This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
ArticleVideos Overview The projects are a way to enhance and improve your knowledge in the data science domain. The post 10 Data Science Projects Every Beginner should add to their Portfolio appeared first on Analytics Vidhya. To boost your resume, here we.
ArticleVideo Book This article was published as a part of the Data Science Blogathon. The post How to make an impressive Data Science Portfolio? ” A good first impression can work wonders” – J. appeared first on Analytics Vidhya.
Why should I focus on data science projects in my data science journey?” The post 6 Open Source Data Science Projects That Provide an Edge to Your Portfolio appeared first on Analytics Vidhya. ArticleVideos Introduction “I understand the concepts well. ” I have been in.
Overview Here is a list of Top 15 Datasets for 2020 that we feel every data scientist should practice on The article contains 5. The post Top 15 Open-Source Datasets of 2020 that every Data Scientist Should add to their Portfolio! appeared first on Analytics Vidhya.
📌Is your Data & AI transformation struggling to really impact the business? Discover the game-changing StratOps approach that: Bridges the Gap : Connect your Data & AI strategy to your operating model, to ensure alignment at every level. 🎯
Pursuing any data science project will help you polish your resume. The post Top Data Science Projects to add to your Portfolio in 2021 appeared first on Analytics Vidhya. Introduction 2021 is a year that proved nothing is better than a Proof of Work to evaluate any candidate’s worth, initiative, and skill.
Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network
However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in. It integrates these digital solutions into everyday workflows, turning raw data into actionable insights. You won’t want to miss this webinar!
Introduction Are you eager to dive into data science and sharpen your skills? This article will explore five exciting data science projects with step-by-step solutions. Look no further!
ArticleVideo Book This article was published as a part of the Data Science Blogathon. The post Portfolio Optimization using MPT in Python appeared first on Analytics Vidhya. Introduction In this article, we shall learn the concepts of.
Acquiring this complimentary portfolio of events contributes to Corinium’s rapid growth strategy, adding to its portfolio of tech-focused in-person, digital and hybrid events for data, analytics and digital innovation-focused executives. Find out more here: [link]. About RE•WORK.
Overview The ideal time to work on your data science portfolio with these open source projects From datasets on COVID-19 to a collection of. The post 6 Open Source Data Science Projects to Make you Industry Ready! appeared first on Analytics Vidhya.
This article was published as a part of the Data Science Blogathon. Introduction BI tools, including software services, apps, and data connectors, make up the Microsoft Power BI portfolio. Data from many sources are combined into a single dataset in this cloud-based platform.
“Everybody needs data literacy, because data is everywhere. Data-driven business management has emerged as an invaluable tool for businesses of all sizes, from startups to large corporations. How startups leverage data for agility and competition Each year, companies that use data grow by more than 30%.
Build a strong data science portfolio by showcasing technical skills, working on real-world projects, staying active on LinkedIn, and leveraging platforms like GitHub and Kaggle to demonstrate your expertise.
A survey from the Data & AI Leadership Exchange, an organization focused on AI and data education efforts, found that 98% of senior data leaders at Fortune 1000 companies expect to increase their AI spending in 2025, up from 82% in 2024. Over 90% of those surveyed said investments in AI and data were top priorities.
Especially if you’re in software development or data science. A portfolio of your projects, blog posts, and open-source contributions can set you apart from other candidates. Hey job seekers! Want to get noticed? Share your work with potential employers.
Overview Here are eight ambitious data science projects to add to your data science portfolio We have divided these projects into three categories – The post Add Shine to your Data Science Resume with these 8 Ambitious Projects on GitHub appeared first on Analytics Vidhya.
This article was published as a part of the Data Science Blogathon. The post Predict your Portfolio’s Stock Price Action using Facebook’s Prophet! Introduction Have you ever tried predicting your favorite stock’s price action? A stock’s price action varies with the company’s fundamentals and technicals.
This article was published as a part of the Data Science Blogathon. Introduction Are you a Data Science enthusiast or already a Data Scientist who is trying to make his or her portfolio strong by adding a good amount of hands-on projects to your resume? But have no clue where to get the datasets from so […].
But because Article was growing so quickly, managing one of the largest student housing portfolios in the US, it needed to be more intentional about operational efficiency. Off-the-shelf solutions simply didnt offer the level of flexibility and integration we required to make real-time, data-driven decisions, she says.
For CIOs leading enterprise transformations, portfolio health isnt just an operational indicator its a real-time pulse on time-to-market and resilience in a digital-first economy. Think of portfolio health as a state-level planning function.
Risks often emerge when an organization neglects rigorous application portfolio management, particularly with the rapid adoption of new AI-driven tools which, if unchecked, can inadvertently expose corporate intellectual property. That may sound straightforward, but many CIOs fall short of this fundamental discipline, Grimes observes.
For AMD, it’s an opportunity to demonstrate the capability of our AI accelerator portfolio in a market where investment in AI and high-performance computing is starting to boom,” said Zaid Ghattas, META Senior Commercial Lead, AMD. The collaboration highlights the growing importance of sovereign cloud infrastructure and AI in the Middle East.
Using data to inform business decisions only works when the data is correct. Unfortunately for the insurance industry’s data leaders, many data sources are riddled with inaccuracies. Data is the lifeblood of the insurance industry.
To address this, Gartner has recommended treating AI-driven productivity like a portfolio — balancing operational improvements with high-reward, game-changing initiatives that reshape business models. Gartner’s data revealed that 90% of CIOs cite out-of-control costs as a major barrier to achieving AI success.
Thirty years ago, businesses were starting to recognize that data was the future. However, they never imagined that big data technology would have the impact that we have witnessed in recent years. More companies are using big data to drive business decisions than ever before.
As the study’s authors explain, these results underline a clear trend toward more personalized services, data-driven decision-making, and agile processes. According to the study, the biggest focus in the next three years will be on AI-supported data analysis, followed by the use of gen AI for internal use.
The products that Klein particularly emphasized at this roundtable were SAP Business Data Cloud and Joule. Business Data Cloud, released in February , is designed to integrate and manage SAP data and external data not stored in SAP to enhance AI and advanced analytics.
Increasing the pace of AI adoption If the headlines around the new wave of AI adoption point to a burgeoning trend, it’s that accelerating AI adoption will allow businesses to reap the full benefits of their data. This is done through its broad portfolio of AI-optimized infrastructure, products, and services.
From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data. Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead. Increasing focus on building data culture, organization, and training. Cloud for data infrastructure.
We have previously talked about the reasons that data analytics technology is changing the financial industry. Analytics Insight has touched on some of the benefits of using data analytics to make better stock market trades. Technical analysts can also benefit from investing in data analytics technology.
In a recent survey , we explored how companies were adjusting to the growing importance of machine learning and analytics, while also preparing for the explosion in the number of data sources. You can find full results from the survey in the free report “Evolving Data Infrastructure”.). (You
Whereas robotic process automation (RPA) aims to automate tasks and improve process orchestration, AI agents backed by the companys proprietary data may rewire workflows, scale operations, and improve contextually specific decision-making. Why focus on the marketing department?
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content