#data-science

#python is First Step to Data Science
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The steadily increasing importance of data science across industries has led to a rapid demand for data scientists. It’s been said that the role of data scientist is the 21st century’s sexiest job title. If you wonder why it has become such a sought after position these days, the short answer is that there has been a huge explosion in both data generated and captured by organizations and common people and data scientists are the people who derive valuable insights from that data and figure out what can be done with it.If you go through some job advertisements for data scientists, you’ll see that expertise in data science and Python are considered as two of the most crucial skills described.In this post, we’re going to discuss why these skills are considered must for data scientists.1- What (...)

#data-science #magnimind-academy #data-scientist #education

12 Key Lessons from ML researchers and practitioners
▻https://hackernoon.com/12-key-lessons-from-ml-researchers-and-practitioners-3d4818a2feff?source

Machine learning algorithms come with the promise of being able to figure out how to perform important tasks by learning from data, i.e., generalizing from examples without being explicitly told what to do. This means that the higher the amount of data, the more ambtious problems can be tackled by these algorithms. However, developing successful machine learning applications requires quite some “black art” that is hard to find in text books or introductory courses on machine learning.I recently stumbled upon a great research paper by Professor Pedro Domingos that puts together lessons learned by machine learning researchers and practitioners. In this post, I am going to walkthrough those lessons with you.Get ready to learn about: pitfalls to avoid, important issues to focus on, and (...)

#machine-learning #development #best-practices #artificial-intelligence #data-science

Our 25 Favorite Data Science Courses From Harvard To Udemy
▻https://hackernoon.com/our-25-favorite-data-science-courses-from-harvard-to-udemy-9a89cac0358d?

Originally Posted HereLearning every facet of data science takes time. We have written pieces on different resources before. But we really wanted to focus on courses, or video like courses on youtube.There are so many options, it can be nice to have a list of classes worth taking.We are going to start with the free data science options so you can decide whether or not you want to start investing more in courses.Tip : Coursera can make it seem like the only option is to purchase the course. But they do have an audit button on the very bottom. Now, if you appreciate Coursera, by all means, you should purchase their specialization, I am still uncertain how I feel about it. But, I do love taking Coursera courses.Select the audit course option to not pay for the courseBootcamps and (...)

#data-science #big-data #python #machine-learning #learning

How to prevent embarrassment in AI
▻https://hackernoon.com/how-to-prevent-embarrassment-in-ai-5e64f437b9bb?source=rss----3a8144eabf

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The must-have safety net that’ll save your baconHow will you prevent embarrassment in machine learning? The answer is… partially.Expect the unexpected!Wise product managers and designers might save your skin by seeing some issues coming a mile off and helping you cook a preventative fix into your production code. Unfortunately, AI systems are complex and your team usually won’t think of everything.There will be nasty surprises that force you into reactive mode.Real life is like that too. I’m meticulous when planning my vacations, but I didn’t consider the possibility that I’d miss my train to Rome thanks to a hospital tour sponsored by shellfish poisoning. True story. It taught college-age me never to repeat the words “I’ve thought of everything.”Speaking of things nobody expects…When the (...)

#data-science #artificial-intelligence #hackernoon-top-story #machine-learning #technology

Forecasting Future Customer Inflow Numbers using #arima and FBProphet
▻https://hackernoon.com/forecasting-future-customer-inflow-numbers-using-arima-and-fbprophet-2f7

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Peaking into the futureSometimes it is important for a venture to peak into the future to understand how much volume foot traffic or how many units of products they can expect to sell. This could help better prepare for the future by scaling up sales and customer services team to adequately prepare for likely sales over the course of the next few years/months and ensuring there are demand side resources to handle projected growth.▻https://medium.com/media/ef398bdfacdcbc10075fcee3b0ac728d/hrefLike our trusted guide, Mr. Knuckles, in the above GIF, we can use historical data points to help us understand not just the present, but the future- helping guide us into the unknown. When we try and make such projections based only on time and values this is known as a time series analysis. We (...)

