𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗵 𝗼𝗳 𝗗𝗮𝘁𝗮𝗦𝗰𝗶𝗲𝗻𝗰𝗲 (with links to many short courses)

Here's a learning path for Data Science with links to short courses:

1. Introduction to Data Science

• Data Science Essentials (IBM) - https://www.ibm.com/training/badge/319fc03b-1e39-431d-b57b-969973ce90ee
• Data Science Fundamentals (IBM) - https://www.ibm.com/training/badge/89d74fae-8dab-4242-b9f9-88d11d22a8c1

2. Python Programming for Data Science

• Introduction to Python (Codecademy) - https://www.codecademy.com/learn/learn-python
• Learn Python the Hard Way (Zed Shaw) - https://learnpythonthehardway.org/
• Data Manipulation with pandas (DataCamp) - https://www.datacamp.com/courses/data-manipulation-with-pandas

3. Statistics and Probability

• Introduction to Statistics (Udacity) - https://www.udacity.com/course/intro-to-statistics--st101
• Statistics and Probability (Khan Academy) - https://www.khanacademy.org/math/statistics-probability

4. Data Visualization

• Data Visualization with ggplot2 (DataCamp) - https://www.datacamp.com/courses/data-visualization-with-ggplot2-1
• Data Visualization with Python (DataCamp) - https://www.datacamp.com/courses/introduction-to-data-visualization-with-python

5. Machine Learning

• Machine Learning (Andrew Ng, Coursera) - https://www.coursera.org/learn/machine-learning
• Applied Machine Learning (IBM) - https://www.ibm.com/training/badge/33d67b85-0f79-40c7-99b4-2b56e956aa50

6. Deep Learning

• Introduction to Deep Learning (Udacity) - https://www.udacity.com/course/deep-learning--ud730
• Neural Networks and Deep Learning (Coursera) - https://www.coursera.org/learn/neural-networks-deep-learning

7. Big Data

• Hadoop Fundamentals (Pluralsight) - https://www.pluralsight.com/courses/hadoop-fundamentals-part-one
• Apache Spark (edX) - https://www.edx.org/course/apache-spark-essentials

8. Data Science Capstone Project

• Data Science Capstone (Coursera) - https://www.coursera.org/learn/data-science-project

I hope you find this helpful! Remember, learning is a journey, and it's important to keep practicing and exploring new topics to become a great Data Scientist. Good luck!

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