Data Science

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Data Science

Machine Learning With machine learning and deep learning, organizations can develop insights and intelligence directly to help grow their business drastically. The demand for machine learning is upsurging. Businesses need and are progressively using machine learning techniques to sustain advanced analytics across a range of continuous industry and market expansions.We at Technova Softwares, assist enterprises with machine learning solutions that fetch a hidden value from enterprise data.

We work with real-time data in engineering and manufacturing, social-media domain more precisely anticipate business and customer needs. We help business and data experts collaborate across multiple domains and make improved decisions, resolve problems and with a data-driven approach.

Prerequisites
Basic knowledge of statistics, linear algebra would be additional plus. The course has facultative status.
Aims
– To develop practical data analysis skills, which can be applied to practical problems.
– To develop fundamental knowledge of concepts underlying data science projects.
– To develop practical skills needed in modern analytics.
– To explain how math and information sciences can contribute to building better algorithms
and software.
– To give a hands-on experience with real-world data analysis.
– To develop applied experience with data science software, programming, applications and processes.

Planning & strategy

This course is aimed at providing our students with a solid DS training, which could boost their careers in one of TOP10 mostly required professions in the world. The course is based the most recent DS tools and developments, brought to the students from the author working experience as a director of DS research department in several IT companies.

Safety net & build wealth

  • Medicine and Health Sector
  • Finance Sector
  • Manufacturing Sector
  • Social Network
  • Prognostics and Telematics
  • Environment Science

Course Outcomes

The main outcome of this class is to train a student to do practical DS work. Career-wise, we expect our students to be able to develop into skilled DS researchers or software developers. After completing the study of the discipline IDS the student should:
• Know basic notions and definitions in data analysis, machine learning.
• Know standard methods of data analysis and information retrieval
• Be able to formulate the problem of knowledge extraction as combinations of data filtration, analysis and exploration methods.
• Be able to translate a real-world problem into mathematical terms.
• Possess main definitions of subject field.
• Possess main software and development tools of data scientist.
• Learn to develop complex analytical reasoning.