#programming #timeseries #data-science #forecast

Correlations Between Top Coins and the #cryptocurrency Market Dropped in 2019
▻https://hackernoon.com/correlations-between-top-coins-and-the-cryptocurrency-market-dropped-in-

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Last year, I published an article which discovered that 75% of the top 200 coins by market cap had a correlation of 0.67 or higher over the last two years.In 2018, correlations between cryptocurrencies and the total cryptocurrency market were especially high. 75% of the top 200 coins by market cap had a correlation of 0.87 or higher.High correlations are typically a really bad thing because it makes it harder for diversification to mitigate risk and implies that the market cannot differentiate between good & bad projects.2018 was bear market and it makes sense that correlations were high since the prices of all cryptocurrencies tumbled altogether.But since then, the market has changed. So far 2019 has primarily been a sideways market.The market has mostly been flat, with the (...)

#blockchain #bitcoin #finance #data-science

10 Great Articles On Data Science And Data Engineering
▻https://hackernoon.com/10-great-articles-on-data-science-and-data-engineering-d5abdf4a4a44?sour

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Data science and #programming are such rapidly expanding specialities it is hard to keep up with all the articles that come out from Google, Uber, Netflix and one off engineers. We have been reading several over the past few weeks and wanted to share some of our top blog posts for this week April 2019!We hope you enjoy these articles.Building and Scaling Data Lineage at NetflixBy: Di Lin, Girish Lingappa, Jitender AswaniImagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question — “Can I run a check myself to understand what data is behind this metric?”Now, imagine yourself in the role of a software engineer responsible for a micro-service which publishes data consumed by few critical (...)

#python #big-data #machine-learning #data-science

#learning Data Science : Our Favorite Data Science #books
▻https://hackernoon.com/learning-data-science-our-favorite-data-science-books-d02ada5ed5d?source

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Originally Posted HereWhether you are just breaking into data science, or you are looking to improve your data science skills. Books are one great method to get a base level understanding of specific topics. Now, we personally believe nothing beats experience, but in lieu of that, taking a course or reading a book is a great way possibilities that you can build on later when you are trying to practically approach data science.In data science, there are many topics to cover, so we wanted to focused on several specific topics. This post will cover books on #python, R programming, big data, SQL and just some generally good reads for data scientists.Heads Up! — This post contains referral links from Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a (...)

#big-data #data-science

#learning Data Science : Our Favorite #python Resources
▻https://hackernoon.com/learning-data-science-our-favorite-python-resources-from-free-to-not-877

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Python is a common language that is used by both data engineers and data scientists. This is because it can automate the operational work that data engineers need to do and has the algorithms, analytics, and data visualization libraries required by data scientist.In both rolls, the need to manage, automate and analyze data is made easier by only a few lines of code. So much so that one of the books we have read and seen in many data focused practitioners libraries in the book Automate The Boring Stuff With Python.The book covers python basics and some simple automation tips. This is especially good for business analysts who work heavily in Excel.There are also books by O’Reilly that are also a great overview of the basics.You can start your list of books with the Python Cookbook. This (...)

#programming #machine-learning #data-science

Learning Data Science In 6 Weeks — How You Can Do It?
▻https://hackernoon.com/learning-data-science-in-6-weeks-how-you-can-do-it-d46520c12d43?source=r

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Learning Data Science In 6 Weeks — How You Can Do It?With data science emerging as one of the hottest professions in the recent years, there’s an extremely high demand for data practitioners.However, many aspirants are bogged down by the myth that you need a Ph.D. or a Master’s in the field to become a data scientist.Those with very little statistics or programming skills too are often afraid of taking up courses in data science fearing they won’t find any use of what they learn.But in reality, if you have a passion for learning, can grapple with new challenges and persevere, you can learn data science in just 6 weeks data camp.And we don’t mean learning some basic things that won’t help you in getting a job.In 6 weeks, the right course can get you job-ready by teaching you the requisite skills (...)

