Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Manu Jeevan
Top 5 AI trends for 2020
By Manu Jeevan, KDnuggets. The idea that computers or software programs can both learn and make decisions is particularly significant and something that we should be aware of, as their processes are growing exponentially over time. Because of these two skills, AI systems can now accomplish many of the tasks that were once reserved for humans. Over the last few years, the growth of AI technology has been staggering.
How to Select Rows and Columns in Pandas Using [ ], .loc, iloc, .at and .iat
Image by on Freepik You can download the Jupyter notebook of this tutorial here. In this blog post, I will show you how to select subsets of data in Pandas using [ ], .loc, .iloc, .at, and .iat. I will be using the wine quality dataset hosted on the UCI website. This data record 11 chemical properties (such as the concentrations of sugar, citric acid, alcohol, pH, etc.) of thousands of red and white wines from northern Portugal, as well as the quality of the wines, recorded on a scale from 1 to 10.
How to Learn Python for Data Science the Right Way
Most aspiring data scientists begin to learn Python by taking programming courses meant for developers. They also start solving Python programming riddles on websites like LeetCode with an assumption that they have to get good at programming concepts before starting to analyzing data using Python. This is a huge mistake because data scientists use Python for retrieving, cleaning, visualizing and building models; and not for developing software applications.
A Step-by-Step Guide to Transitioning your Career to Data Science – Part 2 - KDnuggets
By Manu Jeevan, KDnuggets. This is a continuation of my previous blog post on the same topic. Make sure you have read that one before continuing here. Step 5: Build your portfolio project Your prospects have answered all your questions. Now, you have to analyze them and answer these two questions: “What business problems are my prospects working on?” “What are the main technical skills they posses?” That’s it. Nothing more.
Nofima: Postdoctoral Researcher [Tromso, Norway]
At: Nofima Location: Tromsø, Norway Web: www.nofima.no Position: Data Scientist Contact: kate.washburn@nofima.no About the position: Nofima, the Norwegian food research institute, is searching for a candidate for a three-year postdoctoral project from the fall of 2019 at their Tromsø location. The project is in conjunction with UC Berkeley and Lund University. Start date is negotiable.
Top Stories, May 27- June 2: A Step-by-Step Guide to Transitioning your Career to Data Science - Part 1; Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis
Top Stories, May 27- June 2: A Step-by-Step Guide to Transitioning your Career to Data Science – Part 1; Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis Understanding Backpropagation as Applied to LSTM; How the Lottery Ticket Hypothesis is Challenging Everything we Knew About Training Neural Networks; AI in the Family: how to teach machine learning to your kids
Paperspace: Developer Evangelist (AI and Machine Learning) [Brooklyn, NY]
At: Paperspace Location: Brooklyn, NY Web: www.paperspace.com Position: Data Scientist Apply here. About Paperspace Paperspace is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use Paperspace to iterate faster and collaborate on intelligent, real-time prediction engines.
A Step-by-Step Guide to Transitioning your Career to Data Science – Part 1 - KDnuggets
If you are looking to transition your career to data science, the most common advice you may have heard is to learn Python or R, or to learn machine learning by pursuing courses like Andrew Ng's ML course on Coursera, or to start learning big data technologies like Spark and Hadoop. I call this a technology-focused route to a data science career.
Io-Tahoe Integrates with OneTrust and Joins Data Discovery Partner Program
Sponsored Post. Io-Tahoe, a pioneer in Smart Data Discovery and AI-Driven Data Catalog products, in its efforts to continue to transform the data discovery market, today announced its membership in the OneTrust Data Discovery Partner Program. Io-Tahoe’s integration with OneTrust will help customers populate the results of data discovery scans into the OneTrust Data Inventory & Mapping solution and trigger additional privacy workflows to maintain up-to-date records of processing.
