Objectives of this course

The main goal of this course is to

  • Help students learn, understand, and practice data analytics and data mining techniques,
  • Understand the study of modern computing big data technologies and scaling up machine learning techniques focusing on industry applications.
  • Define the conceptualization and summarization of big data and machine learning and trivial data versus big data,
  • Elaborate computing technologies for statistical modelling like R and Python for machine learning techniques, and scaling up machine learning approaches.
  • Develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics.
  • Practice real life data problem analysis and decision-making.
  • Gain practical, hands-on experience with statistics programming languages and big data tools through coursework and applied research experiences.

Outcome of this course

After completion of this course students will be able to

  • Apply Statistical modeling and data analysis techniques to the solution of real-world business problems, communicate findings, and effectively present results using data visualization techniques.
  • Use new technologies in data science with the help of different software like SQL.
  • Apply ethical practices in data science activities and make well-reasoned ethical business and data management decisions.
  • Demonstrate knowledge of statistical data analysis techniques utilized in business decision making.
  • Apply principles of Data Science to the analysis of business problems.
  • Use data mining software like R and Python to solve real-world problems.
  • Students will demonstrate proficiency with statistical analysis of data.
  • Employ cutting edge tools and technologies to understand the big data problems.
  • Apply algorithms to build data mining techniques including supervised and unsupervised.