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
Included with 
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
- Data Science
- Matplotlib
- Data Manipulation
- Histogram
- Data Cleansing
- Probability & Statistics
- Programming Principles
- Machine Learning Algorithms
- Python Programming
- Statistical Methods
- Data Visualization Software
- Applied Machine Learning
- Box Plots
- Statistical Analysis
- Statistical Inference
- NumPy
- Scripting
- Regression Analysis
- Scatter Plots
- Data Analysis
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
October 2025
Assessments
14 assignments
Taught in English
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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.
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- Gain a foundational understanding of a subject or tool
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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
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