Search results for "visualization"

1695 documents found out of 7175 total.

• PyCaret 101: An introduction for beginners

  This article is a great overview of how to get started with PyCaret for all your machine learning projects.

  https://www.kdnuggets.com/2021/06/pycaret-101-introduction-beginners.html

• Machine Learning Model Interpretation

  Read this overview of using Skater to build machine learning visualizations.

  https://www.kdnuggets.com/2021/06/machine-learning-model-interpretation.html

• Make Pandas 3 Times Faster with PyPolars

  Learn how to speed up your Pandas workflow using the PyPolars library.

  https://www.kdnuggets.com/2021/05/pandas-faster-pypolars.html

• Choosing the Right BI Tool for Your Business

  Here are six questions to ask as you search for the best BI tool for your specific needs.

  https://www.kdnuggets.com/2021/05/choosing-right-bi-tool-business.html

• Topic Modeling with Streamlit

  What does it take to create and deploy a topic modeling web application quickly? Read this post to see how the author uses Python NLP packages for topic modeling, Streamlit for the web application framework, and Streamlit Sharing for deployment.

  https://www.kdnuggets.com/2021/05/topic-modeling-streamlit.html

• Data Validation in Machine Learning is Imperative, Not Optional

  Before we reach model training in the pipeline, there are various components like data ingestion, data versioning, data validation, and data pre-processing that need to be executed. In this article, we will discuss data validation, why it is important, its challenges, and more.

  https://www.kdnuggets.com/2021/05/data-validation-machine-learning-imperative.html

• DataOps: 5 things that you need to know

  DataOps (Data Operations) has assumed a critical role in the age of big data to drive definitive impact on business outcomes. This process-oriented and agile methodology synergizes the components of DevOps and the capabilities of data engineers and data scientists to support data-focused workloads in enterprises. Here is a detailed look at DataOps.

  https://www.kdnuggets.com/2021/05/dataops-5-things-need-know.html

• Awesome list of datasets in 100+ categories

  With an estimated 44 zettabytes of data in existence in our digital world today and approximately 2.5 quintillion bytes of new data generated daily, there is a lot of data out there you could tap into for your data science projects. It's pretty hard to curate through such a massive universe of data, but this collection is a great start. Here, you can find data from cancer genomes to UFO reports, as well as years of air quality data to 200,000 jokes. Dive into this ocean of data to explore as you learn how to apply data science techniques or leverage your expertise to discover something new.

  https://www.kdnuggets.com/2021/05/awesome-list-datasets.html

• Animated Bar Chart Races in Python

  A quick and step-by-step beginners project to create an animation bar graph for an amazing Covid dataset.

  https://www.kdnuggets.com/2021/05/animated-race-bar-charts-python.html

• Easy MLOps with PyCaret + MLflow

  A beginner-friendly, step-by-step tutorial on integrating MLOps in your Machine Learning experiments using PyCaret.

  https://www.kdnuggets.com/2021/05/easy-mlops-pycaret-mlflow.html

• Vaex: Pandas but 1000x faster">Vaex: Pandas but 1000x faster

  If you are working with big data, especially on your local machine, then learning the basics of Vaex, a Python library that enables the fast processing of large datasets, will provide you with a productive alternative to Pandas.

  https://www.kdnuggets.com/2021/05/vaex-pandas-1000x-faster.html

• Binary Classification with Automated Machine Learning

  Check out how to use the open-source MLJAR auto-ML to build accurate models faster.

  https://www.kdnuggets.com/2021/05/binary-classification-automated-machine-learning.html

• The Explainable Boosting Machine

  As accurate as gradient boosting, as interpretable as linear regression.

  https://www.kdnuggets.com/2021/05/explainable-boosting-machine.html

• How to become an online data science tutor

  Your expertise in data science may be serving you well in your day job or you are on track to land that next dream position to do what you love. There are many others aspiring to attain your level of skill, and maybe you could consider helping them out... through a side gig of teaching.

