The course will introduce data manipulation and cleaning techniques using the popular Python pandas data science library and introduce the abstraction of the Series and Data Frame as the central data structures for data analysis.
By enrolling in the Program in Data Science, you can gain valuable employment skills for in-demand positions in data science. This data science program is the perfect fit for students looking to advance their careers and benefit from a comprehensive, multidisciplinary approach.
Airtics Education has been approved by the Knowledge and Human Development Authority (KHDA), the regulatory and quality assurance body that oversees private education across Dubai. The KHDA regulates teachers’ curricula, inspects educational institutes, and, most importantly, makes sure that all the students of the United Arab Emirates are receiving the education they need. Airtics Education complies with all guidelines proposed by KHDA for approval. Airtics Education is well aligned with the UAE government’s Vision 2021 to develop a first-rate education system by promoting transformation through quality digital education.
This course is well suited for participants of all levels of experience because of the high demand for Data Science with Python programmers. Data Science with Python is beneficial for analytics professionals interested in Python, software and IT professionals interested in Analytics, as well as anyone with a genuine interest in Data Science
| Si. No. | Module name | Module Content | Learning Outcomes |
|---|---|---|---|
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Module 1 |
Python for Data Science |
Python basics
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L01. Learning python structure and how to write programs in it.
L02.Basic concepts of Python, its syntax, functions, and conditional statements. L03.Acquire the prerequisite Python skills to move into specific branches - Machine Learning, and Data Science. L04.Understand packages to enable them to write scripts for data manipulation and analysis |
|
Module 2 |
Mathematics and Statistics for Data Treatments
|
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L01-Introduce statistical tools for working with datasets
L02-Learn the essentials of probability and statistics for data analysis & visualization. L03-Know how to import and clean data using libraries like NumPy and Pandas for data exploration and analysis. L04-Learning to fix incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. |
|
Module 3 |
Data cleaning and pre-processing |
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L01.You will learn about training data, and how to use a set of data to discover potentially predictive relationships.
L02.Understand basic concepts and common tools used in machine learning.
L03. you will master machine learning techniques, including supervised and unsupervised learning and hands-on modelling, rounding out your artificial intelligence education. L04.Learn the basics of HTML/CSS, and Git version control system (VCS). L05.Build and Deploy a model to enable WebView. |
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Specialization Module 1 |
ML Deployment (Unsupervised Learning) |
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L01. Understanding the significance of carefully defining the problem before choosing a technique
L02. How to get data ready for unsupervised algorithms in particular L03. Options for integrating unsupervised models into the organization's decision-making process L04. How to innovatively blend supervised and unsupervised models for improved performance L05. Interpret and track your unsupervised models for ongoing development |
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Project/ Internship |
Capstone Project |
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