Where to Begin with Data Science?

For about three years, I wasn’t sure where to begin learning data science. I’ve heard of different language to use (Python or R), seen the terms SQL and NoSQL thrown around, and the various tutorials with detailed explanations on for the command line, but rushing through Pandas. There is so much information and not enough information on how to consume such information. I’ve complied two or three basic resources for Python, NumPy, Pandas, and Jupyter Notebooks. These tools are essential in getting started in data science, and it takes some time to get comfortable with them.

The following is not so much a tutorial, but rather, what concepts and programs to look at first. Learning data science can be an overwhelming experience and not knowing where to begin can leave you in beginner’s purgatory for years.

2. Pandas. Pandas is Python library. It allows you clean and manipulate data as needed. For some, Pandas can be quite difficult learn, as it can be seen as abstract in nature. For me Pandas did not lend itself well to lecture, and the only way I became comfortable Pandas, was working with it on data sets.

3. NumPy. NumPy is a mathematical library for Python. NumPy extends naturally from Python and is easy to implement. However, given its extensive commands, references are always needed.

4. And finally, Jupyter Notebooks. Jupyter Notebooks is where you will apply the above. In Jupyter Notebooks, you can clean, manipulate, analyze, and visualize your data. Each notebook is self-contained, easy to organize, and you can quickly see the results of your written code.

Extra Credit: Where to get data? The following resources are a good place to start and to possibly find areas of interest:

Hopefully these resources will find you well. Whatever your plans for learning data science(self learn, bootcamp, degree program, etc.) are, understanding the above tools will be essential.

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