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Data Science with Python: Analyze & Visualize

EDUCBA via Coursera

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Overview

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Master the essential skills of data science with Python by learning how to analyze data, create meaningful visualizations, apply statistical methods, and implement foundational machine learning techniques. This course takes you through a structured learning journey, beginning with Python programming fundamentals and progressing to data visualization, statistical analysis, probability, hypothesis testing, Bayesian inference, regression, gradient descent, and practical data analysis. Designed for aspiring data scientists, data analysts, business intelligence professionals, and anyone looking to strengthen their analytical skills, this course combines programming with practical data science workflows. You will learn to build reusable Python functions and libraries, preprocess datasets, create charts, line graphs, scatter plots, histograms, and box plots, evaluate statistical measures and data distributions, and apply regression models to generate reliable insights. What makes this course unique is its integrated approach, connecting Python programming, visualization, statistics, and machine learning into a single learning path. Rather than learning these topics in isolation, you will develop the ability to interpret data, validate assumptions using statistical techniques, and communicate findings through effective visualizations. By the end of the course, you will be able to analyse datasets with confidence, implement data-driven workflows, and apply Python-based analytical techniques to solve real-world data challenges.

Syllabus

Python Foundations for Data Science

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.

Data Visualization Techniques

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.

Statistics and Probability in Action

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.

Machine Learning and Applied Data Analysis

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.