Sat.Apr 23, 2022 - Fri.Apr 29, 2022

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Introduction to SQL for Data Engineering

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this article, we will be looking for a very common yet very important topic i.e. SQL also pronounced as Ess-cue-ell. So this time I’ll be answering some of the factual questions about SQL which every beginner needs to know before getting […]. The post Introduction to SQL for Data Engineering appeared first on Analytics Vidhya.

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How To Improve Incident Response Time for Data Breaches

Smart Data Collective

It is no secret that cyberattacks are escalating in frequency and severity each year. They have led to a growing number of data breaches, which are creating major concerns for people all over the world. IBM reports that the average data breach cost over $4.2 million in 2021 , which is a figure that grows every year. Malicious actors are becoming increasingly crafty at intercepting communication and penetrating organizations to steal valuable data.

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15 Python Coding Interview Questions You Must Know For Data Science

KDnuggets

Solving the Python coding interview questions is the best way to get ready for an interview. That’s why we’ll lead you through 15 examples and five concepts these questions cover.

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Don’t Fear Artificial Intelligence; Embrace it Through Data Governance

CIO Business Intelligence

As someone who is passionate about the transformative power of technology, it is fascinating to see intelligent computing – in all its various guises – bridge the schism between fantasy and reality. Organisations the world over are in the process of establishing where and how these advancements can add value and edge them closer to their goals. The excitement is palpable.

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How To Get Promoted In Product Management

Speaker: John Mansour

If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.

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Face detection using the Caffe model

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this section, we will build a face detection algorithm using Caffe model, but only OpenCV is not involved this time. Instead, along with the computer vision techniques, deep learning skills will also be required, i.e. We will use the deep learning […]. The post Face detection using the Caffe model appeared first on Analytics Vidhya.

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What Artificial Intelligence can Help Businesses Manage Their Online Profiles

Smart Data Collective

A lot of factors go into building a business, but online reputation is a huge part of it. A lot of organizations don’t recognize the role that AI technology can play when it comes to business management, improving customer relationships and managing your business’s online profile. Customers tend to Google an organization prior to engaging with their services.

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15 best data science bootcamps for boosting your career

CIO Business Intelligence

An education in data science can help you land a job as a data analyst , data engineer , data architect , or data scientist. The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. Data engineers and data architects spend more time dealing with code, databases, and complex queries, whereas data analysts and data scientists typical

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Spark Data Streaming with MongoDB

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this article, we are going to talk about data streaming with apache spark in Python with codes. We will also talk about how to persist our streaming data into MongoDB.We have already covered the basics of Pyspark in the last article […]. The post Spark Data Streaming with MongoDB appeared first on Analytics Vidhya.

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7 Misconceptions About Data Science

Smart Data Collective

Data science is a broad field that can help organizations glean significant insights into various aspects of their operations. Whether it’s uncovering truths about customer buying habits or discovering new ways to make teams collaborate more efficiently, data science can be an extremely useful tool to all who take advantage of it. This is why the demand for data scientists is growing so rapidly.

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How Metadata Improves Security, Quality, and Transparency

KDnuggets

Metadata is the data providing context about the data, more than what you see in the rows and columns. By managing your metadata, you're effectively creating an encyclopedia of your data assets.

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Navigating the Future: Generative AI, Application Analytics, and Data

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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How MLOps Is Helping Overcome Machine Learning’s Biggest Challenges

CIO Business Intelligence

Enterprises are betting big on machine learning (ML). According to IDC , 85% of the world’s largest organizations will be using artificial intelligence (AI) — including machine learning (ML), natural language processing (NLP) and pattern recognition — by 2026. And a survey conducted by ESG found, “62% of organizations plan to increase their year-over-year spend on AI, including investments in people, process, and technology.”.

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Docker Tutorial for Beginners Part-I

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this article, we are going to introduce you to Docker tutorial and will further learn the basic commands as well from installing docker, Pulling images from the hub to running Linux machines in docker. Let’s first understand the challenges we face […]. The post Docker Tutorial for Beginners Part-I appeared first on Analytics Vidhya.

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Benefits of Using Big Data to Assist with Digital Adoption

Smart Data Collective

Digital transformation has been a goal for many organizations in recent years, and the shift to remote and hybrid working arrangements has only made this desire to future-proof their business more immediate. However, this transition isn’t easy and wouldn’t be possible without major advances in big data technology. Digital adoption has been defined as the reinvention of a company through the optimization of legacy systems and utilization of new technologies.

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Top 5 Free Cloud Notebooks in 2022

KDnuggets

Create and collaborate on data science projects or train machine learning models using free cloud Jupyter notebook platforms. You get a hassle-free IDE experience and free compute resources.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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4 Steps to Data-first Modernization

CIO Business Intelligence

Launching a data-first transformation means more than simply putting new hardware, software, and services into operation. True transformation can emerge only when an organization learns how to optimally acquire and act on data and use that data to architect new processes. The data-first transformation journey can appear to be a lengthy one, but it’s possible to break it down into steps that are easier to digest and can help speed you along the pathway to achieving a modern, data-first organizati

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Exploring Octoparse Web Scraping tool for Data Preparations

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this article let’s discuss one among the very popular and handy web-scraping tools Octoparse and its key features and how to use it for our data-driven solutions. Hope you all are familiar with “WEB SCRAPING” techniques and the captured data has […]. The post Exploring Octoparse Web Scraping tool for Data Preparations appeared first on Analytics Vidhya.

