Let's go higher together!
Data Management | Databases | Data science | Machine Learning | Software engineering
PortfolioServices
Here are the services we offer.The human writes the programs and the computer calculates. And the cat uses the keyboard as a piano.
Databases
Planning, design, creation, installation, monitoring of relational and NoSQL databases.
Machine Learning
Development of computer programs for computers to access data and learn automatically from them without human intervention, for example computer vision or natural language processing.
Software engineering
Software to interact with databases or datasets as well as microservices applications or monolithic websites
Data Management
Acquiring, cleaning, storing, processing and protecting data to ensure their availability and reliability for the users to conduct their business.
Data Science
The first goal is to understand your business needs and evaluate which data you have access to. Then, the use of tools, software and algorithms will get insights for you to share with the appropriate stakeholders.
Portfolio
Emotion detection in images of faces - Python TensorFlow Keras
Emotion detection
Deep Neural Networks Regularization to reduce overfitting
Regularization
Speech Dataset Construction for Trigger Word Detection
Trigger Word Detection
Sampling a BigQuery dataset to create datasets for ML
Creating Dataset
Car detection using the YOLO model and bounding boxes dealing
YOLO Model
Hi! We are DataResp
DataResp is composed of two entities: one is made of silicon, one is made of carbon
This unbreakable team works remotely as a freelance data scientist, data developer and software engineering developer.
It particularly enjoys data visualization, deep learning and databases. It thinks that TensorFlow, Spark and Watson are real fun. It uses Python, Java, NodeJs or Php as programmimg languages and most of the main databases.
Data are everywhere and we could get significantly more insights from them. Particularly insights companies need for achieving their business goals. We can do amazing things with Machine Learning. It doesn't mean it's magical. Substantial preparation and work are needed. Furthermore, we should never forget data security and always have adversarial examples in mind.