  https://www.kdnuggets.com/2021/05/how-become-online-data-science-tutor.html

• Essential Linear Algebra for Data Science and Machine Learning">Essential Linear Algebra for Data Science and Machine Learning

  Linear algebra is foundational in data science and machine learning. Beginners starting out along their learning journey in data science--as well as established practitioners--must develop a strong familiarity with the essential concepts in linear algebra.

  https://www.kdnuggets.com/2021/05/essential-linear-algebra-data-science-machine-learning.html

• Rebuilding My 7 Python Projects">Rebuilding My 7 Python Projects

  This is how I rebuilt My Python Projects: Data Science, Web Development & Android Apps.

  https://www.kdnuggets.com/2021/05/rebuilding-7-python-projects.html

• How to get started managing data quality with SQL and scale

  Silent data quality issues are the biggest problem facing data teams today, who are flying blind with no systems or processes in place to monitor and detect bad data before it has a downstream impact.

  https://www.kdnuggets.com/2021/05/soda-io-managing-data-quality-sql-scale.html

• Gradient Boosted Decision Trees – A Conceptual Explanation

  Gradient boosted decision trees involves implementing several models and aggregating their results. These boosted models have become popular thanks to their performance in machine learning competitions on Kaggle. In this article, we’ll see what gradient boosted decision trees are all about.

  https://www.kdnuggets.com/2021/04/gradient-boosted-trees-conceptual-explanation.html

• FluDemic – using AI and Machine Learning to get ahead of disease

  We are amidst a healthcare data explosion. AI/ML will be more vital than ever in the prevention and handling of future pandemics. Here, we walk you through the different facets of modeling infectious diseases, focusing on influenza and COVID-19.

  https://www.kdnuggets.com/2021/04/fludemic-ai-machine-learning-disease.html

• Data careers are NOT one-size fits all! Tips for uncovering your ideal role in the data space

  Thriving as a data professional is about more than just making good money! It’s about FULFILLMENT & IMPACT. In this article, I will help you discover the BEST data role for you given your unique skill sets, personality & goals.

  https://www.kdnuggets.com/2021/04/data-careers-not-one-size-fits-all.html

• Data Science Books You Should Start Reading in 2021">Data Science Books You Should Start Reading in 2021

  Check out this curated list of the best data science books for any level.

  https://www.kdnuggets.com/2021/04/data-science-books-start-reading-2021.html

• Top 10 Must-Know Machine Learning Algorithms for Data Scientists – Part 1

  New to data science? Interested in the must-know machine learning algorithms in the field? Check out the first part of our list and introductory descriptions of the top 10 algorithms for data scientists to know.

  https://www.kdnuggets.com/2021/04/top-10-must-know-machine-learning-algorithms-data-scientists-1.html

• Data Analysis Using Tableau

  Read this overview of using Tableau for sale data analysis, and see how visualization can help tell the business story.

  https://www.kdnuggets.com/2021/04/data-analysis-using-tableau.html

• Build an Effective Data Analytics Team and Project Ecosystem for Success

  Apply these techniques to create a data analytics program that delivers solutions that delight end-users and meet their needs.

  https://www.kdnuggets.com/2021/04/build-effective-data-analytics-team-project-ecosystem-success.html

• How to organize your data science project in 2021">How to organize your data science project in 2021

  Maintaining proper organization of all your data science projects will increase your productivity, minimize errors, and increase your development efficiency. This tutorial will guide you through a framework on how to keep everything in order on your local machine and in the cloud.

  https://www.kdnuggets.com/2021/04/how-organize-your-data-science-project-2021.html

• What makes a song popular? Analyzing Top Songs on Spotify

  With so many great (and not-so-great) songs out there, it can be hard to find those that match your musical preferences. Follow along this ML model building project to explore the extensive song data available on Spotify and design a recommendation engine that could help you discover your next favorite artist!

  https://www.kdnuggets.com/2021/04/song-popular-analyzing-top-songs-spotify.html

• Continuous Training for Machine Learning – a Framework for a Successful Strategy

