Data Analytics Fundamentals


This course provides a comprehensive and unified view of data analytics fundamentals. Here students will able to understand four functional facets of data analytics which are descriptive, diagnostic, predictive, and prescriptive. The demand for data science and learning science skills has continued to increase as classrooms, labs, and organizations look to optimize their data and improve learning environments for students and employees. In this fundamental course, you will develop a solid understanding of fundamental learning analytics theories and processes, and explore different types of educational data. You will gain experience working with educational data sets and the R programming language, and hear from a diverse set of voices in the field. Finally, you will also consider ethics and privacy issues, as well explore how to work as part of a team in a domain that is becoming increasingly cross-disciplinary. By grasping these fundamental areas, you will have a better understanding of the field of learning analytics and be able to apply skills to any occupation that utilizes educational data.

Course Objectives

  • The field of learning analytics and explore how data and information are used
  • Common learning analytics methods and approaches, such as data wrangling and cleaning, structure discovery, and basic prediction modeling
  • How to conduct basic data wrangling and analyses
  • Ethics and privacy considerations
  • Working in a collaborative, cross-disciplinary setting
  • Common toolsets used

Artificial Intelligence Fundamentals

Artificial Intelligence (AI) is a field that has a long and storied history but is still growing and solving complex real world problems. In this course, you will learn the basics of AI and its applications in all businesses and industries. Along the way, we will also discuss about its numerous applications to get an insight into how AI is being applied to our everyday problems. You will learn about the history of AI, its impact on everyday life, its use in businesses across industries, and more.

Course Objectives

  • Introduction to Artificial Intelligence
  • Intelligent agents
  • History of Artificial Intelligence
  • Building intelligent agents (search, games, logic, constraint satisfaction problems)
  • Machine Learning algorithms
  • Applications of AI (Natural Language Processing, Robotics/Vision)
  • Solving real AI problems through programming with Python

Machine Learning Fundamentals

Over the last one decade, Machine Learning has completely changed the way we view the world around us. But what is Machine Learning? It is the science of having computers to act intelligently without being explicitly programmed. Machine Learning has given us driver-less cars, speech recognition in every appliance and products we use at our home, better search engines and more. Machine learning has become so ingrained in our lives that we do not even know that we are using in many ways than we could imagine. This course will enable you to learn about the most effective machine learning techniques and gain practice by implementing and working with them. More importantly, you will understand not only the theoretical underpinnings of machine learning, but also gain practical know-how needed to quickly and successfully apply these techniques to new problems. Finally, you will learn about some of best practices in innovation in machine learning This course will introduce you to machine learning, datamining, and statistical pattern recognition. You will also learn from numerous case studies and applications during the duration of this course, besides learning to apply algorithms for building smart gadgets and machines.

Course Objectives

  • Understanding Machine Learning
  • Linear Regression with One Variable
  • Linear Algebra
  • Linear Regression with Multiple Variables
  • Octave/Matlab
  • Logistic Regression
  • Regularization
  • Machine Learning System Design
  • Vector Machines
  • Unsupervised Learning
  • Anomaly Detection
  • Recommender Systems
  • Large Scale Machine Learning
  • Application Example: Photo OCR