Soil Science Courses

Coursera

Python for Data Science (and Version Control with GitHub)

Learn to load, clean, and analyze data in Python with Pandas and NumPy, visualize it in Matplotlib, Seaborn, and Plotly, run A/B tests, and version work on GitHub.

• Coursera
• 6 weeks, 6 hours a week
• Self-Paced
• Paid Course

Packt

Data Science Foundations: NumPy, Pandas & Visualization

Learn Python basics, then manipulate arrays with NumPy, filter and transform data with Pandas DataFrames, and plot line, scatter, violin and heatmap charts in Matplotlib and Seaborn.

• Coursera
• 5 weeks, 2 hours a week
• Self-Paced
• Paid Course

Packt

Data Science Model Deployments and Cloud Computing on GCP

Deploy machine learning models on Google Cloud: containerize apps with Docker and Cloud Run, serve models via App Engine and Cloud Functions, and orchestrate pipelines in Vertex AI.

• Coursera
• 10 weeks, 1 hour a week
• Self-Paced
• Paid Course

Data Science Fundamentals, Part 1: From Zero to Your First Project

Set up a Python environment, learn language basics, and build an Airbnb listing recommender while following a structured data science workflow on real data.

• Coursera
• 7 hours 6 minutes
• Self-Paced
• Paid Course

Data Science Fundamentals, Part 1: Data Collection & Modeling

Source real-world data through web requests, scraping, APIs, files, and relational databases, then parse XML and JSON and model it with object-oriented code in an ETL pipeline.

• Coursera
• 9 hours 5 minutes
• Self-Paced
• Paid Course

Data Science Fundamentals, Part 1: Managing Data with Python

Map Python data structures to relational tables with ORMs, build ETL pipelines, and validate, clean, and explore datasets using Pandas and descriptive statistics.

• Coursera
• 8 hours 3 minutes
• Self-Paced
• Paid Course

Data Science Fundamentals, Part 2: Data–Analysis and Visualization

Explore data with Python's matplotlib and seaborn: build histograms, boxplots, and scatter plots, spot correlations, and avoid pitfalls like Simpson's Paradox.

• Coursera
• 7 hours 33 minutes
• Self-Paced
• Paid Course

Data Science Fundamentals, Part 2: Estimation and Evaluation

Draw reliable conclusions from data: use estimation, hypothesis tests, confidence intervals, and sampling-based methods, apply A/B testing to web optimization, and separate correlation from causation.

• Coursera
• 6 hours 9 minutes
• Self-Paced
• Paid Course

Data Science Fundamentals, Part 2: Statistical Modeling and ML

Build and evaluate statistical models on Airbnb listing data: fit probability distributions with SciPy and NumPy, estimate parameters, run regressions, and predict with Scikit-learn.

• Coursera
• 9 hours 16 minutes
• Self-Paced
• Paid Course

Python With Data Science Private (Ages 14-18) (Live Online)

Discover the power of Python in data analytics with our immersive course designed for ages 14-18.

• CourseHorse
• 12 hours
• Self-Paced
• $399.00

Praxis Physical Science (5485) Study Guide and Test Prep

A Praxis 5485 study guide combining chemistry, physics, scientific practices, and teaching strategies with practice questions and full-length tests.

• Study.com
• Self-Paced
• Paid Course

GACE Computer Science P-12 (555) Study Guide and Test Prep

GACE Computer Science P-12 exam prep covering core concepts from algorithms and programming to networks, security, data, and computing systems.

• Study.com
• Self-Paced
• Paid Course

Packt

Foundations of ML & Python for Data Science

Build machine learning foundations: explore ML terminology and algorithm types, apply probability, hypothesis testing and the Central Limit Theorem, and handle data with Python, NumPy and Pandas.

• Coursera
• 3 weeks, 5 hours a week
• Self-Paced
• Paid Course

Packt

Matrix Calculus for Data Science & Machine Learning

Differentiate matrix and vector expressions — linear forms, quadratic forms, chain rules — and apply gradient descent and Newton's method in Python to least squares and Gaussian problems.

• Coursera
• 6 weeks, 1 hour a week
• Self-Paced
• Paid Course

EDUCBA

Statistics for Data Science with Python

Learn to summarize data with central tendency, dispersion and correlation, calculate probabilities, test hypotheses with p-values, and fit regression models using Python, Pandas and NumPy.

• Coursera
• 3 weeks, 3 hours a week
• Self-Paced
• Paid Course