Top KDnuggets Tweets, May 22 - 28: Mona Lisa smiles, speaks, and frowns: #Machinelearning brings old paintings and photos to life
KDnuggets Home » News » 2019 » May » Top Stories, Tweets » Top KDnuggets Tweets, May 22 – 28: Mona Lisa smiles, speaks, and frowns: #Machinelearning brings old paintings and photos to life Most popular @KDnuggets tweets for May 22 - 28 were: Most Retweeted, Liked, Clicked & Viewed: Mona Lisa smiles, speaks, and frowns: #Machinelearning brings old paintings and photos to life https://t.co/CbTwKVHipV https://t.co/aoD3e27JjK Top 10 most engaging Tweets Mona Lisa smiles, speaks, and frowns:...
Top Stories, May 20-26: How to Deploy Machine Learning Models: The Ultimate Guide; 7 Steps to Mastering SQL for Data Science
Top Stories, May 20-26: How to Deploy Machine Learning Models: The Ultimate Guide; 7 Steps to Mastering SQL for Data Science Building a Computer Vision Model: Approaches and datasets; Your Guide to Natural Language Processing (NLP); Analyzing Tweets with NLP in Minutes with Spark, Optimus and Twint; The 3 Biggest Mistakes on Learning Data Science
Top KDnuggets Tweets, May 15 - 21: 7 Steps to Mastering SQL for Data Science - 2019 Edition
KDnuggets Home » News » 2019 » May » Top Stories, Tweets » Top KDnuggets Tweets, May 15 – 21: 7 Steps to Mastering SQL for Data Science — 2019 Edition ( 19:n21 ) Most popular @KDnuggets tweets for May 15 - 21 were: Most Retweeted, Liked, Clicked & Viewed: 7 Steps to Mastering SQL for Data Science — 2019 Edition https://t.co/OXUUhlD9n9 https://t.co/snBc7VL1NS Top 10 most engaging Tweets 7 Steps to Mastering SQL for Data Science — 2019 Edition https://t.co/OXUUhlD9n9 https://t.co/snBc7VL1NS...
How to Combat ‘Non-Subscription Churn’ with Predictive Analytics - Dataconomy
Simply put, non-subscription churn happens when users or customers, who can end their relationship with your business at any time, leave your website or sales funnel. These types of customers may gradually reduce their purchase frequency over time, or they may all of a sudden never buy again. Companies in nearly every industry have to address this type of churn because it has the power to plateau the growth of any businesses even if that business is gaining customers quickly.
Technology’s Influence on Baggage Handling and Tracking
Anyone who has ever had their bag go AWOL while travelling knows that sinking feeling that comes with being told that your luggage is lost. Sadly, lost luggage is one of the perils of modern travel, and one that seems to happen on an all-too-often basis. Fortunately, just as technology is influencing the way we reach our destination, it’s also having an impact on our luggage, its transfer, tracking and safe arrival.
Big Data in Real Estate: Staying Ahead of the Curve - Dataconomy
Rapidly changing data management technology in the 21st Century has created cost-saving, business-expanding opportunities. Big companies around the world have realized the potential of modern technology to take over complex operations with more ease than a skilled worker could. There are debates about how it is pushing some traditional methods aside, but advantages tend to outweigh disadvantages.
Keep it real — say no to algorithm porn! - Dataconomy
For people in the know, machine learning is old hat. Even so, it’s set to become the data buzzword of the year?—?for a rather mundane reason. When things get complex, people expect technology to ‘automagically’ solve the problem. Whether it’s automated financial product consultation or shopping in the supermarket of the future?—?machine learning is the answer. Data scientists are jumping on the bandwagon, trying to outdo each other in the race for the coolest algorithm.
Three Mistakes that Set Data Scientists up for Failure - Dataconomy
The rise of the data scientists continues and social media is filled with success stories – but what about those who fail? There are no cover articles praising the failures of the many data scientists that don’t live up to the hype and don’t meet the needs of their stakeholders. The job of the data scientist is solving problems. And some data scientists can’t solve them. They either don’t know how to, or are obsessed about the technology part of the craft and forget what the job is all about.
