Are you keen on becoming an information scientist but don’t know where to begin? Well, you’re in luck because I’ve got a study plan that’ll assist you to learn Python for data science in only 10 hours per week!

First things first, let’s talk resources. For those who’re on a budget, no problem! We’ll be sticking to free video resources to assist you to learn the ropes.

Before we dive in, I would like to say that I’m not affiliated with any of the resources suggested. Recommendations are based purely by myself experiences and people of other learners in the sector.

We’ll start off with the fundamentals of Python programming. You may’t construct a skyscraper and not using a solid foundation, and you’ll be able to’t turn into an information scientist without mastering the basics of Python. Listed below are some great resources to start:

When you’ve got a very good grasp of the fundamentals, it’s time to maneuver on to data evaluation with Python. On this stage, you’ll learn tips on how to use Python to work with data, including manipulating data, cleansing data, and visualizing data. Some great resources for this stage include:

• Pandas Tutorial by Corey Schafer: This YouTube tutorial series covers all the pieces you should learn about working with data in Python using the Pandas library. The trainer provides clear explanations and many examples to assist you to start. [Link: https://www.youtube.com/watch?v=vmEHCJofslg&list=PL-osiE80TeTsWlCv3ltqybIXRzMrvEvHE]
• Data Visualization with Matplotlib and Seaborn by Edureka: This YouTube tutorial covers all the pieces you should learn about data visualization in Python using Matplotlib and Seaborn. The trainer provides clear explanations and many examples to assist you to start. [Link: https://www.youtube.com/watch?v=6guP1VeWz2w]

Now that you just’ve got a very good foundation in Python and data evaluation, it’s time to maneuver on to machine learning. Machine learning is a key component of knowledge science, and Python has a wealth of libraries that make it easy to construct machine learning models. Start by learning about the differing types of machine learning, akin to supervised and unsupervised learning. Then, learn tips on how to use Scikit-learn to construct machine learning models. Some great resources for this stage include:

Now that you just’ve covered the fundamentals of Python programming, data evaluation, and machine learning, it’s time to place your skills to the test! On this stage, you’ll work on a capstone project that may assist you to apply what you’ve learned to a real-world problem.

Some great ideas for capstone projects include:

• Analyzing sales data to discover trends and make recommendations for improvement
• Constructing a advice system for movies or music
• Developing a predictive model for a particular problem, akin to predicting customer churn for a business

Listed below are some resources to assist you to start in your capstone project:

And that’s it! By following this study plan, you’ll be well in your option to becoming an information scientist in only 10 hours per week. Remember, the important thing to success is consistency and dedication. Keep practicing, continue to learn, and don’t surrender!