  A basic appreciation by anyone who builds machine learning models is that the model is not useful without useful data. This doesn't change after a model is deployed to production. Effectively monitoring and retraining models with updated data is key to maintaining valuable ML solutions, and can be accomplished with effective approaches to production-level continuous training that is guided by the data.

  https://www.kdnuggets.com/2021/04/continuous-training-machine-learning.html

• E-commerce Data Analysis for Sales Strategy Using Python

  Check out this informative and concise case study applying data analysis using Python to a well-defined e-commerce scenario.

  https://www.kdnuggets.com/2021/04/e-commerce-data-analysis-sales-strategy-python.html

• Shapash: Making Machine Learning Models Understandable">Shapash: Making Machine Learning Models Understandable

  Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.

  https://www.kdnuggets.com/2021/04/shapash-machine-learning-models-understandable.html

• The 8 Most Common Data Scientists

  Admit it all you wanna-be, newbie, and old-old-school Data Scientists on the planet, whether you like it or not, you've probably behaved like one of these types. Or two. Or all eight.

  https://www.kdnuggets.com/2021/04/8-most-common-data-scientists.html

• Software Engineering Best Practices for Data Scientists

  This is a crash course on how to bridge the gap between data science and software engineering.

  https://www.kdnuggets.com/2021/03/software-engineering-best-practices-data-scientists.html

• MS in Data Science at Ramapo

  Ramapo College’s Master of Science in Data Science program will teach you to collect, synthesize, and analyze big data, become skilled in programming languages like R and Python, and leverage advanced tools to meet the demands of modern business and science.

  https://www.kdnuggets.com/2021/03/ramapo-ms-data-science.html

• Data Science Curriculum for Professionals

  If you are looking to expand or transition your current professional career that is buried in spreadsheet analysis into one powered by data science, then you are in for an exciting but complex journey with much to explore and master. To begin your adventure, following this complete road map to guide you from a gnome in the forest of spreadsheets to an AI wizard known far and wide throughout the kingdom.

  https://www.kdnuggets.com/2021/03/data-science-curriculum-professionals.html

• 15 Habits I Learned from Highly Effective Data Scientists

  I’m using these habits in 2021 to become a more effective future data scientist.

  https://www.kdnuggets.com/2021/03/15-habits-learned-from-highly-effective-data-scientists.html

• Top 10 Python Libraries Data Scientists should know in 2021">Top 10 Python Libraries Data Scientists should know in 2021

  So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.

  https://www.kdnuggets.com/2021/03/top-10-python-libraries-2021.html

• How to Succeed in Becoming a Freelance Data Scientist">How to Succeed in Becoming a Freelance Data Scientist

  With recent growth in data science, now is the best time to get into freelancing. The following steps will help you get started with finding clients or help you improve your current strategy.

  https://www.kdnuggets.com/2021/03/succeed-becoming-freelance-data-scientist.html

• Top YouTube Machine Learning Channels

  These are the top 15 YouTube channels for machine learning as determined by our stated criteria, along with some additional data on the channels to help you decide if they may have some content useful for you.

  https://www.kdnuggets.com/2021/03/top-youtube-machine-learning-channels.html

• The Best Machine Learning Frameworks & Extensions for Scikit-learn">The Best Machine Learning Frameworks & Extensions for Scikit-learn

  Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.

  https://www.kdnuggets.com/2021/03/best-machine-learning-frameworks-extensions-scikit-learn.html

• How to frame the right questions to be answered using data

  Understanding your data first is a key step before going too far into any data science project. But, you can't fully understand your data until you know the right questions to ask of it.

  https://www.kdnuggets.com/2021/03/right-questions-answered-using-data.html

• Forget Telling Stories; Help People Navigate

  When designing reporting & visualizations, think of them as part of a navigation framework rather than stand-alone information.

  https://www.kdnuggets.com/2021/03/forget-telling-stories-help-people-navigate.html

• Know your data much faster with the new Sweetviz Python library">Know your data much faster with the new Sweetviz Python library

  One of the latest exploratory data analysis libraries is a new open-source Python library called Sweetviz, for just the purposes of finding out data types, missing information, distribution of values, correlations, etc. Find out more about the library and how to use it here.