How to do time series prediction using RNNs, TensorFlow and Cloud ML Engine - Dataconomy
The Estimators API in tf.contrib.learn (See tutorial here) is a very convenient way to get started using TensorFlow. The really cool thing from my perspective about the Estimators API is that using it is a very easy way to create distributed TensorFlow models. Many of the TensorFlow samples that you see floating around on the internets are not distributed?—?they assume that you will be running the code on a single machine.
Top 10 Big Data Videos on Youtube - Dataconomy
Whether you’re entirely new to the field of big data, or looking to expand your machine learning knowledge; whether you have 3 hours or 3 minutes; whether you want you want to know more about the technology, or the high-level applications- this list is a sample of the best Youtube has to offer on big data. Hit play, and enjoy a time- and cost-effective way to continue your big data exploration. It’s no surprise that a video from the ludicrously popular TED Talks has topped our list.
Operational Intelligence: The Next-Generation of Business Intelligence - Dataconomy
For years, companies have relied on business intelligence (BI) tools to drive strategy and identify opportunities based on historical data. However, in today’s fast-paced, data-driven economy, traditional BI often moves too slowly. Now, with the emergence of the Internet of Things and the demand for greater customer personalization, companies are increasingly striving to quickly make sense of their data as it changes.
14 Best Python Pandas Features - Dataconomy
Pandas is the most widely used tool for data munging. It contains high-level data structures and manipulation tools designed to make data analysis fast and easy. In this post, I am going to discuss the most frequently used pandas features. I will be using olive oil data set for this tutorial, you can download the data set from this page (scroll down to data section). Apart from serving as a quick reference, I hope this post will help you to quickly start extracting value from Pandas.
Top Slack Communities for Data Enthusiasts - Dataconomy
Slack is one of the fastest growing companies this year, and startup founders are going crazy over it. It offers persistent chat rooms organized by topic, as well as direct messaging. They also support the integration of other trendy, including Trello, GoogleDrive, DropBox, and GitHub. There are some 500,000 active users daily and 135,000 paid accounts. For some, Slack seems nothing more than a fancy glorified chat room. For others, the site’s simplicity is what makes it great.
Python Packages For Data Mining - Dataconomy
Just because you have a “hammer”, doesn’t mean that every problem you come across will be a “nail”. The intelligent key thing is when you use the same hammer to solve what ever problem you came across. Like the same way when we indented to solve a datamining problem we will face so many issues but we can solve them by using python in a intelligent way. In very next post I am going to wet your hands to solve one interesting datamining problem using python programming language.
10 Internet of Things Influencers You Need to Know - Dataconomy
It is a truth (almost) universally acknowledged that the Internet of Things is going to revolutionise how we live, work and think. From the explosion of sensor data, to connected cars, to the smart homes and cities of the future, IoT is permeating its way into every facet of our lives.
Jumping from PhD to Data Scientist: 3 Tips for Success - Dataconomy
William is a Data scientist at Quora, interested in data-driven decision making to improve both product and business. Always interested in learning new things and exploring the ubiquity of data in everyday life. DJ Patil is VP of Product at RelateIQ (acquired by Salesforce). He’s held many other roles, including former Chief Scientist at LinkedIn. DJ co-coined the term “Data Scientist” and co-authored Data Scientist: The Sexiest Job of the 21st Century.
Understanding Big Data: The Ecosystem - Dataconomy
For the uninitiated, the Big Data landscape can be daunting. The vast proliferation of technologies in this competitive market mean there’s no single go-to solution when you begin to build your Big Data architecture. In this series of articles, we will examine the Big Data ecosystem, and the multivarious technologies that exist to help enterprises harness their data.
How Data Science Is Driving The Driverless Car - Dataconomy
Google may be the first name associated with driverless cars, but they’re not the only ones interested in the technology. More importantly, it seems people have wildly different opinions on exactly how the story will play out. How much data will these cars produce? What kind of legal quandaries will we face?
