Course Introduction
This course, titled Python for Beginners: Data Tools, is a comprehensive training program designed to introduce learners to essential Python-based data analysis tools. The course explores industry-standard libraries such as Jupyter Notebooks, Anaconda, pandas, NumPy, Matplotlib, and scikit-learn, enabling learners to understand, manipulate, and visualize datasets effectively. Throughout the course, students will learn how to handle data structures, prepare datasets, perform basic machine-learning tasks, and conduct exploratory data analysis using practical, hands-on demonstrations. It is tailored for beginners, aspiring data analysts, and learners seeking a strong foundation in Python-based data science workflows.
Course Duration and Modules
The total duration of this course is based on the combined length of all 31 instructional videos, providing approximately 3 hours and 50 minutes of guided learning content.
The course is structured into 31 modules, each corresponding to a short, focused video lesson that gradually builds your skills.
Overall Duration
4 weeks (recommended)
Total Learning Hours
Total: 20 learning hours
• 4 hours video content
• 16 hours hands-on practice & independent study
Weekly Commitment
Learners are encouraged to commit 4–6 hours per week to complete the course comfortably.
Pacing
This is a self-paced course and can be completed within 4–6 weeks, depending on individual study habits.
Modules and Video Lecture Titles
(All video titles exactly as provided appear in the "Course Videos" section below.)
Course Presenter
The course is presented by Microsoft Developer, a trusted global authority in technical education and programming instruction. With extensive expertise in Python, data science, cloud technologies, and software engineering, Microsoft Developer produces high-quality tutorials known for their clarity, practical demonstrations, and structured teaching approach. Their teaching style emphasizes real-world application, step-by-step explanations, and hands-on guidance, making complex concepts easy to understand for learners at all levels.
Course Certificate
Upon successful completion of this course, learners will receive the Qalam Scholar International Certificate. This certificate is globally recognized and includes a barcode verification system, ensuring authenticity and professional credibility. The certificate enhances your employability and supports both national and international career opportunities in programming, data analysis, and technology-related fields.
Learning Objectives
By the end of this course, students will be able to:
· Understand the purpose and use of Jupyter Notebooks in data science workflows.
· Install, manage, and operate Python environments using Anaconda.
· Load, manipulate, and analyze datasets using the pandas DataFrame structure.
· Handle missing, duplicated, and inconsistent data efficiently.
· Perform basic machine-learning operations using scikit-learn.
· Visualize datasets with NumPy arrays and Matplotlib charts.