A lot of fledgling data scientists start to learn Python with programming courses that are meant for developers. Some even go as far as solving Python riddles on websites such as LeetCode. This is because they have an assumption that they need to be good with programming concepts before they can start to analyze data with Python.
This is a terrible mistake since Data Scientists make use of Python to retrieve, clean, visualize and build models. They don’t use it to develop software or applications. That’s why you need to focus on learning the libraries and modules in Python so you can be able to perform tasks like these.
Hopefully, these steps can help in your Python for data science journey.
1. Configure Your Programming Environment
One powerful programming environment is the Jupyter Notebook. It can be used to develop and present data science projects. The easiest way to install this notebook on your system is through the installation of Anaconda. This is the first choice Python distribution meant for data science. It is pre-loaded with most of the popular libraries.
You should look for blogs, websites or tutorial videos that will teach you the installation process of Anaconda. During this process, you should select the latest Python version. When you’re done installing Anaconda, you can then find a tutorial that will teach you how to make use of the Jupyter Notebook.
2. NumPy and Pandas
Python can actually be slow when dealing with large data or numerically heavy algorithms. You might then be asking: Why is Python so popular for data science then?
The reply to this is that for Python, it is simple to transfer numerically heavy tasks to a lower layer in Fortran or C extension form. This is where NumPy and Pandas come in. It is important for data science with Python.
First of all, you have to learn NumPy. This is because it is a fundamental module for computing. It ensures the support of optimized multidimensional arrays. These make up the fundamental data structure of a lot of Machine Learning algorithms.
You should then learn Pandas. The role of data scientists involves cleaning data. This is also called data wrangling or data munging. When it comes to manipulation of data, Panda is a very popular Python library. Pandas’ underlying code makes extensive use of the NumPy library. Data frame is the major data structure of Pandas.
3. Learn Basic Statistics With Python
A lot of budding data scientists jump into machine learning without attempting to learn the fundamentals of statistics. Ensure that you don’t make this mistake as statistics is absolutely vital to data science. Although potential data scientists that learn statistics end up learning only the theoretical concepts, most of them won’t learn the practical concepts. Practical concepts involve knowing what kind of problem is solvable with statistics.
The following are a handful of fundamental statistical concepts you need to learn. They are: Mean, Mode, Median, measure of variability, frequency distributions, significant testing, hypothesis testing, A/B testing, confidence intervals, z-scores, standard deviation, sampling, and probability basics, just to name a few.