#ai #entrepreneur #data-science #machine-learning #magnimind-academy

Interview with #kaggle Grandmaster, Data Scientist at Point API: Pavel Pleskov
▻https://hackernoon.com/interview-with-kaggle-grandmaster-data-scientist-at-point-api-pavel-ples

Interview with Kaggle Grandmaster, Data Scientist at Point API (NLP startup): Pavel PleskovPart 25 of The series where I interview my heroes.Index to “Interviews with ML Heroes”Today I’m honored to be interviewing a Kaggle Grandmaster from the ods.ai community.I’m excited to be talking to Competitions GrandMaster (Ranked #4, Kaggle: @ppleskov) and Discussions Expert: (Ranked #29): Pavel PleskovPavel has a background in Math and Economics. Currently, he is working as a Data Scientist at Point API (NLP startup). He has worked as a Financial Consultant and as a Quant Researcher earlier.Sanyam:​ Hello Grandmaster, Thank you for taking the time to do this.Pavel Pleskov: My pleasure!Sanyam:​ Currently, you’re one of the Top 5 ranked Comp GrandMasters and are actively working on Data Science (...)

#data-science #deep-learning #artificial-intelligence #machine-learning

Learn Data Engineering : My Favorite Free Resources
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Learn Data Engineering: My Favorite Free Resources For Data EngineersBy Benjamin Rogojan originally posted hereThere are a lot of lists of resources for data science and machine learning. We wanted to create a list of resources for those of you who might not be interested in data science but instead would like to pursue data engineering.Let’s first lay out the basic skills required to be a good data engineer.A data engineer specializes in several specific technical aspects. Data engineers have solid automation/programming skills, ETL design, understand systems, data modeling, #sql, and usually some other more niche skills. For instance, some data engineers start to dabble with R and data analytics. This will also be driven by their specific role. As a data engineer, I have been asked to (...)

#data-engineer-resources #data-engineer #data-engineer-skills #data-science

How to Solve Talent Shortage in Data Science Jobs
▻https://hackernoon.com/how-to-solve-talent-shortage-in-data-science-jobs-51a4e45ce9a7?source=rs

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Recent years have brought an explosion of data science jobs, and the demand is on the rise. Data analysts, data miners, Big Data scientists, and more similar job titles populate online job boards now. This is a new domain, and very few people, if any, occupying these positions have an academic qualification in this exact field.Most data scientists come from other jobs or entirely different career paths. Some of them were engineers; others were programmers, statisticians or mathematicians. Mostly any job which required a solid understanding of logic and statistics can act as a launching point for a career in data science. Here is an outlook on a few trends identified, proposed and analyzed by the likes of PwC, Forbes and InData Labs.Call for Specialists and Fierce CompetitionAs most (...)

#big-data #data-science #data-science-job #ai #hr-talent-management

I Found 10,000 Ways That Didn’t Work
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Learnings from getting my feet wet in a #kaggle competitionProtip: If you overfit enough, you will get great results on your training dataThe dust settles on the first Kaggle competition I’ve put serious time into and though still quite far from the coveted top spots, I reached the goal I had set out of earning a first medal for my profile page, and even got a silver medal for placing in the top 2% in the competition, ending up as number 58 out of over 4000 competing teams.More than the end placement though, the competition gave some good learnings when it comes to competitive data science, and data science in general, that I’m noting down in hope it will help other people avoid making the same mistakes I’ve made. Learning from others mistakes is the best strategy after all, since you (...)

#data-science #kaggle-competition #machine-learning

10 Great Articles On Data Science And #programming!
▻https://hackernoon.com/10-great-articles-on-data-science-and-programming-eec816941896?source=rs

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Data science and programming are two topics that continue to expand and evolve as computation, knowledge bases and best practices continue to improve. This makes it very difficult to keep up with all the new articles and bodies of thought. So We compiled a list of 10 articles we or other people have enjoyed in the past year or so on the topics of programming, data science and machine learning. We hope they provide you new perspective as well as practical advice.1. Why businesses fail at machine learningby Cassie KozyrkovImagine hiring a chef to build you an oven or an electrical engineer to bake bread for you. When it comes to machine learning, that’s the kind of mistake I see businesses making over and over.If you’re opening a bakery, it’s a great idea to hire an experienced baker (...)

