Data is something that we encounter on a daily basis. Using data to make decisions and create groundbreaking solutions is an art. Data science is a field that involves using data to make meaningful decisions and create solutions, often incorporating machine learning (ML) and artificial intelligence (AI). To harness the full power of data science, you need to be familiar with the Python language and its libraries. These libraries include Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn, and others that you will encounter as you traverse your data science journey.
LIBRARIES
Pandas is an open-source data manipulation and analysis library for Python. It provides data structures and functions needed to manipulate structured data seamlessly, making it a popular choice for data science tasks. NumPy, which stands for numerical Python, is an open-source library for Python that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. It's a fundamental package for scientific computing in Python and serves as the foundation for many other data science libraries, including Pandas, SciPy, and Scikit-learn. Seaborn is a data visualization library for Python built on top of Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is one of the most widely used plotting libraries in the Python ecosystem, offering a variety of plotting functions and tools to create a wide range of graphs and charts. Scikit-learn is a popular open-source machine learning library for Python that provides simple and efficient tools for data analysis. It is built on top of NumPy, SciPy, and Matplotlib and offers a wide range of supervised and unsupervised learning algorithms.
Being a guru in data science also involves having a solid background in statistics, which will enable your smooth transition into the field. At first, learning all this will be overwhelming, but as you dive into the world of data, all this will be fun to do.
Networking plays a key role in helping you learn data science. This will enable you to get insights into what other people are doing and also help you get jobs and show others your work, giving you more recognition. Job searching for roles in data science can be done on platforms such as LinkedIn. Statistics show that approximately six people are hired every minute on LinkedIn , which translates to around 360 hires per hour globally.
Data science communities will help you know about relevant events and the technologies to incorporate into your projects.
By leveraging all this, you can get started on your data science journey!
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