By completing this course, learners will be able to apply Python programming to analyze datasets, construct compelling visualizations, evaluate statistical measures, and implement machine learning techniques to generate actionable insights. You will develop hands-on skills in Python scripting, create reusable libraries, build functions, and preprocess data for accurate analysis. Learners will also construct charts, scatter plots, histograms, and box plots, evaluate probabilities and hypotheses, and implement regression and optimization models using gradient descent.
Data Science with Python: Analyze & Visualize
Data Science with Python: Analyze & Visualize
This course is part of Python for Data Science: Real Projects & Analytics Specialization
Instructor: EDUCBA
Access provided by CENTRO DE GRADUADOS DE LA FIUBA
Gain insight into a topic and learn the fundamentals.
7 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Analyze datasets with Python scripting, functions, and libraries.
Visualize data using charts, scatter plots, histograms, and box plots.
Apply ML techniques like regression and gradient descent models.
Skills you'll gain
- Scatter Plots
- Statistical Inference
- Data Visualization Software
- Scripting
- Data Manipulation
- Python Programming
- Box Plots
- Regression Analysis
- Matplotlib
- Data Cleansing
- Histogram
- Machine Learning Algorithms
- Probability & Statistics
- Data Analysis
- Applied Machine Learning
- Statistical Analysis
- NumPy
- Programming Principles
- Statistical Methods
- Data Science
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
14 assignments
Taught in English
Recently updated!
October 2025
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the Python for Data Science: Real Projects & Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 4 modules in this course
This module introduces learners to the core principles of Python programming and its application in data science. Students will explore the Python environment, understand essential coding structures, and build reusable functions and libraries. By the end of this module, learners will have the programming foundation necessary to analyze, process, and manipulate data effectively.
What's included
7 videos3 assignments1 plugin
This module focuses on data visualization methods for effective data storytelling. Learners will develop skills in creating charts, graphs, and scatter plots while exploring the mathematical foundations of vector spaces and matrices. By mastering these visualization tools, students will be able to present data insights clearly and persuasively.
What's included
6 videos3 assignments
This module provides a deep dive into the statistical foundations of data science. Learners will explore measures of central tendency, variability, probability, and hypothesis testing while addressing advanced concepts such as the Central Limit Theorem, Bayesian inference, and p-hacking. These skills prepare students to evaluate datasets critically and draw reliable conclusions.
What's included
11 videos4 assignments
This module introduces learners to regression, optimization, and applied data analysis techniques. Students will implement gradient descent, preprocess datasets, and apply visual tools such as histograms, scatter plots, and box plots to extract insights. The module concludes with practical applications and a summary of the entire learning journey.
What's included
12 videos4 assignments
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Explore more from Data Science
Simplilearn
DeepLearning.AI
Duke University
