The latest articles on DEV Community by Kasyoki Thano (@collinskasyoki). https://dev.to/collinskasyoki https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F1160560%2F51235d2d-223a-425c-9b83-32900054ee6a.jpg https://dev.to/collinskasyoki en Kasyoki Thano Sun, 01 Oct 2023 06:32:27 +0000 https://dev.to/collinskasyoki/data-science-for-beginners-2023-2024-complete-road-map-26jd https://dev.to/collinskasyoki/data-science-for-beginners-2023-2024-complete-road-map-26jd <p>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.</p> <h2> Understanding Data Science </h2> <p>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.</p> <h2> Prerequisites (Programming and Math) </h2> <ul> <li>Python or R – These are versatile programming languages built specifically to be easy to use for handling and manipulating data.</li> <li>Yes. You do need mathematics. Especially statistics and linear algebra.</li> </ul> <h2> Data Gathering </h2> <ul> <li>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.</li> <li>For this, you need a combination of sources, building Python scripts to combine datasets, getting from other programs, and more.</li> <li>For a start, there is no need for complexity, as there are plenty of datasets online to reuse.</li> </ul> <h2> Data Handling </h2> <ul> <li>Master tools like NumPy and Pandas for playing with data.</li> <li>Visualize data using Matplotlib and Seaborn.</li> <li>Learn how to clean and transform data.</li> <li>Use EDA techniques to understand your data better.</li> </ul> <h2> Extracting Insight from Data </h2> <ul> <li>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.</li> <li>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.</li> </ul> <h2> Machine Learning Basics </h2> <ul> <li>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. </li> <li>Unsupervised is the opposite, where the models have to figure out the structure and patterns. </li> <li>Semi-supervised strikes a balance between the previous two (don't worry, there is more to learn as you progress).</li> <li>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.</li> </ul> <h2> Applying the Knowledge </h2> <ul> <li>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.</li> </ul> <p><strong>NB: Keep learning.</strong></p> datascience data dataengineering