The pathway to data science can be a daunting task in this world filled with tutorials, courses, articles, and more. While this is yet another data science article, it takes a different approach to simplicity and brevity.
Understanding Data Science
Data science covers the collection, analysis, and interpretation of data to uncover patterns and insights. It helps you make informed decisions and solve problems. Let's break it down.
Prerequisites (Programming and Math)
- Python or R – These are versatile programming languages built specifically to be easy to use for handling and manipulating data.
- Yes. You do need mathematics. Especially statistics and linear algebra.
Data Gathering
- This is a skill that is not emphasized enough, but data is the main pillar for the entire data science field. Learning how to gather data into a form that can be processed is crucial.
- For this, you need a combination of sources, building Python scripts to combine datasets, getting from other programs, and more.
- For a start, there is no need for complexity, as there are plenty of datasets online to reuse.
Data Handling
- Master tools like NumPy and Pandas for playing with data.
- Visualize data using Matplotlib and Seaborn.
- Learn how to clean and transform data.
- Use EDA techniques to understand your data better.
Extracting Insight from Data
- This is possible through visualizations, with Python having libraries like Matplotlib and Seaborn for that. Other useful tools include R and its powerful ggplot2 library, Tableau, Power BI, and more.
- The key is to pick a few and get good at using them, the importance lies in understanding what the data represents rather than using the tools.
Machine Learning Basics
- Machine learning (ML) is basically divided into supervised, unsupervised and semi-supervised learning. Supervised data includes data that has labels, so the ML labels understand its structure.
- Unsupervised is the opposite, where the models have to figure out the structure and patterns.
- Semi-supervised strikes a balance between the previous two (don't worry, there is more to learn as you progress).
- Other notable mentions are deep learning and reinforcement learning, but these come in the advanced stages of data science and might not be suitable for a beginner.
Applying the Knowledge
- Make noise and keep documenting your journey through articles and social media posts. Oh, and do not forget to build a portfolio while at it, you need to prove your skills if you expect to get hired.
NB: Keep learning.
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