Data Sciences, Machine Learning, Predictive Analytics, and Artificial Intelligence
The sheer volume of data captured by organizations and how it is managed is a serious challenge. Just because you have a lot of data does not mean you have the right data. Flexible, parallel storage, and processing platforms such as Hadoop and Spark are common. However, the most state-of-the-art algorithm, the biggest compute cluster, and any magnitude of big data cannot create a trustworthy model without the appropriate data and data preparation. Milletech has a strong pool of data science professionals with skills in analyzing large amounts of data, data mining, and programming skills. Our professionals provide deep skills in the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process
Systems that seek to understand and emulate human behavior. As well as to provide a more natural and intuitive interface between man and machine. This typically involves deploying systems that interface with people in their “native tongue.” In other words, without requiring a user to write or understand code. Cognitive computing platforms accomplish this using a myriad of techniques including natural language processing, advanced machine learning algorithms (including deep learning), and natural language generation.
Natural Language Processing
Capabilities that allow machines to understand written language, voice commands, or both. Natural language processing (NLP) includes the ability to translate language into a form that a machine or algorithm can understand. Natural language generation (NLG) allows the machine to then communicate results or responses in “plain English” (or any language it’s designed to support).
Robust Data Strategy
Machine learning runs on data. A lot of data. Establishing a data process to effectively identify, acquire (or create), provision, and access high-quality data and information assets is critical. To that end, governance policies and the data ecosystem must support exploratory environments (often referred as sandboxes) as well as production environments. This requires a multi-tiered approach to balance access and agility without sacrificing security, privacy, or quality. The introduction of non-traditional (big) data sources including unstructured text, voice, pictures and so on may also require new data management capabilities.
Our Data scientists are result-oriented, with exceptional industry-specific knowledge and possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses on data warehousing, mining, and modeling to build and analyze algorithms.
Our Data Sciences to utilize key technical tools and skills in the following domain.
Our Data scientists possess critical analytical skills and the ability to mine, clean, and present data. Businesses use data scientists to the source, manage, and analyze large amounts of unstructured data with Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, big data tolls, SQL, machine learning
Our Data analysts bridge the gap between data scientists and business analysts and then help organize and analyze data to align with high-level business strategy with skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization.
Our Data engineers help customers manage exponential amounts of rapidly changing data with a focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying utilizing skills Java, Scala, NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)