Free Resources to get your Data Science Career started - Dataconomy
Data Scientist are in high demand, ranked the #1 profession in America on Glassdoor. And according to Forbes, an additional 1,700 job openings paying an average salary of $116,000 US dollars are available, contributing to an exponential expansion of the field. So are you interested in Data Science but not sure where to start?
The 5 Best Reasons to Choose MySQL - and its 5 Biggest Challenges - Dataconomy
The latest version of MySQL is one of the world’s most popular databases. It is open source, reliable, compatible with all major hosting providers, cost-effective, and easy to manage. Many organizations are leveraging the data security and strong transactional support offered by MySQL to secure online transactions and enhance customer interactions. However, enterprises using MySQL are presented with several challenges when their apps experience exponential growth and they need additional scale.
Community - Dataconomy
Dataconomy has 19 Meetups across the world and we’d love to see you at our next event in your community. Check out a full list of our meetups and click the links below to join us at the next one!
Investing, fast and slow - Part 2: Investment for Data Scientists 101 - Dataconomy
Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down to talk with their founders about investing, data, and the challenges of starting up.
MIE Summit 2017 Berlin - Dataconomy
On the 20th of June, join MIE Summit Berlin 2017, the World’s first open-space machine intelligence Summit. This event will give you the opportunity to learn, discuss and network in both an art gallery and a Biergarten (beer garden) in the most vibrant area of Berlin: What exactly does AI mean for all industries, from medicine to cars, from cognitive to neural networks? Can machines really outperform the human?
The Data Pipeline – Analytics at the Speed of Business - Dataconomy
Business leaders are growing weary of making further investments in business intelligence (BI) and big data analytics. Beyond the challenging technical components of data-driven projects, BI and analytics services have yet to live up to the hype. Early adopters and proponents were quick to frame solutions as miraculous reservoirs of insight and functionality. However, big data has not met many C-level executives’ expectations.
Government Stats Are Ready for Change (Book Review) - Dataconomy
For those of you similarly interested (obsessed?) with the changing role of government statistics relative to the explosion of highly dimensional private sector data, I recommend having a look at Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy from the National Academy of Sciences. It’s an easy read and offers a solid foundation for those who seek a better understanding of governmental statistics. This is the first of two volumes.
Big Data Symbiosis: Using Lessons from Big Data to Protect Big Data - Dataconomy
Big data is big news at the moment. The latest Yahoo! data breach, which affected data from over 500 million customers, continues to be discussed by press and public alike, while the role of big data in predicting and even influencing last year’s US election brought the term firmly into mainstream awareness.
Investing, fast and slow - Part 1: The Present and the Future of AI in Investment - Dataconomy
Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down to talk with their founders about investing, data, and the challenges of starting up.
Three Key Facts About Sensors That Are Driving IoT Forward - Dataconomy
As the collectors of actionable input information, networked smart devices with embedded sensors, software and electronics are a key driving force behind the Internet of Things (IoT). However, they do not generate value for organizations on their own. Powerful, fast database technologies are required to create meaningful insight from the connected devices that are generating Big Data.
AIOps - The Type of 'AI' with Nothing Artificial About It - Dataconomy
The term AI (Artificial Intelligence) is being thrown around left and right these days. Many companies claim they have an AI play even when they don’t. But there’s another type of AI—an algorithmic approach to intelligence—that is smart and is emerging as the type of AI that IT organizations of all types could start implementing very soon. And there’s nothing artificial about it.
Programming with R - How to Get a Frequency Table of a Categorical Variable as a Data Frame - Dataconomy
Categorical data is a kind of data which has a predefined set of values. Taking “Child”, “Adult” or “Senior” instead of keeping the age of a person to be a number is one such example of using age as categorical. However, before using categorical data, one must know about various forms of categorical data First of all, categorical data may or may not be defined in an order. To say that the size of a box is small, medium or large means that there is an order defined as small<medium<large.
White Paper: The Importance of a Strategic Response to Cyber Incidents - Dataconomy
In today’s world of never ending cybersecurity threats, companies must be more diligent than ever in protecting their data from external and internal threats. The question of dealing with cybersecurity incidents is not a matter of if but WHEN. How a company responds to a breach is vital – the fact is an ill-conceived response strategy can have long-term, damaging consequences for a business.
