# Data Science Fundamentals, Part 2 Review (2026): 7.8/10 · Coursera

> Independent review of Data Science Fundamentals, Part 2 on Coursera. Rated 7.8/10 by our editorial team. Pros, cons, price, and top alternatives. Certificate…

Data Science Fundamentals, Part 2

![Data Science Fundamentals, Part 2](/api/media/file/hero/data-science-fundamentals-part-2-course.webp?v=2?width=800)

# Data Science Fundamentals, Part 2 Course — Review (7.8/10)

This course delivers practical data science skills through Python-based projects and real-world case studies. It effectively bridges theory and application, though some learners may find the pace chal...

Explore This Course

🎟️ Coursera Discount Offer

Explore This Course

Data Science Fundamentals, Part 2 is a 14 weeks online intermediate-level course on Coursera by Pearson that covers data science. This course delivers practical data science skills through Python-based projects and real-world case studies. It effectively bridges theory and application, though some learners may find the pace challenging. The content is relevant and well-structured, but additional support materials would enhance accessibility. A solid choice for those looking to build confidence in data analysis and modeling. We rate it 7.8/10.

## Prerequisites

Basic familiarity with data science fundamentals is recommended. An introductory course or some practical experience will help you get the most value.

## Pros

- Strong emphasis on hands-on Python programming with pandas and scikit-learn

- Real-world case studies enhance practical understanding of data problems

- Covers full data workflow from acquisition to insight communication

- Teaches essential visualization techniques for effective storytelling

## Cons

- Limited beginner support; assumes prior Python knowledge

- Some topics covered too broadly for deep mastery

- Peer-reviewed assignments may lack timely feedback

## Data Science Fundamentals, Part 2 Course Review

Platform: Coursera

Instructor: Pearson

Updated May 3, 2026·Editorial Standards·How We Rate

## What will you learn in Data Science Fundamentals, Part 2 course

- Master core data science concepts and theoretical foundations through applied learning

- Acquire and clean real-world datasets using pandas and NumPy for reliable analysis

- Apply statistical methods with SciPy to uncover patterns and validate hypotheses

- Build and evaluate machine learning models using scikit-learn on diverse datasets

- Create compelling data visualizations to communicate findings clearly and effectively

### Program Overview

### Module 1: Data Acquisition and Cleaning

4 weeks

- Web scraping and API integration

- Handling missing data and outliers

- Structuring datasets with pandas

### Module 2: Exploratory Data Analysis

3 weeks

- Data visualization with Matplotlib and Seaborn

- Statistical summaries and distributions

- Identifying correlations and anomalies

### Module 3: Machine Learning Foundations

5 weeks

- Supervised learning: regression and classification

- Unsupervised learning: clustering and dimensionality reduction

- Model evaluation and hyperparameter tuning

### Module 4: Communicating Insights

2 weeks

- Storytelling with data

- Visualization best practices

- Presenting findings to stakeholders

### Get certificate

#### Job Outlook

- High demand for data scientists across tech, finance, and healthcare sectors

- Strong career growth potential with foundational and applied skills

- Relevant experience for roles in analytics, machine learning, and business intelligence

## Editorial Take

Building on foundational knowledge, Data Science Fundamentals, Part 2 offers a rigorous, application-focused path into core data science practices. This Coursera specialization by Pearson targets learners ready to move beyond basics into real analysis and modeling.

### Standout Strengths

- Hands-On Python Integration: The course embeds pandas, NumPy, and scikit-learn throughout, ensuring learners gain fluency in tools used by professionals. Code exercises are structured to mirror real data workflows.

- End-to-End Data Workflow: From data scraping to storytelling, the curriculum covers the full pipeline. This holistic approach helps learners see how stages interconnect in actual projects.

- Case Study-Driven Learning: Real-world scenarios ground abstract concepts in tangible problems. This method strengthens analytical thinking and decision-making in messy, imperfect datasets.

- Machine Learning Application: Supervised and unsupervised models are introduced with practical implementation. Learners gain confidence in selecting, training, and evaluating algorithms.

- Visualization and Communication: Emphasis on Matplotlib and Seaborn ensures learners can present insights clearly. This skill is often overlooked but critical for career success.

- Industry Alignment: Tools and techniques reflect current standards in data science. Completing the course builds a portfolio relevant to job requirements in analytics and ML roles.

### Honest Limitations

- Assumed Programming Proficiency: The course presumes familiarity with Python. Beginners may struggle without prior coding experience, limiting accessibility despite its intermediate label.

- Surface-Level Topic Coverage: Some complex areas like hyperparameter tuning are introduced but not deeply explored. Learners seeking mastery may need supplementary resources.

- Inconsistent Feedback Mechanisms: Peer-graded assignments can suffer from delays or variable quality in reviews. This affects learning continuity and motivation.

- Limited Theoretical Depth: While applied focus is a strength, deeper statistical theory is sometimes glossed over. This may leave gaps for learners aiming at research or advanced roles.

### How to Get the Most Out of It

- Study cadence: Dedicate 6–8 hours weekly with consistent scheduling. Spacing sessions improves retention and allows time for debugging code exercises effectively.

- Apply each module’s techniques to a personal dataset. Reinforces learning and builds a practical portfolio beyond course assignments.

- Note-taking: Document code snippets, errors, and solutions. A well-organized digital notebook aids future reference and interview preparation.

- Community: Engage in Coursera forums and study groups. Peer discussions clarify doubts and expose you to diverse problem-solving approaches.

- Practice: Re-run labs with modified parameters or datasets. Experimentation deepens understanding of model behavior and data patterns.

- Consistency: Complete assignments promptly to maintain momentum. Falling behind can make catching up difficult due to cumulative concepts.

### Supplementary Resources

- Book: "Python for Data Analysis" by Wes McKinney complements pandas instruction with deeper examples and best practices.

- Tool: Jupyter Notebook extensions like nbextensions improve workflow efficiency and code organization during practice sessions.

- Follow-up: Enroll in a dedicated machine learning specialization to deepen algorithmic knowledge after this foundational course.

- Reference: scikit-learn’s official documentation provides detailed guides and examples that extend beyond course material.

### Common Pitfalls

- Pitfall: Skipping data cleaning steps to rush into modeling. This leads to inaccurate results and poor model performance, undermining learning outcomes.

- Pitfall: Copying code without understanding logic. This hampers skill development and makes troubleshooting future projects more difficult.

- Pitfall: Ignoring visualization principles. Poor charts reduce credibility and fail to communicate insights, even with strong analysis.

### Time & Money ROI

- Time: At 14 weeks, the investment is substantial but reasonable for the skill depth. Most learners complete it part-time without burnout.

- Cost-to-value: The paid model offers structured learning and certification, though self-study alternatives exist. Value depends on learner goals and need for credentials.

- Certificate: The specialization certificate enhances resumes, particularly for career switchers. It signals applied competence to employers.

- Alternative: Free tutorials may cover similar tools, but lack integration, feedback, and credentialing that justify the course cost for many.

### Editorial Verdict

Data Science Fundamentals, Part 2 stands out for its applied structure and use of industry-standard tools. It successfully transitions learners from theory to practice, making it a strong choice for those with some Python background looking to formalize their data science skills. The integration of real-world case studies ensures that knowledge is not just academic but immediately applicable. While not perfect, its focus on the complete data workflow—from cleaning to communication—prepares learners for real challenges in analytics roles.

That said, the course isn’t ideal for absolute beginners or those seeking deep theoretical grounding. Its intermediate pacing and limited explanatory depth in advanced topics mean self-directed learners must supplement material independently. Still, for the right audience—career-focused individuals wanting hands-on experience with Python and machine learning—it delivers solid value. We recommend it as a stepping stone in a broader learning path, especially when paired with additional projects and community engagement. The certificate adds professional credibility, making it worth the investment for those transitioning into data roles.

## How Data Science Fundamentals, Part 2 Compares

| Course | Platform | Rating | Level | Duration |

| --- | --- | --- | --- | --- |

| Data Science Fundamentals, Part 2 | Coursera | 7.8/10 | Intermediate | 14 weeks |

| PowerBI Zero to Hero Course | Udemy | 9.7/10 | N/A | N/A |

| Complete MLOps Bootcamp With 10+ End To End ML Projects Course | Udemy | 9.7/10 | N/A | N/A |

| LLM Engineering: Master AI, Large Language Models & Agents Course | Udemy | 9.7/10 | N/A | N/A |

## Who Should Take Data Science Fundamentals, Part 2?

This course is best suited for learners with foundational knowledge in data science and want to deepen their expertise. Working professionals looking to upskill or transition into more specialized roles will find the most value here. The course is offered by Pearson on Coursera, combining institutional credibility with the flexibility of online learning. Upon completion, you will receive a specialization certificate that you can add to your LinkedIn profile and resume, signaling your verified skills to potential employers.

If you are exploring adjacent fields, you might also consider courses in Agile & Scrum Courses, AI Courses, Arts and Humanities Courses, which complement the skills covered in this course.

### Career Outcomes

- Apply data science skills to real-world projects and job responsibilities

- Advance to mid-level roles requiring data science proficiency

- Take on more complex projects with confidence

- Add a specialization certificate credential to your LinkedIn and resume

- Continue learning with advanced courses and specializations in the field

## More Data Science Courses on Coursera

Explore other highly rated courses in data science available on Coursera to expand your learning path:

- Geographic Information Systems (GIS) Specialization Course 9.8/10

- IBM Data Management Professional Certificate Course 9.8/10

- DeepLearning.AI Data Analytics Professional Certificate Course 9.8/10

- Prepare Data for Exploration Course 9.8/10

- Process Data from Dirty to Clean Course 9.8/10

- Analyze Data to Answer Questions Course 9.8/10

- Sequence Models Course 9.8/10

- Generative Adversarial Networks (GANs) Specialization Course 9.8/10

- Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital Course 9.8/10

- Executive Data Science Specialization Course 9.8/10

## Top Alternatives on Other Platforms

Looking for a different teaching style or approach? These top-rated data science courses from other platforms cover similar ground:

- PowerBI Zero to Hero Course 9.7/10 Udemy

- Complete MLOps Bootcamp With 10+ End To End ML Projects Course 9.7/10 Udemy

- LLM Engineering: Master AI, Large Language Models & Agents Course 9.7/10 Udemy

- LangChain Mastery: Build GenAI Apps with LangChain &Pinecone Course 9.7/10 Udemy

- ChatGPT Training Course: Beginners to Advanced Course 9.7/10 Edureka

- Learn Data Science Course 9.7/10 Educative

- HarvardX: Data Science: R Basics course 9.7/10 EDX

- DavidsonX: Analyzing and Visualizing Data with Power BI course 9.7/10 EDX

- HarvardX: Fundamentals of TinyML course 9.7/10 EDX

- HarvardX: CS50’s Introduction to Databases with SQL course 9.7/10 EDX

## More Courses from Pearson

Pearson offers a range of courses across multiple disciplines. If you enjoy their teaching approach, consider these additional offerings:

- AWS Certified Machine Learning - Specialty 8.1/10

- Red Hat Certified Engineer (RHCE) EX294 Specialization 8.1/10

- Java SE 17 Developer (1Z0-829): Unit 5 - Mastering Streams and Lambda Expressions 8.1/10

- Certified Kubernetes Administrator (CKA): Unit 6 8.1/10

- Cisco Software-Defined WAN for Enterprise and Cloud Specialization 8.1/10

- Certified Kubernetes Security Specialist (CKS): Unit 5 8.1/10

- Full-Stack React with Spring Boot Course 8.1/10

- Learning Deep Learning: From Perception to Large Language Models 8.1/10

View all courses from Pearson →

## Related Articles & Guides

Deepen your understanding with these articles from our editorial team, covering career advice, industry trends, and learning strategies:

- Build AI skills with the Google AI Professional Certificate

- Python Tutorial: Best Courses to Learn Python in 2026

- CISSP vs CompTIA Security+: Which Cert Should You Pursue?

- Coursera Data Analytics Professional Certificate: Worth It in 2026?

- Best edX Courses in 2026: Top Picks by Enrollment and Career Value

- Best Online Coursera Courses in 2026: What's Actually Worth Your Time

- Udemy Online: What the Platform Actually Delivers in 2026

- OKR for Leaders: 7 Best Training Courses Compared (2026)

- Generative AI for Marketing with Microsoft 365 Copilot: Professional Certificate Review

- The Best React Courses in 2026, Ranked and Reviewed

## Explore All Course Categories

Not sure what to learn next? Browse our full catalog of course categories to find the right fit for your career goals:

Agile & Scrum Courses

AI Courses

Arts and Humanities Courses

Business & Management Courses

Cloud Computing Courses

Computer Science Courses

Construction Management Courses

Cybersecurity Courses

Data Analyst Courses

Data Analytics Courses

Data Engineering Courses

Data Science Courses

Design Courses

Developer Courses

Economics & Finance Courses

Education & Teacher Training Courses

Entrepreneurship Courses

Excel Courses

Finance Courses

Game Development Courses

Graphic Design Courses

Health Science Courses

Information Technology Courses

Language Learning Courses

Leadership Courses

Lifestyle Courses

Machine Learning Courses

Marketing Courses

Math and Logic Courses

Music Courses

Negotiation Courses

Office Productivity Courses

Other

Personal Development Courses

Photography & Videography Courses

Physical Science and Engineering Courses

Project Management Courses

Python Courses

SEO Courses

Social Media Marketing Courses

Social Sciences Courses

Software Development Courses

Supply Chain Management Courses

Teaching Courses

Uncategorized

UX Design Courses

Web Development Courses

Explore related topics

Machine Learning

Data Analytics

Data Analyst

Python

Explore Related Topics

Best Data Science Courses

Learning Path

How to Become a Data Analyst

Browse All Courses

## User Reviews

No reviews yet. Be the first to share your experience!

## FAQs

What are the prerequisites for Data Science Fundamentals, Part 2?

A basic understanding of Data Science fundamentals is recommended before enrolling in Data Science Fundamentals, Part 2. Learners who have completed an introductory course or have some practical experience will get the most value. The course builds on foundational concepts and introduces more advanced techniques and real-world applications.

Does Data Science Fundamentals, Part 2 offer a certificate upon completion?

Yes, upon successful completion you receive a specialization certificate from Pearson. This credential can be added to your LinkedIn profile and resume, demonstrating verified skills to employers. In competitive job markets, having a recognized certificate in Data Science can help differentiate your application and signal your commitment to professional development.

How long does it take to complete Data Science Fundamentals, Part 2?

The course takes approximately 14 weeks to complete. It is offered as a paid course on Coursera, which means you can learn at your own pace and fit it around your schedule. The content is delivered in English and includes a mix of instructional material, practical exercises, and assessments to reinforce your understanding. Most learners find that dedicating a few hours per week allows them to complete the course comfortably.

What are the main strengths and limitations of Data Science Fundamentals, Part 2?

Data Science Fundamentals, Part 2 is rated 7.8/10 on our platform. Key strengths include: strong emphasis on hands-on python programming with pandas and scikit-learn; real-world case studies enhance practical understanding of data problems; covers full data workflow from acquisition to insight communication. Some limitations to consider: limited beginner support; assumes prior python knowledge; some topics covered too broadly for deep mastery. Overall, it provides a strong learning experience for anyone looking to build skills in Data Science.

How will Data Science Fundamentals, Part 2 help my career?

Completing Data Science Fundamentals, Part 2 equips you with practical Data Science skills that employers actively seek. The course is developed by Pearson, whose name carries weight in the industry. The skills covered are applicable to roles across multiple industries, from technology companies to consulting firms and startups. Whether you are looking to transition into a new role, earn a promotion in your current position, or simply broaden your professional skillset, the knowledge gained from this course provides a tangible competitive advantage in the job market.

Where can I take Data Science Fundamentals, Part 2 and how do I access it?

Data Science Fundamentals, Part 2 is available on Coursera, one of the leading online learning platforms. You can access the course material from any device with an internet connection — desktop, tablet, or mobile. The course is paid, giving you the flexibility to learn at a pace that suits your schedule. All you need is to create an account on Coursera and enroll in the course to get started.

How does Data Science Fundamentals, Part 2 compare to other Data Science courses?

Data Science Fundamentals, Part 2 is rated 7.8/10 on our platform, placing it as a solid choice among data science courses. Its standout strengths — strong emphasis on hands-on python programming with pandas and scikit-learn — set it apart from alternatives. What differentiates each course is its teaching approach, depth of coverage, and the credentials of the instructor or institution behind it. We recommend comparing the syllabus, student reviews, and certificate value before deciding.

What language is Data Science Fundamentals, Part 2 taught in?

Data Science Fundamentals, Part 2 is taught in English. Many online courses on Coursera also offer auto-generated subtitles or community-contributed translations in other languages, making the content accessible to non-native speakers. The course material is designed to be clear and accessible regardless of your language background, with visual aids and practical demonstrations supplementing the spoken instruction.

Is Data Science Fundamentals, Part 2 kept up to date?

Online courses on Coursera are periodically updated by their instructors to reflect industry changes and new best practices. Pearson has a track record of maintaining their course content to stay relevant. We recommend checking the "last updated" date on the enrollment page. Our own review was last verified recently, and we re-evaluate courses when significant updates are made to ensure our rating remains accurate.

Can I take Data Science Fundamentals, Part 2 as part of a team or organization?

Yes, Coursera offers team and enterprise plans that allow organizations to enroll multiple employees in courses like Data Science Fundamentals, Part 2. Team plans often include progress tracking, dedicated support, and volume discounts. This makes it an effective option for corporate training programs, upskilling initiatives, or academic cohorts looking to build data science capabilities across a group.

What will I be able to do after completing Data Science Fundamentals, Part 2?

After completing Data Science Fundamentals, Part 2, you will have practical skills in data science that you can apply to real projects and job responsibilities. You will be equipped to tackle complex, real-world challenges and lead projects in this domain. Your specialization certificate credential can be shared on LinkedIn and added to your resume to demonstrate your verified competence to employers.

## Similar Courses

Other courses in Data Science Courses

![Computer Science for Databases using SQL course](/api/media/file/images/2026/02/Computer-Science-for-Databases-using-SQL.webp?width=480)

EDX

Computer Science Courses

### Computer Science for Databases using SQL course

★★★★½

EDX

View Course »

Enroll

![Data Science Decisions in Time: Using Causal Information Course](/api/media/file/hero/data-science-decisions-in-time-using-causal-information-course.webp?v=2?width=480)

Coursera

Data Science Courses

### Data Science Decisions in Time: Using Causal Information Course

★★★★½

Coursera

View Course »

Enroll

![Introduction to Statistics for Data Science using Python Course](/api/media/file/hero/introduction-to-statistics-for-data-science-using-python-course.jpg?width=480)

EDX

Data Science Courses

### Introduction to Statistics for Data Science using Python Course

★★★★½

EDX

View Course »

Enroll

![Probability and Statistics in Data Science using Python Course](/api/media/file/hero/probability-statistics-data-science-python-course.jpg?width=480)

EDX

Data Science Courses

### Probability and Statistics in Data Science using Python Course

★★★★½

EDX

View Course »

Enroll

![Analyze Data Science Concepts Using R Course](/api/media/file/hero/analyze-data-science-concepts-using-r-course.webp?v=2?width=480)

Coursera

Data Science Courses

### Analyze Data Science Concepts Using R Course

★★★★½

Coursera

View Course »

Enroll

![Data Science Decisions in Time: Using Data Effectively Course](/api/media/file/hero/data-science-decisions-in-time-using-data-effectively-course.webp?v=2?width=480)

Coursera

Data Science Courses

### Data Science Decisions in Time: Using Data Effectively Course

★★★★☆

Coursera

View Course »

Enroll

## Related Job Opportunities

### High School Teacher

Asian College Of Teachers is a trading brand of TTA Training Pvt. Ltd

Warszawa, PL

Full-Time

PLN 54–86/yr

### Alternance chargé(e) de communication & marketing produit SaaS - Paris (F/H)

OKTOGONE

Paris, FR

Full-Time

### Bautechnik Freileitungsmast Planung Infrastruktur (m/w/d)

50Hertz Transmission GmbH

Berlin, DE

Full-Time

### Ingenieur Energietechnik als Projektmanager Inbetriebnahme & Dokumentation (m/w/d)

50Hertz Transmission GmbH

Berlin, DE

Full-Time

### IT Governance Compliance Managerin (m/w/d)

50Hertz Transmission GmbH

Berlin, DE

Full-Time

Browse more jobs on JobsNearMe.career →

### Explore Related Categories

All Data Science Courses

Explore Course Reviews

### Review: Data Science Fundamentals, Part 2

Your Name *

Email (optional, not displayed)

Rating *

Your Review *

### Discover More Course Categories

Explore expert-reviewed courses across every field

AI Courses

Python Courses

Machine Learning Courses

Web Development Courses

Cybersecurity Courses

Data Analyst Courses

Excel Courses

Cloud & DevOps Courses

UX Design Courses

Project Management Courses

SEO Courses

Agile & Scrum Courses

Business Courses

Marketing Courses

Software Dev Courses

Browse all 10,000+ courses »