Data science certification – why it is important and where to get it?
Data science jobs are one of most sought after and in-demand jobs in the IT industry right now. In order to get into this field and get these data science jobs, certification is needed and that is widely discussed below.
on Nov 30, 2020 in Certification, Data Science Certificate, Data Science Education, Great Learning
Deploying Trained Models to Production with TensorFlow Serving
TensorFlow provides a way to move a trained model to a production environment for deployment with minimal effort. In this article, we’ll use a pre-trained model, save it, and serve it using TensorFlow Serving.
on Nov 30, 2020 in Deployment, Modeling, Neural Networks, Python, TensorFlow
Data Science History and Overview
In this era of big data that is only getting bigger, a huge amount of information from different fields is gathered and stored. Its analysis and extraction of value have become one of the most attractive tasks for companies and society in general, which is harnessed by the new professional role of the Data Scientist.
on Nov 30, 2020 in About Gregory Piatetsky, Data Science, Data Scientist, History, Python
A Friendly Introduction to Graph Neural Networks
Despite being what can be a confusing topic, graph neural networks can be distilled into just a handful of simple concepts. Read on to find out more.
on Nov 30, 2020 in Graph, Neural Networks, Recurrent Neural Networks
The 4 Stages of Being Data-driven for Real-life Businesses
Building a new company or transforming an existing one into a data-driven enterprise is a growing process through multiple stages that takes time. The challenge is progressing into the next stage and, having attained the goal, maintaining a company culture that can remain there.
on Nov 27, 2020 in Business, Data-Driven Business
 Learn Deep Learning with this Free Course from Yann LeCun
Here is a freely-available NYU course on deep learning to check out from Yann LeCun and Alfredo Canziani, including videos, slides, and other helpful resources.
on Nov 27, 2020 in Courses, Deep Learning, NYU, Yann LeCun
How to Know if a Neural Network is Right for Your Machine Learning Initiative
It is important to remember that there must be a business reason for even considering neural nets and it should not be because the C-Suite is feeling a bad case of FOMO.
on Nov 26, 2020 in Algorithms, Machine Learning, Neural Networks
Better data apps with Streamlit’s new layout options
Introducing new layout primitives - including columns, containers and expanders!
on Nov 26, 2020 in App, Data Science, Machine Learning, Streamlit
Essential Math for Data Science: Integrals And Area Under The Curve
In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.
on Nov 25, 2020 in Machine Learning, Mathematics, Metrics, numpy, Python, Unbalanced
How to Incorporate Tabular Data with HuggingFace Transformers
In real-world scenarios, we often encounter data that includes text and tabular features. Leveraging the latest advances for transformers, effectively handling situations with both data structures can increase performance in your models.
on Nov 25, 2020 in Data Preparation, Deep Learning, Machine Learning, NLP, Python, Transformer
Simple Python Package for Comparing, Plotting & Evaluating Regression Models
This package is aimed to help users plot the evaluation metric graph with single line code for different widely used regression model metrics comparing them at a glance. With this utility package, it also significantly lowers the barrier for the practitioners to evaluate the different machine learning algorithms in an amateur fashion by applying it to their everyday predictive regression problems.
on Nov 25, 2020 in Data Visualization, Metrics, Modeling, Python, Regression
 TabPy: Combining Python and Tableau
This article demonstrates how to get started using Python in Tableau.
on Nov 24, 2020 in Data Visualization, Python, Tableau
Fraud through the eyes of a machine
Data structured as a network of relationships can be modeled as a graph, which can then help extract insights into the data through machine learning and rule-based approaches. While these graph representations provide a natural interface to transactional data for humans to appreciate, caution and context must be applied when leveraging machine-based interpretations of these connections.
on Nov 24, 2020 in Fraud, Fraud Detection, Graph Analytics, Machine Learning
 15 Exciting AI Project Ideas for Beginners
There are many branches to AI to learn, but a project-based approach can keep things interesting. Here is a list of 15 such projects you can get started on implementing today.
on Nov 23, 2020 in AI, Beginners, Great Learning
Know-How to Learn Machine Learning Algorithms Effectively
The takeaway from the story is that machine learning is way beyond a simple fit and predict methods. The author shares their approach to actually learning these algorithms beyond the surface.
on Nov 23, 2020 in Algorithms, Complexity, Interpretability, Machine Learning
The Rise of the Machine Learning Engineer
The evolution of Big Data into machine learning applications ushered in an exciting era of new roles and skillsets that became necessary to implement these technologies. With the Machine Learning Engineer being such a crucial component today, where the evolution of this field will take us tomorrow should be fascinating.