Using Data To Build Better Online User Experiences - Dataconomy
Do you know what an Exabyte is? One Exabyte is equal to one billion gigabytes. So if something is 2.5 exabytes in size, numerically it is written as 2,500,000,000 gigabytes. The full value, showing the gigabytes numerically as well, is probably too many zeroes for anyone to comprehend. Regardless, 2.5 exabytes signifies the amount of data created daily on the Internet. This consumer data is the stuff of every marketing director’s dreams.
Seizing Opportunities in with Data-as-a-Service Products - Dataconomy
We’ve published a white paper, where we look back at the big data and business intelligence trends over the past years and highlight examples of successful Data-as-a-Service products with deep dives into Social Media Monitoring, Self-Service BI and Visual Data Discovery and Analytics Merging the Physical and Virtual Worlds, complete with lessons you can apply to your own projects.
What Does Trust Mean in IoT? - IoT-EPI Challenge - Dataconomy
On a sunny Friday morning, the IoT-EPI Challenge started bright and early at 9 AM (which is quite early, for Berlin standards) with an introductory talk for the participants and the media to get the rundown of the day. This wasn’t a typical idea-hackathon. Collaboration was key.
How Augmented Intelligence Helps Businesses Grow - Dataconomy
It used to be a maxim that expanding too fast was the quickest way to kill a successful business. Rapid growth brings risks as well as opportunities. But utilizing augmented intelligence, an already-popular technology which surfaces patterns in data without humans having to look at it, means that organizations can avoid some of the bear traps that lie in that uncharted territory. Businesses using this take opportunities to grow with more confidence.
"Collaboration is the key for transformation" - Open Innovation at the IoT-EPI Meet & Greet - Dataconomy
Collaboration and innovation go hand in hand, and are based in transcending frontiers – physical, technological, economical, you name it. Open innovation is one of the ways in which Europe can secure global competitiveness.
Data Science vs. Data Analytics - Why Does It Matter? - Dataconomy
Jargon can be downright intimidating and seemingly impenetrable to the uninformed. While complicated vernacular is an unfortunate side effect of the similarly complicated world of machines, those involved in computers, data and whole host of other tech-intensive sectors don’t do themselves any favors with sometimes redundant sounding terminology. Take the fields of data science and data analytics. Any sports fan will be familiar with the term analytics.
FinTech's Greatest Challenge - Identity & Trust in a Data Economy - Dataconomy
As humans, we were born to trust and form social connections. When we enter the world, within an hour we will draw our heads to seek the eyes or face of the person gazing at us. The Oxford Dictionary defines trust as “a firm belief in the reliability, truth, ability, or strength of someone or something”. This means trust is a form of nurturance – the parent-child bond, community cooperation and other social connections.
If you care about Big Data, you care about Stream Processing - Dataconomy
As the scale of data grows across organizations with terabytes and petabytes coming into systems every day, running ad hoc queries across the entire dataset to generate important metrics and intelligence is no longer feasible. Once the quantum of data crosses a threshold, even simple questions such as what is the distribution of request latencies becomes infuriatingly slow with the usual sql and database model.
The Problem With (Statistical) False Friends - Dataconomy
I recently stumbled across a research paper, Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US, which piqued my interest in derivative uses of data, an ongoing research interest of mine. A variety of deep learning techniques were used to draw conclusions about relationships of car ownership, political affiliation and demographics.
Free Webinar: How to Set up and Manage a Corporate Self Service Analytics Environment [DE] - Dataconomy
Data is one of the most prized commodities of the 21st. century. As companies face the challenges arising from a surge in the number of customer interactions and data, it can be difficult to successfully manage the vast quantities of information and still provide a positive customer experience. Businesses need to create a consumer-centric experience that is powered by (predictive) analytics.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Manu
Contact Manu, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack