Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
Data Science Fundamentals, Part 1 Specialization
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Data Science Fundamentals, Part 1 Specialization
Basic Concepts, Data Wrangling, Databases--Python. Gain hands-on experience in real-world data acquisition, parsing, and ML applications.
Instructor: Pearson
Included with 
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 5 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 5 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Acquire, clean, and structure real-world data from diverse sources using Python, APIs, and relational databases.
Analyze, visualize, and model data using industry-standard libraries such as Pandas, NumPy, SciPy, Statsmodels, and Scikit-learn.
Build, validate, and deploy machine learning models, applying best practices in data science to solve practical, real-world problems.
Overview
Skills you'll gain
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
August 2025
7 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Pearson
Specialization - 3 course series
What you'll learn
Develop a strong foundation in data science concepts, theory, and the practical application of Python’s data ecosystem.
Acquire, manipulate, and analyze real-world datasets using industry-standard tools and libraries.
Build and evaluate machine learning models, including recommendation engines, with hands-on projects.
Master the end-to-end data science process, from data acquisition to visualization and effective communication of results.
Skills you'll gain
Category: Python Programming
Category: Data Manipulation
Category: Data Science
Category: Data Analysis
Category: NumPy
Category: Pandas (Python Package)
Category: Machine Learning
Category: Scikit Learn (Machine Learning Library)
Category: Applied Machine Learning
Category: Programming Principles
Category: Computational Thinking
Category: Exploratory Data Analysis
What you'll learn
Master the ETL (Extract, Transform, Load) process for seamless data acquisition and integration.
Acquire practical skills in sourcing data from APIs, web scraping, and managing data lineage.
Parse and transform diverse data formats (XML, JSON) for structured analysis.
Build and apply data models using object-oriented programming to streamline data workflows.
Skills you'll gain
Category: Data Transformation
Category: Extensible Markup Language (XML)
Category: Extract, Transform, Load
Category: Data Pipelines
Category: Relational Databases
Category: JSON
Category: Data Processing
Category: Data Modeling
Category: Object Oriented Programming (OOP)
Category: Application Programming Interface (API)
Category: Data Integration
Category: Web Scraping
What you'll learn
Master the fundamentals of relational databases and persistent data storage.
Build and optimize ETL pipelines using Python and object-relational mappers.
Apply data validation techniques to ensure data quality and integrity.
Utilize Pandas for effective data exploration, transformation, and statistical analysis.
Skills you'll gain
Category: Data Transformation
Category: Data Validation
Category: Extract, Transform, Load
Category: Descriptive Statistics
Category: Pandas (Python Package)
Category: Object-Relational Mapping
Category: Data Storage Technologies
Category: Relational Databases
Category: Data Manipulation
Category: Data Cleansing
Category: Data Integrity
Category: Data Processing
Category: Data Quality
Category: Database Management
Category: SQL
Category: Exploratory Data Analysis
Category: Databases
Category: Data Analysis
Category: Data Pipelines
Earn a career certificate
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Frequently asked questions
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
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