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The intension for most data science projects is to build something that people use. Creating something purposeful requires a solid infrastructure and processes that keeps problem-solving front-and-center for your audience.
People realize that effective uses of data can increase competitiveness, even in a challenging marketplace. Here are six industries hiring data scientists now that will likely continue doing so for the foreseeable future.
In our quest to better understand and predict business outcomes, traditional predictive modeling tends to fall flat. However, causal inference techniques along with business analytics approaches can unravel what truly changes your KPIs.
We might have a reasonable sense for what "noise" is as some statically random phenomena that occurs in Nature. But, how can this same characteristic be defined--and understood--within the context of making judgements, such as in human behavior, corporate decision-making, medicine, the law, and AI systems?
Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.
Also: Open Source Datasets for Computer Vision; Prefect: How to Write and Schedule Your First ETL Pipeline with Python; Most Common Data Science Interview Questions and Answers; How to Select an Initial Model for your Data Science Problem.
Django is a Python web application framework enjoying widespread adoption in the data science community. But what else can you use Django for? Read this article for 9 use cases where you can put Django to work.
So many options are now available online to learn in the field of data science. There are several factors to consider to determine if these options or a traditional degree from an academic institution is the best approach for your personal learning style and career aspirations.
After working as a Data Scientist for a year, I am here to share some things I learnt along the way that I feel are helpful and have increased my efficiency. Hopefully some of these tips can help you in your journey :)
The results of the 2021 Stack Overflow Developer Survey were recently released, which is a fascinating snapshot of today's developers and the tools they are using. Have a look at some selections from the report, particularly those which may be of interest to data professionals.
AI models are necessarily trained on historical data from the real-world--data that is generated from the daily goings on of society. If social-based biases are inherent in the training data, then will the AI predictions highlight these same biases? If so, what should we do (or not do) about making AI fair?
Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.
Personalization drives growth and is a touchstone of good customer experience. Personalization driven through machine learning can enable companies to improve this experience while improving ROI for marketing campaigns. However, challenges exist in these techniques for when personalization makes sense and how and when specific options are recommended.
Access to high-quality, noise-free, large-scale datasets is crucial for training complex deep neural network models for computer vision applications. Many open-source datasets are developed for use in image classification, pose estimation, image captioning, autonomous driving, and object segmentation. These datasets must be paired with the appropriate hardware and benchmarking strategies to optimize performance.
Rendezvous Architecture helps you run and choose outputs from a Champion model and many Challenger models running in parallel without many overheads. The original approach works well for smaller data sets, so how can this idea adapt to big data pipelines?
Also: Most Common Data Science Interview Questions and Answers; 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks; How My Learning Path Changed After Becoming a Data Scientist; MLOPs And Machine Learning RoadMap
These top blogs were winners of KDnuggets Top Blog Rewards Program for July: Data Scientists and ML Engineers Are Luxury Employees; Top 6 Data Science Online Courses in 2021; Advice for Learning Data Science from Google's Director of Research; Pandas not enough? Here are a few good alternatives; A Learning Path To Becoming a Data Scientist; 5 Lessons McKinsey Taught Me That Will Make You a Better Data Scientist
The notion of Agile in software development has made waves across industries with its revolution for productivity. Can the same benefits be applied to the often arduous task of annotating data sets for machine learning?
Using Ray, you can take Python code that runs sequentially and transform it into a distributed application with minimal code changes. Read on to find out why you should use Ray, and how to get started.
Having access to broad and detailed population data can potentially offer enormous value to any organization looking to interact with specific demographics. However, access alone is not sufficient without being able to leverage advanced techniques to explore and visualize the data.
Once fresh out of school and ready to burst into an organization as a new hire with newly-developed skills and knowledge, many have learned that things tend to be a little different in the "real world" compared to university. A few shifts in your approach to continued learning and expanding your confidence might help you professionally reach a little further, faster.
For decades, SQL has been the foundation for how humans interact with data. Alternate approaches seem to continually attempt to replace this powerful language. However, while much progress remains in the techniques and tools for the curation and management of data, the skilled craftspeople who work with data -- through the lens of SQL -- are likely to be around for decades more.
What do you need to get started on your AI journey? Putting together a combination of the right project, people and infrastructure is no easy task. SAS and MIT SMR have collaborated to provide a comprehensive set of resources to guide you from conception to implementation. Learn from experts that successfully launched AI projects.
SQL is a very important skill for data analysts and data scientists. However, when you are just starting out learning in the field, how can you practice querying with SQL if you don’t have any data stored in a database?
This article summarizes the most common mistakes to avoid and outline best practices to follow in programming in general. Follow these tips to speed up the code review iteration process and be a rockstar developer in your reviewer’s eyes!
Also: Most Common Data Science Interview Questions and Answers; How Visualization is Transforming Exploratory Data Analysis; GitHub Copilot Open Source Alternatives; How To Become A Freelance Data Scientist – 4 Practical Tips
The strategic power of AI has been established thoroughly across many industries and companies, leading to surges in model creation. Investments in the people, processes, and tools for operationalizing models, referred to as ModelOps, lag. This function of operationalizing, integrating, and deploying AI models in line with businesses value expectations is growing into a core business capability as global use of AI matures.
What is a Modern Data Stack and how do you deploy one? This guide will motivate you to start on this journey with setup instructions for Airbyte, BigQuery, dbt, Metabase, and everything else you need using Terraform.
When Data Scientists use chi square test for feature selection, they just merely go by the ritualistic “If your p-value is low, the null hypothesis must go”. The automated function they use behaves no differently.
After analyzing 900+ data science interview questions from companies over the past few years, the most common data science interview question categories are reviewed in this guide, each explained with an example.
Artificial Intelligence and Machine Learning are the next-gen technology used in various fields. With the rise in online threats, it has become essential to include these technologies in cybersecurity. In this post, we will know what roles do AI and ML play in cybersecurity.
If you are a nerd-ish data scientist who wants to start working as an independent (remote) freelance data scientist, then these four practical tips can help you transition from the traditional 9-to-5 job to a dynamic experience as a remote contractor, just as the author did three years ago.
With so many accent variations, how do speech and voice technologies keep up? In a few words: accented speech training data, representative of diverse groups of people. The more people your model can understand, the more likely you are to acquire and retain customers.
There is always a lot to learn in machine learning. Whether you are new to the field or a seasoned practitioner and ready for a refresher, understanding these key concepts will keep your skills honed in the right direction.
Looking to up your data analytics consulting rates? Learn exactly what most freelancers are charging, and the rates you SHOULD be charging as a business intelligence and analytics consultant. This post will show you what you need to know to achieve maximum results for your data consulting career.
Also: Advice for Learning Data Science from Google’s Director of Research; Design patterns in machine learning; A Brief Introduction to the Concept of Data; 5 Mistakes I Wish I Had Avoided in My Data Science Career
While there may always seem to be something new, cool, and shiny in the field of AI/ML, classic statistical methods that leverage machine learning techniques remain powerful and practical for solving many real-world business problems.
Typically, development and testing ETL pipelines is done on real environment/clusters which is time consuming to setup & requires maintenance. This article focuses on the development and testing of ETL pipelines locally with the help of Docker & LocalStack. The solution gives flexibility to test in a local environment without setting up any services on the cloud.