#analytics #data-science #self-improvement #python

Interview with Kaggle Grandmaster, Senior CV Engineer at Lyft: Dr. Vladimir I. Iglovikov
▻https://hackernoon.com/interview-with-kaggle-grandmaster-senior-cv-engineer-at-lyft-dr-vladimir

Interview with Kaggle Grandmaster, Senior Computer Vision Engineer at Lyft: Dr. Vladimir I. IglovikovPart 24 of The series where I interview my heroes.Today, I’m honored to be talking to another great kaggler from the ODS community: (kaggle: iglovikov) Competitions Grandmaster (Ranked #97), Discussions Expert (Ranked #30): Dr. Vladimir I. IglovikovVladimir is currently working as the Senior Computer Vision Engineer at Level5, Self-Driving Division, Lyft Inc.Prior to Lyft, Vladimir was working as a Senior Data Scientist at TrueAccord. He has a background in Physics and holds a Ph.D. in Physics from UC Davis.About the Series:I have very recently started making some progress with my Self-Taught Machine Learning Journey. But to be honest, it wouldn’t be possible at all without the amazing (...)

#artificial-intelligence #machine-learning #data-science #deep-learning #computer-vision

R vs Python: What’s The Difference
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The challenge under ten categoriesWith the massive growth in the importance of Big Data, machine learning, and data science in the software industry or software service companies, two languages have emerged as the most favourable ones for the developers. R and Python have become the two most popular and favourite languages for the data scientists and data analysts. Both of these are similar, yet, different in their ways which makes it difficult for the developers to pick one out of the two.R is considered to be the best #programming language for any statistician as it possesses an extensive catalogue of statistical and graphical methods. On the other hand, Python does pretty much the same work as R, but data scientists or data analysts prefer it because of its simplicity and high (...)

#software-development #machine-learning #hackernoon-top-story #data-science

What is a #data Lake and How to Create One for Your Business
▻https://hackernoon.com/what-is-a-data-lake-im-confused-8e12d554f7a0?source=rss----3a8144eabfe3-

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If you are following the trends in data science, it is more likely that you have heard the words big data, analytics, and machine learning. These days everyone wants to jump into this area of data science. Many of the software giants like Google, Amazon, Microsoft & etc. are already leading the way.However, it’s not that easy for a new business to enter this area of expertise due to many reasons. One of the core problems is that the data is scattered everywhere in different systems and their own databases. It’s likely these datasets will live for many years, hardly providing any value for its businesses.Why Not Create a Data Warehouse Instead?Although it would be wonderful if we can create a data warehouse in the first place (Check my article on Things to consider before building a (...)

#data-science #data-warehouse #data-lake

The first things you need to know about machine learning
▻https://hackernoon.com/the-first-things-you-need-to-know-about-machine-learning-2c21b8afeff6?so

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Get initiated into the machine learning world now!photo from pexelsEveryone is talking about Machine Learning. Those who are in the business of fields related to computers and those who aren’t.It has become a new trend. It has become one of the words that make you sound intelligent. My curiosity has driven me to learn about it. At first, just for the sake of having something to say when the topic is brought up. However, after I have learned a bit about it. I was impressed. And I hope you do too after reading this article. I will write to you the basics with which you will get an idea about machine learning. If you find yourself interested then that’s a sign that you should dive into the topic more!Let me guide you through the non-technical aspect first.We will work our way with an (...)

#machine-learning #data-science #tech #technology #about-machine-learning

Taking Data Visualization to Another Level
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Courtesy: ▻https://pixabay.com/illustrations/background-data-network-web-3228704/When you tend to use one library for a certain period of time, you get used to it. But, you need to evolve and learn something new every day. If you are still stuck up with Matplotlib(Which is amazing), Seaborn(This is amazing too), Pandas(Basic, yet easy Visualization) and Bokeh, You need to move on and try something new. Many amazing visualization libraries are available in #python, which turns to be very versatile. Here, I’m going to discuss about these amazing libraries:PlotlyCufflinksFoliumAltair + VegaD3.js (My best Pick, i code in JS too)If you are aware and use the libraries mentioned above then you are on the right track of evolution. They can help in generating some amazing visualizations and syntax (...)

