Data Science has become one of the most sought-after fields in today’s data-driven world, and Python stands at its core. This course empowers learners to master the art of data science using Python—one of the most versatile programming languages for analyzing, visualizing, and interpreting data.
Mastering Python for Data Science
Mastering Python for Data Science
Instructor: Packt - Course Instructors
Access provided by Chula Engineering
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Manage data and perform linear algebra in Python
Derive inferences using inferential statistics
Create data visualizations and mine for patterns
Skills you'll gain
- Pandas (Python Package)
- Object Oriented Programming (OOP)
- Python Programming
- Data Visualization
- JavaScript Frameworks
- Apache Hadoop
- Statistics
- Data Analysis
- Data Manipulation
- Analytics
- HTML and CSS
- Probability & Statistics
- Regression Analysis
- Big Data
- Statistical Inference
- Random Forest Algorithm
- Data Science
- Data Mapping
- Machine Learning
- Data Preprocessing
- Skills section collapsed. Showing 10 of 20 skills.
Details to know
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Assessments
12 assignments
Taught in English
Recently updated!
February 2026
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There are 12 modules in this course
In this section, we explore parsing raw data from multiple sources, cleaning datasets, and manipulating data using NumPy and pandas for effective analysis.
What's included
2 videos5 readings1 assignment
In this section, we explore probability distributions, hypothesis testing, confidence intervals, and errors to make population inferences from sample data using statistical methods.
What's included
1 video6 readings1 assignment
In this section, we explore structured data mining techniques, domain-driven analysis, and pattern discovery to uncover actionable insights for informed decision-making in real-world scenarios.
What's included
1 video3 readings1 assignment
In this section, we explore techniques for controlling plot properties, combining multiple visualizations, and creating advanced data displays using Python. These methods enhance data communication and insight extraction.
What's included
1 video4 readings1 assignment
In this section, we explore supervised, unsupervised, and reinforcement learning, focusing on their applications, key concepts like feature vectors, and practical problem-solving in data-driven systems.
What's included
1 video3 readings1 assignment
In this section, we explore simple and multiple linear regression models, focusing on variable relationships, correlation coefficients, and model training for predictive analysis.
What's included
1 video3 readings1 assignment
In this section, we build and evaluate logistic regression models using statsmodels and SciKit, focusing on predicting event likelihood with the Titanic dataset and assessing performance via ROC curves.
What's included
1 video3 readings1 assignment
In this section, we explore user-based and item-based collaborative filtering techniques, focusing on calculating similarity using Euclidean distance and generating recommendations through weighted averages.
What's included
1 video3 readings1 assignment
In this section, we explore random forest models for classification, analyze census data to predict income levels, and evaluate model performance using accuracy metrics.
What's included
1 video2 readings1 assignment
In this section, we explore k-means clustering for customer segmentation, focusing on determining optimal clusters and interpreting results for business insights.
What's included
1 video3 readings1 assignment
In this section, we preprocess text data using NLTK, generate wordclouds, and apply tokenization, POS tagging, and named entity recognition to extract insights from unstructured data.
What's included
1 video3 readings1 assignment
In this section, we explore Python's role in big data processing, focusing on Hadoop, MapReduce, and distributed computing techniques for efficient data analysis.
What's included
1 video5 readings1 assignment
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