on Nov 23, 2020 in Data Engineer, Data Engineering, Data Scientist, Machine Learning Engineer, Trends
Computer Vision at Scale With Dask And PyTorch
A tutorial on conducting image classification inference using the Resnet50 deep learning model at scale with using GPU clusters on Saturn Cloud. The results were: 40x faster computer vision that made a 3+ hour PyTorch model run in just 5 minutes.
on Nov 23, 2020 in Computer Vision, Dask, Python, PyTorch, Scalability
How Machine Learning Works for Social Good
We often discuss applying data science and machine learning techniques in term so of how they help your organization or business goals. But, these algorithms aren't limited to only increasing the bottom line. Developing new applications that leverage the predictive power of AI to benefit society and those communities in need is an equally valuable endeavor for Data Scientists that will further expand the positive impact of machine learning to the world.
on Nov 21, 2020 in Advice, Chicago, Machine Learning, Social Good
Top 6 Data Science Programs for Beginners
Udacity has the best industry-leading programs in data science. Here are the top six data science courses for beginners to help you get started.
on Nov 20, 2020 in Beginners, Certificate, Data Engineer, Data Science Education, Data Visualization, Online Education, Python, R, SQL, Udacity
Adversarial Examples in Deep Learning – A Primer
Bigger compute has led to increasingly impressive deep learning computer vision model SOTA results. However most of these SOTA deep learning models are brought down to their knees when making predictions on adversarial images. Read on to find out more.
on Nov 20, 2020 in Adversarial, Computer Vision, Deep Learning
How Data Scientists Can Avoid ‘Lost in Translation’ Syndrome When Communicating With Management
When it comes to data science projects, the disconnect between business executives and data teams can lead to major tension. Keeping these challenges from arising in the first place through effective communication will help reduce friction with stakeholders.
on Nov 20, 2020 in Career, Career Advice, Communication, Data Science Skills, Data Scientist
AI and Automation meets BI
Organizations use a variety of BI tools to analyze structured data. These tools are used for ad-hoc analysis, and for dashboards and reports that are essential for decision making. In this post, we describe a new set of BI tools that continue this trend.
on Nov 19, 2020 in AI, Automation, BI
Compute Goes Brrr: Revisiting Sutton’s Bitter Lesson for AI
"It's just about having more compute." Wait, is that really all there is to AI? As Richard Sutton's 'bitter lesson' sinks in for more AI researchers, a debate has stirred that considers a potentially more subtle relationship between advancements in AI based on ever-more-clever algorithms and massively scaled computational power.
on Nov 19, 2020 in AI, AlphaGo, Machine Learning, OpenAI, Richard Sutton, Scalability, Trends
Kubernetes vs. Amazon ECS for Data Scientists
In this article, we’ll look at two container management solutions — Kubernetes and Amazon Elastic Container Service (ECS) — from a perspective that makes sense for aspiring and current data scientists.
on Nov 19, 2020 in Amazon, AWS, Containers, Data Science, Data Scientist, Kubernetes
Hypothesis Vetting: The Most Important Skill Every Successful Data Scientist Needs
A well-thought hypothesis sets the direction and plan for a Data Science project. Accordingly, a hypothesis is the most important item for evaluating whether a Data Science project will be successful.
on Nov 18, 2020 in Data Science, Data Science Skills, Data Scientist
5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use
If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.
on Nov 18, 2020 in Data Science Tools, Data Scientist, GitHub, Heroku, Machine Learning, Postgres, PyCharm, PyTorch, scikit-learn, Streamlit
AI Is More Than a Model: Four Steps to Complete Workflow Success
The key element for success in practical AI implementation is uncovering any issues early on and knowing what aspects of the workflow to focus time and resources on for the best results—and it’s not always the most obvious steps.
on Nov 17, 2020 in AI, Data Preparation, Data Science Process, Deployment, MathWorks, Simulation, Workflow
Facebook Open Sourced New Frameworks to Advance Deep Learning Research
Polygames, PyTorch3D and HiPlot are the new additions to Facebook’s open source deep learning stack.
on Nov 17, 2020 in Deep Learning, Facebook, Open Source, PyTorch, Research
Is Data Science for Me? 14 Self-examination Questions to Consider
You are intrigued by this exciting new field of Data Science, and you think you want in on the action. The demand remains very high and the salaries are strong. Before taking the leap onto this path, these questions will help you evaluate if you are ready for the challenges and opportunities.
on Nov 17, 2020 in Career Advice, Communication, Data Science, Data Science Skills, Jobs, Salary
Algorithms for Advanced Hyper-Parameter Optimization/Tuning
In informed search, each iteration learns from the last, whereas in Grid and Random, modelling is all done at once and then the best is picked. In case for small datasets, GridSearch or RandomSearch would be fast and sufficient. AutoML approaches provide a neat solution to properly select the required hyperparameters that improve the model’s performance.
