Data Science in Real Life is an immersive course designed to provide students with a comprehensive understanding of the field of Data Science and its practical applications. In this course, students will delve into the truth behind Data Science, gaining insights from real practitioners who will share their personal experiences and stories about working in this dynamic and highly sought-after field.
The course begins by demystifying Data Science, examining its core concepts, methodologies, and tools. Students will explore the fundamental principles and techniques used to extract valuable insights from vast amounts of data. Through a combination of theoretical lectures, hands-on exercises, and real-world case studies, students will develop a solid foundation in the key components of Data Science.
The course places a strong emphasis on the practical aspects of Data Science, focusing on real-life applications and use cases. Students will gain exposure to a wide range of industries where Data Science is actively utilized, such as finance, healthcare, marketing, and social media. They will learn how Data Science is applied to solve complex problems, make informed business decisions, and drive innovation in various domains.
Statistics is the science of assigning a probability to an event based on experiments. It is the application of quantitative principles to the collection, analysis, and presentation of numerical data. Students will learn the fundamentals of Data Science, statistics, and Machine Learning with this course. It will enable students to define statistics and essential terms related to it, explain central tendency and dispersion measures, and comprehend skewness, correlation, regression, and distribution. Students will be able to make data-driven predictions through statistical inference.
Introduction
to Artificial Intelligence course is designed to help learners decode
the mystery of Artificial Intelligence and understand its business
applications. The course provides an overview of Artificial Intelligence
concepts and workflows, Machine Learning, Deep Learning, and
performance metrics. Students learn the difference between supervised,
unsupervised, and reinforcement learning-be exposed to use cases, and
see how clustering and classification algorithms help identify
Artificial Intelligence business applications.
Python for Data Science is a comprehensive course designed to equip students with the essential programming skills and techniques required to effectively manipulate and analyze data using Python. This course is specifically tailored for individuals interested in entering the field of data science or those who wish to enhance their data analysis capabilities through the power of Python.
Throughout the course, students will learn the fundamentals of the Python programming language and explore its wide range of libraries and tools specifically designed for data science tasks. By combining theoretical explanations, hands-on coding exercises, and real-world examples, students will gain practical experience in using Python to tackle various data analysis challenges.