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What is Model Risk and Why Does it Matter?

DataRobot Blog

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. When business decisions are made based on bad models, the consequences can be severe.

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Best Data Science Career Tracks of 2022

KDnuggets

Top-rated data science tracks consist of multiple project-based courses covering all aspects of data. It includes an introduction to Python/R, data ingestion & manipulation, data visualization, machine learning, and reporting.

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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Cloud infrastructure sales rise as companies seek digital resiliency

CIO Business Intelligence

Global spending on cloud infrastructure services increased by 34% to a total of nearly $56 billion in the first quarter of 2022, driven by the need for resiliency and flexibility as businesses face supply chain problems and geopolitical upheaval, according to a report released Friday by analyst firm Canalys. Migrating workloads to the cloud, investing in data storage, and cloud-native application development have all been particular drivers of SMB (small and medium business) investment in the cl

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Guide to Audio Classification Using Deep learning

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction One of the most widely used applications in Deep Learning is Audio classification, in which the model learns to classify sounds based on audio features. When given the input, it will predict the label for that audio feature. These can be used […]. The post Guide to Audio Classification Using Deep learning appeared first on Analytics Vidhya.

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Data Analytics Technology is Positively Disrupting the World of Sports

Smart Data Collective

In recent years, organized sports have been steadily changed by big data. The news programs and sports updates shown on television have been made to be more entertaining, partly because of research conducted and the information analyzed. This information is usually gained from viewers and the wider public. More sports companies are likely to invest in big data in the future.

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KDnuggets Top Posts for March 2022: Why Are So Many Data Scientists Quitting Their Jobs?

KDnuggets

Also: 8 Free MIT Courses to Learn Data Science Online; Build a Machine Learning Web App in 5 Minutes; Best Data Science Books for Beginners; Linear vs Logistic Regression; and more!

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Understanding User Needs and Satisfying Them

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

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Salesforce extends Flow automations across Mulesoft, Slack and Tableau

CIO Business Intelligence

Salesforce is expanding its process automation tool Flow to enable a wider range of workflows and process automations to be built and triggered from across its large family of enterprise applications. Announced in 2021, Flow is a low-code tool which initially consolidated various process automation and workflow tools for Salesforce users to build workflows between the various parts of the Salesforce suite.

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From Blob Storage to SQL Database Using Azure Data Factory

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Azure data factory (ADF) is a cloud-based ETL (Extract, Transform, Load) tool and data integration service which allows you to create a data-driven workflow. The data-driven workflow in ADF orchestrates and automates the data movement and data transformation. In this article, I’ll show […].

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Manufacturers Benefit from Using AI to Facilitate Augmented Reality

Smart Data Collective

AI is changing the future of the manufacturing sector. According to one survey, 76% of manufacturing companies have either deployed AI or are in the process of developing an AI system to use in the near future. More and more the interaction between humans and machines becomes a hot topic in the manufacturing world. Leading manufacturers use AI to integrate automation in their production processes wherever possible, in order to handle repetitive tasks in a more efficient manner.

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KDnuggets News, April 27: A Brief Introduction to Papers With Code; Machine Learning Books You Need To Read In 2022

KDnuggets

A Brief Introduction to Papers With Code; Machine Learning Books You Need To Read In 2022; Building a Scalable ETL with SQL + Python; 7 Steps to Mastering SQL for Data Science; Top Data Science Projects to Build Your Skills.

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How Embedded Analytics Gets You to Market Faster with a SAAS Offering

Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

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Lessons From a Telco to Leverage Guardrails in Transformating through Data-first Modernization

CIO Business Intelligence

On the Amalfi Coast in Italy is the spectacular “Sentiero Degli Dei,” the Path of Gods. It provides a stunning scene. I have been lucky enough to stand there and take it in. However, I recall that to my left there was a steep drop of several hundred meters to the sea – with nothing in between. Why am I telling about my trip to Italy? Because it made me realize that yes, sometimes it is wonderful to go freely, without limitations or assistance and gain a spectacular view, but guardrails can help

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Building State-of-the-art Text Classifier Using HuggingFace and Tensorflow

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this blog, let’s explore how to train a state-of-the-art text classifier by using the models and data from the famous HuggingFace Transformers library. We will see how to load the dataset, perform data processing, i.e. tokenisation and then use the processed […].

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Emerging Risks are Systemic

Teradata

Managing the new class of emerging risks requires infusing the principles of resiliency and efficient risk analytics into traditional risk management frameworks.

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Data Management: How to Stay on Top of Your Customer’s Mind?

KDnuggets

Extract, profile, and manage your customer data in a flash with customer data management solutions, and achieve a customer-centric culture.

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Manufacturing Sustainability Surge: Your Guide to Data-Driven Energy Optimization & Decarbonization

Speaker: Kevin Kai Wong, President of Emergent Energy Solutions

In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.