Pearson
Data Science Fundamentals, Part 2 Specialization

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Pearson

Data Science Fundamentals, Part 2 Specialization

Applied Data Science with Python. Analyze realworld datasets, building applications, & applying machine learning with Python’s PyData

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 manipulate real-world data using Python libraries, APIs, and databases, and perform exploratory data analysis and visualization.

  • Build, evaluate, and interpret statistical and machine learning models to make predictions and draw inferences from complex datasets.

  • Apply best practices in hypothesis testing, A/B testing, and model validation to solve practical problems and communicate results effectively.

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Taught in English
Recently updated!

August 2025

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Specialization - 3 course series

What you'll learn

  • Gain a foundational understanding of Exploratory Data Analysis (EDA) and its historical context.

  • Develop practical skills in Python data visualization using matplotlib and seaborn.

  • Learn to identify and interpret relationships and correlations within datasets using advanced charting techniques.

  • Recognize and avoid common pitfalls in data analysis, including mixed effects and Simpson’s Paradox.

Skills you'll gain

Category: Seaborn
Category: Matplotlib
Category: Data Visualization Software
Category: Box Plots
Category: Correlation Analysis
Category: Data Visualization
Category: Histogram
Category: Statistical Analysis
Category: Scatter Plots
Category: Statistical Methods
Category: Data Analysis
Category: Exploratory Data Analysis
Category: Descriptive Statistics

What you'll learn

  • Master foundational and modern techniques for statistical inference and data analysis.

  • Apply computational and sampling-based approaches to real-world data problems.

  • Conduct hypothesis tests and optimize processes using A/B testing methodologies.

  • Distinguish between correlation and causation to ensure robust, actionable insights.

Skills you'll gain

Category: Statistical Hypothesis Testing
Category: Correlation Analysis
Category: A/B Testing
Category: Statistical Analysis
Category: Data-Driven Decision-Making
Category: Analytics
Category: Sampling (Statistics)
Category: Estimation
Category: Statistical Methods
Category: Statistical Inference
Category: Probability & Statistics
Category: Data Analysis

What you'll learn

  • Build and evaluate statistical models to predict outcomes using Python libraries such as SciPy, NumPy, and Scikit-learn.

  • Understand and apply the fundamentals of probability, statistical distributions, and regression analysis.

  • Identify and overcome common challenges in model fitting and performance evaluation.

  • Distinguish between statistical inference and prediction, and leverage machine learning algorithms for real-world applications.

Skills you'll gain

Category: Data Analysis
Category: Statistical Analysis
Category: Regression Analysis
Category: Statistical Inference
Category: Performance Metric
Category: Business Analytics
Category: Probability & Statistics
Category: Machine Learning
Category: Estimation
Category: Predictive Modeling
Category: Scikit Learn (Machine Learning Library)
Category: Statistical Modeling

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Instructor

Pearson
Pearson
221 Courses2,129 learners

Offered by

Pearson

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