  https://www.kdnuggets.com/2021/03/know-your-data-much-faster-sweetviz-python-library.html

• Evaluating Object Detection Models Using Mean Average Precision

  In this article we will see see how precision and recall are used to calculate the Mean Average Precision (mAP).

  https://www.kdnuggets.com/2021/03/evaluating-object-detection-models-using-mean-average-precision.html

• Are You Still Using Pandas to Process Big Data in 2021? Here are two better options">Are You Still Using Pandas to Process Big Data in 2021? Here are two better options

  When its time to handle a lot of data -- so much that you are in the realm of Big Data -- what tools can you use to wrangle the data, especially in a notebook environment? Pandas doesn’t handle really Big Data very well, but two other libraries do. So, which one is better and faster?

  https://www.kdnuggets.com/2021/03/pandas-big-data-better-options.html

• Top YouTube Channels for Data Science">Top YouTube Channels for Data Science

  Have a look at the top 15 YouTube channels for data science by number of subscribers, along with some additional data on the channels to help you decide if they may have some content useful for you.

  https://www.kdnuggets.com/2021/03/top-youtube-channels-data-science.html

• Graph Databases, Explained

  Between the four main NoSQL database types, graph databases are widely appreciated for their application in handling large sets of unstructured data coming from various sources. Let’s talk about how graph databases work and what are their practical uses.

  https://www.kdnuggets.com/2021/02/understanding-nosql-database-types-graph.html

• Data Science Learning Roadmap for 2021">Data Science Learning Roadmap for 2021

  Venturing into the world of Data Science is an exciting, interesting, and rewarding path to consider. There is a great deal to master, and this self-learning recommendation plan will guide you toward establishing a solid understanding of all that is foundational to data science as well as a solid portfolio to showcase your developed expertise.

  https://www.kdnuggets.com/2021/02/data-science-learning-roadmap-2021.html

• Pandas Profiling: One-Line Magical Code for EDA

  EDA can be automated using a Python library called Pandas Profiling. Let’s explore Pandas profiling to do EDA in a very short time and with just a single line code.

  https://www.kdnuggets.com/2021/02/pandas-profiling-one-line-magical-code-eda.html

• Powerful Exploratory Data Analysis in just two lines of code">Powerful Exploratory Data Analysis in just two lines of code

  EDA is a fundamental early process for any Data Science investigation. Typical approaches for visualization and exploration are powerful, but can be cumbersome for getting to the heart of your data. Now, you can get to know your data much faster with only a few lines of code... and it might even be fun!

  https://www.kdnuggets.com/2021/02/powerful-exploratory-data-analysis-sweetviz.html

• Inside the Architecture Powering Data Quality Management at Uber

  Data Quality Monitor implements novel statistical methods for anomaly detection and quality management in large data infrastructures.

  https://www.kdnuggets.com/2021/02/inside-architecture-powering-data-quality-management-uber.html

• 10 resources for data science self-study

  Many resources exist for the self-study of data science. In our modern age of information technology, an enormous amount of free learning resources are available to anyone, and with effort and dedication, you can master the fundamentals of data science.

  https://www.kdnuggets.com/2021/02/10-resources-data-science-self-study.html

• A Critical Comparison of Machine Learning Platforms in an Evolving Market

  There’s a clear inclination towards the MLaaS model across industries, given the fact that companies today have an option to select from a wide range of solutions that can cater to diverse business needs. Here is a look at 3 of the top ML platforms for data excellence.

  https://www.kdnuggets.com/2021/02/critical-comparison-machine-learning-platforms-evolving-market.html

• 7 Most Recommended Skills to Learn to be a Data Scientist

  The Data Scientist professional has emerged as a true interdisciplinary role that spans a variety of skills, theoretical and practical. For the core, day-to-day activities, many critical requirements that enable the delivery of real business value reach well outside the realm of machine learning, and should be mastered by those aspiring to the field.