#javascript #data-visualization #data-analysis #data-science

Introducing: FakeAI companies
▻https://hackernoon.com/introducing-fakeai-companies-610d81d0f466?source=rss----3a8144eabfe3---4

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In recent days the network has been going crazy around a new study that found that 40% of European companies that say they use AI do not really do so. Wow.The study looked at about 2,830 companies who defined themselves as companies using artificial intelligence in some way, but when MMC’s research team went in, they discovered that there was really no evidence of using a Machine learning or anything resembling AI. Why those companies did it? Most simply said it helped them raise more money from investors.What do we learn from that? A. Not every company which adds “AI” to its name actually doing some AI work behind the scenes.B. There is a need to make a very significant distinction between companies that use AI by developing algorithms independently, and companies that use ready-made (...)

#data-science #artificial-intelligence #fake-news #machine-learning

The damaging effects of unplanned work
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For practically anyone, unplanned work kills several hours of planned productivity. For creative workers, such as those who write software, it kills days.A while ago at work, a prospective customer told us that if we could analyze their data in a certain way then they were interested in doing business with us. We have a team that handles ad-hoc analytic requests, but that team didn’t have the capacity at the moment to take the job on. They asked around for help and I that’s how I got involved.I’ve done a lot of customer-facing analytics over the course of my career and I don’t particularly like it. When the only definition of “done” is “the customer said they were satisfied with the analysis”, you know the scope of your project is going to forever creep until the customer decides to pay (...)

#technology #data-science #unplanned-work #software-development #programming

Data is the New Oil
▻https://hackernoon.com/data-is-the-new-oil-1227197762b2?source=rss----3a8144eabfe3---4

“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”— Clive HumbyDeep Learning is a revolutionary field, but for it to work as intended, it requires data. The area related to these big datasets is known as Big Data, which stands for the abundance of digital data. Data is as important for Deep Learning algorithms as the architecture of the network itself, i.e., the software. Acquiring and cleaning the data is one of the most valuable aspects of the work. Without data, the neural networks cannot learn.Most of the time, researchers can use the data given to them directly, but there are many (...)

#machine-learning #feifei-li #data-science #imagenet

Learning to understand possible effects variables such as income and physical activity have on…
▻https://hackernoon.com/learning-to-understand-possible-effects-variables-such-as-income-and-phy

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Learning to understand possible effects variables such as income and physical activity have on mental health using RMental Health, Source: PixabayMental health is increasingly becoming a topical issue that has traditionally been glossed over. We have began to understand the impact mental health has on productivity, general wellbeing, relationships and physical health and placed greater focus on mental wellbeing. Even employers have began placing greater emphasis on providing work environments and conditions that keep their employees as happy and healthy as possible, with progressive companies providing perks such as monthly massages, catered meals, free and subsidised gym and unlimited vacation. In light of this and as an individual interested in developing my #programming and data (...)

#mental-health #data-science #healthcare #r

My Hackathon Experiences
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My experiments in the world of hackathon started out of my boredom in office work and have turned out to be a rich collection of experiences. You can find the solutions at my github.RBL bank hackathon(3 days)Problem statement — Make a data science solution with customer dataOffline | Prize pool — 2 lakhsWhat I likedGreat food arrangementComfy workplaceCould have been betterForced to use API provided for fetching dataThe API didn’t work for 1.5 daysAPI have transaction data of only 1 user of just a few months. No machine learning was possible over it.No guidance provided on what to do with so less dataTeam presentations were private with the JuryCoinberg hackathonCoinberg hackathon(2 days)Problem statement — Cryptocurrency — Arbitrage trading | Sentiment analysis | Portfolio management | Trend (...)

#data-science #deep-learning #hackathons #machine-learning #nlp