- 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.
Objectives of this course
The main goal of this course is to
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.