Python is one of the most popular programming languages for data scientists, thanks to its powerful libraries and extensive support for machine learning and other data analysis tasks. There is a roadmap that I’ve gone through and recommend to you in order to get a basic understanding of Data Science from a programmatic perspective.
Step 0: Learn basic Python concepts:
Familiarizing yourself with the basics of programming. This includes learning fundamental concepts such as variables, if statements, loops, functions, and classes, also understanding basic data structures in Python, such as lists, dictionaries, and tuples.
Step 1: Perform basic operations on datasets
- Numpy
- Pandas
Numpy and pandas are two of the most popular libraries for working with data in Python. Numpy is a library designed specifically for numerical computing, providing powerful tools for performing complex mathematical calculations. Pandas, on the other hand, provides a more general-purpose framework for data analysis tasks, offering tools for exploring and visualizing data as well as for performing more advanced data wrangling and analysis.
Step 2: Visualize your data
- Matplotlib
- Plotly
Matplotlib provides tools for creating and visualizing data plots, while Plotly is a more advanced library that offers powerful tools for producing interactive data plots. Both of these libraries are widely used by data scientists working with Python, offering easy-to-use APIs for creating rich data visualizations.
Step 3 Begin your journey with basic algorithms of machine learning
- Scikit-learn
SciKit-Learn is a machine learning library that provides support for common algorithms like regression, classification, and clustering, making it easy to train models or perform data analysis tasks with just a few lines of code. SciKit-Learn also has tools for preprocessing data, visualizing the results, and testing models.
Step 4: Dive into the world of deep learning
- TensorFlow
- PyTorch
TensorFlow and Pytorch are two of the most popular libraries for performing machine-learning tasks in Python. TensorFlow was developed by Google and is a powerful, general-purpose library that supports a wide variety of machine-learning algorithms. It offers tools for training neural networks and other deep learning models, as well as support for mathematical operations on matrices and arrays. Pytorch is a newer library that was developed by Facebook, and it offers similar functionality to TensorFlow but with a more flexible, dynamic architecture that makes it easier for developers to customize and tailor their models for specific applications.
Step 5: Deploy your model
- Flask
- Django
Flask and Django provide tools for building web applications. Flask is a lightweight library, designed to be quick and easy to use, while Django is a more comprehensive framework, with many built-in features and utilities that make it easier to manage complex web applications. Both libraries provide powerful templating capabilities and other essential tools for developing web applications in Python.
Summary:
There are a lot more libraries to learn as a data scientist to face real-world tasks and meet expectations. But these libraries are powerful and can be qualified as the main ones after learning that you are a valuable candidate in the job market. Personally, it took me half a year to feel comfortable with using libraries like Numpy, Pandas, Matplotlib, Plotly, and Django. Other libraries, especially, these about machine learning require more time, practice, and basically more theoretical knowledge to become effective and confident with using them. These libraries are essential to become good at Data Science, other libraries, which you will learn along the way, are just an addition to the core that incorporates libraries included in the article that you’ve just read.
Thank you for your time!
Sources:
https://builtin.com/data-science/python-libraries-data-sciencehttps://pandas.pydata.org/docs/index.html https://numpy.org/doc/stable/
https://matplotlib.org/ https://plotly.com/ https://www.djangoproject.com/start/overview/