Data science/ML/AI

Data Science Techniques

Cloud Engineer Roadmap

Self guide to become a data analyst

Going Denser with Open-Vocabulary Part Segmentation Publication date: 18 May 2023 Topic: Object detection Paper: https://arxiv.org/pdf/2305.11173v1.pdf GitHub: https://github.com/facebookresearch/vlpart Description: Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this work, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation. This ability comes from two designs: 🔹 We train the detector on the joint of part-level, object-level and image-level data. 🔹 We parse the novel object into its parts by its dense semantic correspondence with the base object.

Roadmap to Devops

How to choose a graph

One question to make your data project 10x more valuable If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started. The magic question Luckily, we don't need to spend all of our time defining the problem. Here is the one simple question that will get to the heart of any data request within minutes: "What decision are you trying to make?" Subtext: What action will you take once you have the answers? If there is no action, then there will be no impact. This question will cut through all of the clutter and get straight to the action. And the answer can be VERY telling! That's why it's so powerful. A good response is specific! Almost immediately, you should be able to picture what they'll do once they see the data. 🔗 Read more

R, ggplot, and Simple Linear Regression Begin to use R and ggplot while learning the basics of linear regression Rating ⭐️: 4.1 out 5 Students 👨‍🎓 : 42,633 Duration ⏰ : 2hr 14min of on-demand video Created by 👨‍🏫: Charles Redmond 🔗 Course Link #R #linear #Regression ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

Data Engineer's Pathway

Top 5 Reasons Why Machine Learning Projects Fail The intent of our article today is to help you get acquainted with the many reasons behind machine learning projects’ failure. We are hopeful that the information will help you plan a better implementation, one that carries fewer chances of failure in all three stages of ML execution: pre-project, during the project, and post-project. 1. Insufficient data 2. ML Models unsynchronized with the legacy systems 3. Lack of enough data scientists 4. Difficulty in updating 5. Lack of leaders’ support The solution to addressing these challenges more often than not lies with partnering with a skilled machine learning solution provider company that understands both business and technical implications of applying a new-gen technology in a non-digital organization. They can help you in not just creating a work plan of how to integrate machine learning projects but also with adopting the new system in the most optimal way. 🔗 Read more

Python Libraries For Data Science

7 Platforms for Getting High Paying Data Science Jobs 1. LinkedIn 2. Wellfound 3. Toptal 4. Upwork 5. Kolabtree 6. Indeed 7. Amazon Jobs

Logistic Regression Practical Case Study Breast Cancer detection using Logistic Regression Rating ⭐️: 4.7 out 5 Students 👨‍🎓 : 35,819 Duration ⏰ : 1hr 4min of on-demand video Created by 👨‍🏫: Hadelin de Ponteves, SuperDataScience Team, Ligency Team 🔗 Course Link #Logistic #Regression ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

8 Books that Will Teach You the Basics of Data Science In an era where data is hailed as the new oil, the demand for data scientists continues to soar. Data science, a multidisciplinary field that extracts insights and knowledge from data, has become a cornerstone of many industries. For those aspiring to enter this dynamic field, building a solid foundation is essential. Books are a timeless source of knowledge, and in this article, we’ll explore eight must-read books that will teach you the basics of data science, making your journey into this fascinating world more accessible. 1. “Python for Data Analysis” by Wes McKinney Wes McKinney’s book is a fantastic starting point for beginners. It focuses on the practical use of Python, one of the most popular programming languages in data science. You’ll learn how to work with data structures, perform data cleaning, and apply statistical analysis. The book also introduces the powerful Pandas library for data manipulation. Source-Link: analyticsinsight