on Nov 17, 2020 in Automated Machine Learning, AutoML, Hyperparameter, Optimization, Python
5 Things You Are Doing Wrong in PyCaret
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient. Find out 5 ways to improve your usage of the library.
on Nov 16, 2020 in Machine Learning, PyCaret, Python, Tips
How to Get Into Data Science Without a Degree
Breaking into any new field or slogging through a career change is always a challenge, and requires focus and even a little grit. While transitioning to becoming a Data Scientist is no different, aspiring to this role is possible, even without a formal post-secondary degree, largely due to the vast amount of quality learning resources available today.
on Nov 16, 2020 in Career Advice, Data Science, Online Education
Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision
This article compiles the 30 top Python libraries for deep learning, natural language processing & computer vision, as best determined by KDnuggets staff.
on Nov 16, 2020 in Computer Vision, Data Science, Deep Learning, Machine Learning, Neural Networks, NLP, Python
tensorflow + dalex = :) , or how to explain a TensorFlow model
Having a machine learning model that generates interesting predictions is one thing. Understanding why it makes these predictions is another. For a tensorflow predictive model, it can be straightforward and convenient develop an explainable AI by leveraging the dalex Python package.
on Nov 13, 2020 in Dalex, Explainability, Explainable AI, Machine Learning, Python, TensorFlow
How to Acquire the Most Wanted Data Science Skills
We recently surveyed KDnuggets readers to determine the "most wanted" data science skills. Since they seem to be those most in demand from practitioners, here is a collection of resources for getting started with this learning.
on Nov 13, 2020 in Algorithms, Amazon, Apache Spark, AWS, Computer Vision, Data Science, Data Science Skills, Deep Learning, Docker, NLP, NoSQL, PyTorch, Reinforcement Learning, TensorFlow
Do’s and Don’ts of Analyzing Time Series
When handling time series data in your Data Science analysis work, a variety of common mistakes are made that are basic, but very important, to the processing of this type of data. Here, we review these issues and recommend the best practices.
on Nov 12, 2020 in Data Preparation, Data Visualization, Seasonality, Time Series
Free From MIT: Intro to Computational Thinking with Julia
Introduction to Computational Thinking with Julia, with Applications to Modeling the COVID-19 Pandemic is another freely-available offering from MIT's Open Courseware.
on Nov 12, 2020 in Computer Science, COVID-19, Data Science, Julia, MIT
Top KDnuggets tweets, Nov 04-10: #DataVisualization of people votes. Land doesn’t vote. People do.
Also: Accelerated Natural Language Processing: A #Free Amazon #MachineLearning University Course; Essential data science skills that no one talks about; U.S. election maps are wildly misleading, so this designer fixed them; Top Certificates and Certifications in #Analytics, #DataScience, #MachineLearning and AI
on Nov 11, 2020 in Top tweets
Most Popular Distance Metrics Used in KNN and When to Use Them
For calculating distances KNN uses a distance metric from the list of available metrics. Read this article for an overview of these metrics, and when they should be considered for use.
By Sarang Anil Gokte on Nov 11, 2020 in K-nearest neighbors, Metrics, scikit-learn
Learn to build an end to end data science project
Appreciating the process you must work through for any Data Science project is valuable before you land your first job in this field. With a well-honed strategy, such as the one outlined in this example project, you will remain productive and consistently deliver valuable machine learning models.
on Nov 11, 2020 in Data Preparation, Data Science, GitHub, Portfolio, Python, Regression, Salary
Mastering TensorFlow Tensors in 5 Easy Steps
Discover how the building blocks of TensorFlow works at the lower level and learn how to make the most of Tensor objects.
on Nov 11, 2020 in Deep Learning, Python, Tensor, TensorFlow
Predicting Heart Disease Using Machine Learning? Don’t!
I believe the “Predicting Heart Disease using Machine Learning” is a classic example of how not to apply machine learning to a problem, especially where a lot of domain experience is required.
on Nov 10, 2020 in Advice, Failure, Healthcare, Machine Learning, Medical, Prediction
Every Complex DataFrame Manipulation, Explained & Visualized Intuitively
Most Data Scientists might hail the power of Pandas for data preparation, but many may not be capable of leveraging all that power. Manipulating data frames can quickly become a complex task, so eight of these techniques within Pandas are presented with an explanation, visualization, code, and tricks to remember how to do it.
on Nov 10, 2020 in Data Preparation, Pandas, Python
Moving from Data Science to Machine Learning Engineering
The world of machine learning — and software — is changing. Read this article to find out how, and what you can do to stay ahead of it.
on Nov 10, 2020 in Career Advice, Data Engineering, Data Science, Machine Learning, Machine Learning Engineer
5 Reasons Why Containers Will Rule Data Science
Historically, containers were a way to abstract a software stack away from the operating system. For data scientists, containers have historically offered few benefits.
