Mastering Python for Data Science

Mastering Python for Data Science

Instructor: Packt - Course Instructors

Access provided by Patient Advocacy Leadership Collective

12 modules

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

12 modules

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There are 12 modules in this course

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.

Through hands-on learning and guided projects, learners will gain the practical skills to clean, manipulate, and analyze data efficiently. The course also explores essential machine learning techniques and visualization tools, enabling professionals to uncover hidden patterns and derive actionable insights. Unlike typical theory-heavy programs, this course bridges the gap between academic knowledge and industry application. It combines conceptual clarity with real-world case studies that reflect how data science operates in business and technology environments. This course is ideal for Python developers, data analysts, and aspiring data scientists seeking to enhance their analytical capabilities. A basic understanding of Python and foundational data science concepts will help learners maximize the course benefits.

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.