Learn Python Programming for Data Science
Master Python programming fundamentals for data science with hands-on practice using Jupyter Notebook. Perfect for beginners seeking essential coding skills.
Master Python programming fundamentals for data science with hands-on practice using Jupyter Notebook. Perfect for beginners seeking essential coding skills.
This comprehensive beginner-friendly course introduces fundamental programming concepts using Python, specifically designed for aspiring data scientists. Students learn essential programming skills through hands-on practice in Jupyter Notebook, covering data types, control structures, and functions. The course provides detailed explanations and practical exercises, making it ideal for those starting their programming journey.
4.1
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What you'll learn
Open Jupyter Notebook and use it to run Python code
Identify Python operators, data types and containers
Program control structures in Python including if statements and loops
Write Python functions that take input and return output
Use mathematical operators and basic calculations
Work with different data structures in Python
Skills you'll gain
This course includes:
30 Minutes PreRecorded video
3 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course provides a solid foundation in Python programming for data science applications. Students learn to use Jupyter Notebook for coding, understand fundamental Python concepts including data types, operators, and control structures, and develop practical programming skills through hands-on exercises. The curriculum emphasizes both theoretical understanding and practical application, preparing learners for more advanced data science courses.
First steps with Python
Module 1 · 1 Hours to complete
Data Types in Python
Module 2 · 2 Hours to complete
Control Structures and Functions
Module 3 · 4 Hours to complete
Fee Structure
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Instructors
Expert in Mathematical Modelling of Collective Human Behaviour
Dr. Jonathan Ward holds an MSci degree in Physics and Astrophysics from the University of Bristol, where he also completed his PhD in the Engineering Mathematics department. His research focuses on modelling collective human behaviour, leveraging his expertise in nonlinear dynamics, network science, graph theory, nonequilibrium statistical mechanics, Bayesian statistics, uncertainty quantification, and industrial applied mathematics. A Fellow of the Higher Education Academy, Dr. Ward has been teaching a variety of undergraduate and postgraduate mathematics modules at the University of Leeds since 2013, contributing significantly to the academic development of his students.
Lecturer in Statistics and Expert in Enumerative Combinatorics
Dr. Hassan Izanloo is a Lecturer in Statistics at the University of Leeds, specializing in Enumerative Combinatorics, Graph Theory, Probability and Statistics, and Machine Learning. He completed his undergraduate and master's degrees in Pure Mathematics in Iran before transitioning to a research role and spending three years as a mathematics lecturer and module leader at Parand Islamic Azad University in Tehran. He earned his PhD in August 2019 under the guidance of Professor Roger Behrend and served as a teaching associate in Statistical Sciences at the University of Bristol from October 2019 until June 2023. Joining the University of Leeds in October 2023, Dr. Izanloo is also the module leader for OMAT5200M. His research interests focus on various aspects of enumerative combinatorics, including alternating sign matrices, polytopes, partially ordered sets, and graphs. Dr. Izanloo is a qualified Teaching Fellow in Higher Education and is a member of the London Mathematical Society (LMS).
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4.1 course rating
17 ratings
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