Astron Hosts Courses

Course Description

Our Data Science course is designed to take you from the basics to advanced, real-world data analysis and modeling. You will learn how to collect, clean, analyze, and visualize data using Python and essential data science tools.

This course focuses on hands-on projects, data manipulation, exploratory data analysis, statistics, machine learning fundamentals, and working with real-world datasets. Whether your goal is to become a data scientist, data analyst, or work with data-driven decision making, this course provides practical skills and industry best practices.

By the end of the course, you will be able to analyze datasets, build predictive models, and present data insights confidently using modern data science techniques.

What you'll learn

Python programming for data science
Data cleaning and preprocessing techniques
Exploratory data analysis and visualization

Statistical analysis and data interpretation
Machine learning fundamentals and models
Building, evaluating, and deploying data-driven solutions

This course includes:

Hands-on practical training
Assignments
Real-world projects

Internship opportunity on completion
Career guidance & support
Certificate of completion

Course Content

Introduction to Data Science, types of data, Python overview, and environment setup using Anaconda and Jupyter Notebook.
Week 1
Python fundamentals including variables, data types, operators, loops, functions, and basic problem-solving
Week 2
Python data structures, file handling with CSV/Excel, and NumPy basics for numerical computing.
Week 3
Pandas basics, data cleaning and preprocessing, and a mini data analysis project.
Week 4

Data cleaning techniques, handling missing values, data transformation, and feature scaling basics.
Week 5
Exploratory Data Analysis (EDA), descriptive statistics, correlation, and pattern identification.
Week 6
Data visualization using Matplotlib, creating charts, and visualization best practices..
Week 7
Advanced visualization with Seaborn, basic statistics, and a mini EDA project.
Week 8

Introduction to Machine Learning, its types, applications, and overall workflow.
Week 9
Supervised learning with Linear and Logistic Regression, model training, and evaluation.
Week 10
Classification algorithms including KNN and Decision Trees with performance metrics.
Week 11
Unsupervised learning with K-Means clustering and final project presentation.
Week 12

Requirements

Basic computer knowledge
Understanding of programming concepts (preferred but not mandatory)
No prior python experience required

Student Feedback

4.9

Total 20 Rating

5 star 80%

4 star 75%

3 star 40%

2 star 20%

1 star 0%

Review

Ayesha Khan
(5) Reviewed on Jan 12, 2025

This Data Science course helped me understand Python, data analysis, and visualization from scratch. Everything was explained very clearly.
Bilal Ahmed
(5) Reviewed on Feb 3, 2025

The practical datasets and hands-on exercises really helped me learn Pandas, NumPy, and real data analysis techniques.
Faizan Ali
(5) Reviewed on Mar 18, 2025

I really liked the machine learning part. Concepts like regression and classification were explained in a simple and practical way.
Sana Raza
(5) Reviewed on Apr 9, 2025

The instructor made statistics and data visualization very easy to understand. I feel confident analyzing data now.
Usman Tariq
(5) Reviewed on May 27, 2025

This course is perfect for beginners. The step-by-step approach made Data Science easy and enjoyable.
Hina Yousuf
(5) Reviewed on Jun 14, 2025

From data cleaning to machine learning basics, everything was covered properly. Highly recommended for students.
Faisal Mehmood
(5) Reviewed on Jul 6, 2025

A well-structured Data Science course with real-world examples. It helped me build confidence in data analysis.