupt the status quo. We work full-stack in nearly any platform and take projects full lifecycle from ideation to launch and beyond.
Altoros is a 400+ person strong consultancy that helps Global 2000 organizations with the methodology, training, technology building blocks, and end-to-end solution development required to support digital transformation at scale. We turn cloud-native app development, customer analytics, blockchain, and artificial intelligence into products with a sustainable competitive advantage.
Coherent Solutions is a software product development and consulting company that solves customer business problems by bringing together global expertise, innovation, and creativity to produce world-class technology solutions.
We solve our clients’ toughest data intelligence challenges in weeks, not years. We are strategic data and analytics advisors first, and design and build what we reccomend. Our solid track record of client success coupled with our industry-leading tech expertise ensures your project gets completed with as little pain as possible, on budget and with minimum disruption.
SmartCat is boutique consultancy firm that focuses on Big Data systems - from Data Science to Data Engineering and DevOps. We use experience from each of our areas of expertise to offer both advisory and implementation services to our clients.
Data Science is closely linked to business, but in the end, it is a science, or it is in the process of becoming one, or perhaps not. I think it could be very useful for Data Science to be a science because if that is the case, every project in Data Science should be at least:
- Complies with regulations
Big Data + AI + Data Science = General Artificial Intelligence
I am talking about General Artificial Intelligence (IAG) as the main objective of this revolution. IAGs are general-purpose systems with intelligence comparable to that of the human mind (or perhaps beyond humans).
We need Big Data as a catalyst to reach AGI, because with more data, more new ways of analyzing data, in addition to better software and hardware, we can create better models and a better understanding. We need the current state of AI, very close to Deep Learning, Deep Reinforcement Learning and its surroundings (more about Deep Learning here ), and then we need Data Science as the controller and the science behind this revolution.
What about data engineering, which is the first to deliver data to the data science team? As it is a sophisticated field, I prefer to protect it from the hegemonic aspirations of data science and, moreover, it is much closer to software engineering than statistics.
The difference between data engineering and data science is the difference between before and after. Feel free to see the difference between data engineering and data science as a before and after. Most of the technical work that leads to the birth of data (before) can be called “data engineering” and all we do when some data arrives (after) is “data science.”
Decision Intelligence (DI)
DI has to do with decisions, including making decisions at scale with data, which makes it an engineering discipline. Extends the application of data science with the ideas of social and management sciences. Decision intelligence adds components of the social and management sciences.
In other words, it is a superset of those pieces of data science that do not deal with research things, such as the creation of fundamental methodologies for general use.
If you still don’t know what decisions you want to make, the best thing you can do is go out for inspiration to discover them. This is known as data analysis or analytical or descriptive analytics or exploratory data analysis (EDA in English) or knowledge discovery (KD in English), depending on tastes and colours. And contrary to what the saying goes, this is what the authors have written a lot about.
Start here, unless you already know how to structure your decision making. The good news is that this is easy. Think of your data set as a group of negative photos that you found in a dark development room. Data extraction involves using the equipment to reveal the photos as quickly as possible, so you can see if there is something inspiring or interesting in them. As with the photos, remember not to take what you see seriously. You didn’t take the photos, so you don’t know much about the stories behind them. The golden rule of data mining is: focus on what is here. Just draw conclusions about what you can see, never about what you cannot see (for that you need statistics and much more experience). Data mining experience is judged by the speed with which you can examine the data.
The dark development room is intimidating at first, but not much can be done about it. Just learn to use the development equipment well. Here is a tutorial in R and here in Python to get started. You can call yourself a “data analyst” as soon as you start work, and you can call yourself an “expert analyst” when you can reveal the photos (and all other types of data sets) at lightning speed.
Inspiration is cheap, but rigour is expensive. If you want to go further with the data, you will need specialized training. Having a bachelor’s degree and a postgraduate degree in statistics, my opinion may be a bit biased, but I think that statistical inference (statistics for short) is one of the three areas, the most difficult and loaded with philosophy. Becoming good at this takes more time.
Inspiration is cheap, but rigour is expensive.
If you want to make important, high quality, and controlled risk decisions that are based on conclusions about the world beyond the available data, you will have to add statistical skills to your team. A good example is a moment when your finger is circling around the start button of an Artificial Intelligence (AI) system and it comes to your mind that you should verify that it works correctly before squeezing it (it is always a good idea, in serious). Get away from the button and call the statistician. Statistics is the science of changing your mind (when there is uncertainty).