on Nov 9, 2020 in Containers, Data Science, Gigantum, Kubernetes
My Data Science Online Learning Journey on Coursera
Check out the author's informative list of courses and specializations on Coursera taken to get started on their data science and machine learning journey.
on Nov 9, 2020 in Coursera, Courses, Data Science, Data Science Education
Doing the impossible? Machine learning with less than one example
Machine learning algorithms are notoriously known for needing data, a lot of data -- the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as "few-shot" or "one-shot" learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.
on Nov 9, 2020 in Algorithms, K-nearest neighbors, Machine Learning, Research
Change the Background of Any Image with 5 Lines of Code
Blur, color, grayscale and change the background of any image with a picture using PixelLib.
on Nov 9, 2020 in Computer Vision, Image Processing, Machine Learning, Python, Segmentation
Pandas on Steroids: End to End Data Science in Python with Dask
End to end parallelized data science from reading big data to data manipulation to visualisation to machine learning.
on Nov 6, 2020 in Dask, Data Science, Pandas, Python
Six Ethical Quandaries of Predictive Policing
When predictive machine learning models are applied to real-life scenarios, especially those that directly impact humans, such as cancer detection and other medical-related applications, the risks involved with incorrect predictions carry very high stakes. These risks are also prominent in how machine learning is applied in law enforcement, and serious ethical questions must be considered.
on Nov 6, 2020 in Bias, Crime, Ethics, Police, Predictive Analytics
Essential data science skills that no one talks about
Old fashioned engineering skills are what you need to boost your data science career.
on Nov 6, 2020 in Career Advice, Data Science, Data Science Skills
2 Coding-free Ways to Extract Content From Websites to Boost Web Traffic
There are 2 main coding-free solutions for extracting content from websites to build your content base: use web scraping tools and use content aggregation tools. We review top choices.
on Nov 5, 2020 in Content Curation, Octoparse, Web Scraping
How to Build a Football Dataset with Web Scraping
This article covers using Selenium to scrape JavaScript rendered content.
on Nov 5, 2020 in Javascript, Python, Selenium, Soccer, Web Scraping
Top 5 Free Machine Learning and Deep Learning eBooks Everyone should read
There is always so much new to learn in machine learning, and keeping well grounded in the fundamentals will help you stay up-to-date with the latest advancements while acing your career in Data Science.
on Nov 5, 2020 in Deep Learning, Free ebook, Machine Learning
Interpretability, Explainability, and Machine Learning – What Data Scientists Need to Know
The terms “interpretability,” “explainability” and “black box” are tossed about a lot in the context of machine learning, but what do they really mean, and why do they matter?
on Nov 4, 2020 in Explainability, Explainable AI, Interpretability, Machine Learning
 The Best Data Science Certification You’ve Never Heard Of
The CDMP is the best data strategy certification you’ve never heard of. (And honestly, when you consider the fact that you’re probably working a job that didn’t exist ten years ago, it’s not surprising that this certification isn’t widespread just yet.)
on Nov 4, 2020 in Career Advice, Certification, Data Science, Data Science Education
10 Principles of Practical Statistical Reasoning
Practical Statistical Reasoning is a term that covers the nature and objective of applied statistics/data science, principles common to all applications, and practical steps/questions for better conclusions. The following principles have helped me become more efficient with my analyses and clearer in my conclusions.
on Nov 3, 2020 in Data Analysis, Data Quality, Data Science, Statistical Analysis, Statistics
When good data analyses fail to deliver the results you expect
To all those Data Scientists out there who thrive on discovering actionable insights from your data (all of you, right?), take heed from this cautionary tale of a data analysis, a dashboard, and a huge waste of resources.
on Nov 3, 2020 in Advice, Dashboard, Failure, Goodhart’s Law, Project Fail
Topic Modeling with BERT
Leveraging BERT and TF-IDF to create easily interpretable topics.
on Nov 3, 2020 in BERT, NLP, TF-IDF, Topic Modeling
Data scientist or machine learning engineer? Which is a better career option?
In order to build automated data processing systems, we require professionals like Machine Learning Engineers and Data Scientists. But which of these is a better career option right now? Read on to find out.
on Nov 2, 2020 in Career Advice, Data Scientist, Great Learning, Machine Learning Engineer
The Missing Teams For Data Scientists
Still today, too large a percent of data science projects fail, many of which can be attributed to the impacts of how hard missing data teams hit the data science team. Advocating for the missing data engineering and operations components to your team will make your professional life easier and more productive.
on Nov 2, 2020 in Data Engineering, Data Science Skills, Data Science Team, Data Scientist, Team
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