Data Science with Python: Analyze & Visualize

This course is part of Python for Data Science: Real Projects & Analytics Specialization

Instructor: EDUCBA

Access provided by Talisis

4 modules

Gain insight into a topic and learn the fundamentals.

15 reviews

8 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

15 reviews

8 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

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

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

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.

This course benefits anyone aiming to advance a career in data science, analytics, or business intelligence, providing practical, project-based learning experiences. Unlike generic tutorials, this program integrates Python foundations with real-world statistical methods, Bayesian inference, and applied machine learning workflows. The structured approach—spanning Python basics to advanced analysis—ensures learners can confidently interpret data, validate assumptions, and present findings with clarity.

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 assignments

7 videos Total 37 minutes

Introduction to Data Visualization 4 minutes
Understanding Data Science 5 minutes
Python Environment Framework 4 minutes
Various Python Scripts 7 minutes
Concept of Advanced Python 7 minutes
Creating Functions for Python 5 minutes
Creating a New Library 5 minutes

3 assignments Total 50 minutes

Getting Started with Data Science 10 minutes
Mastering Python Essentials 10 minutes
Graded-Python Foundations for Data Science 30 minutes

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

6 videos Total 45 minutes

Creating Bar Charts 9 minutes
Analysis on Line Chart 6 minutes
Understanding Scattered Plots 7 minutes
Vector Spaces in Linear Algebra 7 minutes
Matrices in Linear Algebra 11 minutes
Analysing Statistical Data 6 minutes

3 assignments Total 50 minutes

Building Basic Visualizations 10 minutes
Advanced Plotting and Graphs 10 minutes
Graded-Data Visualization Techniques 30 minutes

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

11 videos Total 71 minutes

Understanding Central Tendencies 11 minutes
Dispersion for Data 6 minutes
Probability in Discreet Mathematics 3 minutes
Normal Distribution Curve 7 minutes
Example for Normal Distribution Curve 5 minutes
Central Limit Theorem 7 minutes
Concept of Hypothesis 3 minutes
Example on Hypothesis Testing 8 minutes
Defining the Next Value 9 minutes
Principle of P Hacking 5 minutes
Understanding Bayesian Inference 6 minutes

4 assignments Total 60 minutes

Exploring Statistical Foundations 10 minutes
Distribution and Hypothesis Testing 10 minutes
Advanced Statistical Insights 10 minutes
Graded-Statistics and Probability in Action 30 minutes

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

12 videos Total 76 minutes

Line of Best Fit 6 minutes
Datascience with Gradient Descent 3 minutes
Example on Gradient Descent 6 minutes
Value Import 8 minutes
Output Functions for Gradient 7 minutes
Working with Data Analysis 9 minutes
Creating Normal Histogram 8 minutes
Two Dimensional Graph 7 minutes
Multiple Scatter Plots 7 minutes
Analyzing Data Sets 6 minutes
Learnig Box Plots 7 minutes
Overview and Conclusion 3 minutes

4 assignments Total 60 minutes

Regression and Optimization 10 minutes
Practical Data Analysis (Part 1) 10 minutes
Practical Data Analysis (Part 2) 10 minutes
Graded-Machine Learning and Applied Data Analysis 30 minutes

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Instructor

EDUCBA

1,027 Courses 227,853 learners

Offered by

EDUCBA

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Reviewed on Jan 7, 2026

This course offers an excellent balance between Python programming, statistics, and machine learning. The real-world examples make the learning experience highly practical.

Reviewed on Jan 9, 2026

Reviewed on Jan 11, 2026

This course offers an excellent balance between Python programming statistics, and machine learning. The real world examples make the learning experiences highly practical.

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