Pearson
Data Science Fundamentals, Part 1 Specialization

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Pearson

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.

Pearson

Instructor: Pearson

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Get in-depth knowledge of a subject
Beginner level

Recommended experience

4 weeks to complete
at 5 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Beginner level

Recommended experience

4 weeks to complete
at 5 hours a week
Flexible schedule
Learn at your own pace

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.

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Taught in English
Recently updated!

August 2025

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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: NumPy
Category: Programming Principles
Category: Data Manipulation
Category: Data Analysis
Category: Exploratory Data Analysis
Category: Scikit Learn (Machine Learning Library)
Category: Pandas (Python Package)
Category: Machine Learning
Category: Python Programming
Category: Applied Machine Learning
Category: Data Science
Category: Computational Thinking

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: Web Scraping
Category: JSON
Category: Data Transformation
Category: Extract, Transform, Load
Category: Data Integration
Category: Extensible Markup Language (XML)
Category: Data Pipelines
Category: Relational Databases
Category: Data Processing
Category: Object Oriented Programming (OOP)
Category: Application Programming Interface (API)
Category: Data Modeling

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 Cleansing
Category: Exploratory Data Analysis
Category: Data Manipulation
Category: Relational Databases
Category: Data Transformation
Category: SQL
Category: Extract, Transform, Load
Category: Data Quality
Category: Database Management
Category: Descriptive Statistics
Category: Data Validation
Category: Databases
Category: Pandas (Python Package)
Category: Data Integrity
Category: Data Pipelines
Category: Data Storage Technologies
Category: Object-Relational Mapping
Category: Data Processing
Category: Data Analysis

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Instructor

Pearson
Pearson
221 Courses2,129 learners

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Pearson

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