  https://www.kdnuggets.com/2021/02/7-most-recommended-skills-data-scientist.html

• Data Science vs Business Intelligence, Explained">Data Science vs Business Intelligence, Explained

  Knowing the differences between the business intelligence and data science is more than just a matter of semantics.

  https://www.kdnuggets.com/2021/02/data-science-vs-business-intelligence-explained.html

• Deep learning doesn’t need to be a black box">Deep learning doesn’t need to be a black box

  The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this "black box" after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network's architecture -- before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.

  https://www.kdnuggets.com/2021/02/deep-learning-not-black-box.html

• Getting Started with 5 Essential Natural Language Processing Libraries">Getting Started with 5 Essential Natural Language Processing Libraries

  This article is an overview of how to get started with 5 popular Python NLP libraries, from those for linguistic data visualization, to data preprocessing, to multi-task functionality, to state of the art language modeling, and beyond.

  https://www.kdnuggets.com/2021/02/getting-started-5-essential-nlp-libraries.html

• What is Graph Theory, and Why Should You Care?

  Go from graph theory to path optimization.

  https://www.kdnuggets.com/2021/01/graph-theory-why-care.html

• Is M.Tech in Data Science Worth It?

  Is M.Tech in Data Science worth it or should you learn using just online courses and projects. Let's try to find the answer to that question.

  https://www.kdnuggets.com/2021/01/greatlearning-mtech-data-science.html

• The Ultimate Scikit-Learn Machine Learning Cheatsheet">The Ultimate Scikit-Learn Machine Learning Cheatsheet

  With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.

  https://www.kdnuggets.com/2021/01/ultimate-scikit-learn-machine-learning-cheatsheet.html

• Null Hypothesis Significance Testing is Still Useful

  Even in the aftermath of the replication crisis, statistical significance lingers as an important concept for Data Scientists to understand.

  https://www.kdnuggets.com/2021/01/null-hypothesis-significance-testing-useful.html

• Data Engineering — the Cousin of Data Science, is Troublesome">Data Engineering — the Cousin of Data Science, is Troublesome

  A Data Scientist must be a jack of many, many trades. Especially when working in broader teams, understanding the roles of others, such as data engineering, can help you validate progress and be aware of potential pitfalls. So, how can you convince your analysts to realize the importance of expanding their toolkit? Examples from real life often provide great insight.

  https://www.kdnuggets.com/2021/01/data-engineering-troublesome.html

• 8 New Tools I Learned as a Data Scientist in 2020

  The author shares the data science tools learned while making the move from Docker to Live Deployments.

  https://www.kdnuggets.com/2021/01/8-new-tools-learned-data-scientist-2020.html

• My Data Science Learning Journey So Far">My Data Science Learning Journey So Far

  These are some obstacles the author faced in their data science learning journey in the past year, including how much time it took to overcome each obstacle and what it has taught the author.

  https://www.kdnuggets.com/2021/01/data-science-learning-journey.html

• The Best Tool for Data Blending is KNIME

  These are the lessons and best practices I learned in many years of experience in data blending, and the software that became my most important tool in my day-to-day work.

  https://www.kdnuggets.com/2021/01/best-tool-data-blending-knime.html

• Creating Good Meaningful Plots: Some Principles

  Hera are some thought starters to help you create meaningful plots.

  https://www.kdnuggets.com/2021/01/creating-good-meaningful-plots-principles.html

• 5 Tools for Effortless Data Science

  The sixth tool is coffee.

  https://www.kdnuggets.com/2021/01/5-tools-effortless-data-science.html

• Best Python IDEs and Code Editors You Should Know">Best Python IDEs and Code Editors You Should Know

  Developing machine learning algorithms requires implementing countless libraries and integrating many supporting tools and software packages. All this magic must be written by you in yet another tool -- the IDE -- that is fundamental to all your code work and can drive your productivity. These top Python IDEs and code editors are among the best tools available for you to consider, and are reviewed with their noteworthy features.

  https://www.kdnuggets.com/2021/01/best-python-ide-code-editors.html

• 10 Underappreciated Python Packages for Machine Learning Practitioners">10 Underappreciated Python Packages for Machine Learning Practitioners

  Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.

  https://www.kdnuggets.com/2021/01/10-underappreciated-python-packages-machine-learning-practitioners.html

• Learn Data Science for free in 2021">Learn Data Science for free in 2021

  If you are considering starting a career path in machine learning and data science, then there is a great deal to learn theoretically, along with gaining practical skills in applying a broad range of techniques. This comprehensive learning plan will guide you to start on this path, and it is all available for free.

  https://www.kdnuggets.com/2021/01/learn-data-science-free-2021.html

• Six Tips on Building a Data Science Team at a Small Company

  When a company decides that they want to start leveraging their data for the first time, it can be a daunting task. Many businesses aren’t fully aware of all that goes into building a data science department. If you're the data scientist hired to make this happen, we have some tips to help you face the task head-on.

  https://www.kdnuggets.com/2021/01/six-tips-building-data-science-team-small-company.html

• How to easily check if your Machine Learning model is fair?

  Machine learning models deployed today -- as will many more in the future -- impact people and society directly. With that power and influence resting in the hands of Data Scientists and machine learning engineers, taking the time to evaluate and understand if model results are fair will become the linchpin for the future success of AI/ML solutions. These are critical considerations, and using a recently developed fairness module in the dalex Python package is a unified and accessible way to ensure your models remain fair.

  https://www.kdnuggets.com/2020/12/machine-learning-model-fair.html

• Top 9 Data Science Courses to Learn Online

  Learn Data Science from these top courses. Details like cost and course duration are included.

  https://www.kdnuggets.com/2020/12/simplilearn-top-9-data-science-courses-online.html

• Top 2020 Stories: 24 Best (and Free) Books To Understand Machine Learning; If I had to start learning Data Science again, how would I do it?

  Also: Know What Employers are Expecting for a Data Scientist Role in 2020; Top Python Libraries for Data Science, Data Visualization & Machine Learning.

  https://www.kdnuggets.com/2020/12/top-stories-2020.html

• 8 Places for Data Professionals to Find Datasets

  Here is a curated list of sites and resources invaluable for data professionals to acquire practice datasets.

  https://www.kdnuggets.com/2020/12/8-places-data-professionals-find-datasets.html

• Industry 2021 Predictions for AI, Analytics, Data Science, Machine Learning

  We bring you industry predictions from 12 innovative companies - what key trends they expect in 2021 in AI, Analytics, Data Science, and Machine Learning?

  https://www.kdnuggets.com/2020/12/industry-2021-predictions-ai-data-science-machine-learning.html

• How to Create Custom Real-time Plots in Deep Learning

  How to generate real-time visualizations of custom metrics while training a deep learning model using Keras callbacks.

  https://www.kdnuggets.com/2020/12/create-custom-real-time-plots-deep-learning.html

• Data Science Volunteering: Ways to Help

  No matter the field in which you hold some expertise, sharing your skills to benefit the lives of others or supporting non-profit organizations that try to make the world a better place is a noble and time-worthy personal pursuit. Many opportunities exist in data science to contribute to meaningful projects and crucial needs from your local community to a global scale.

  https://www.kdnuggets.com/2020/12/data-science-volunteering.html

• Data Compression via Dimensionality Reduction: 3 Main Methods

  Lift the curse of dimensionality by mastering the application of three important techniques that will help you reduce the dimensionality of your data, even if it is not linearly separable.

  https://www.kdnuggets.com/2020/12/data-compression-dimensionality-reduction.html

• R or Python? Why Not Both?">R or Python? Why Not Both?

  Do you use both R and Python, either in different projects or in the same? Check out prython, an IDE designed to handle your needs.

  https://www.kdnuggets.com/2020/12/r-python-both-prython.html

• 20 Core Data Science Concepts for Beginners">20 Core Data Science Concepts for Beginners

  With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.

  https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html

• 14 Data Science projects to improve your skills

  There's a lot of data out there and so many data science techniques to master or review. Check out these great project ideas from easy to advanced difficulty levels to develop new skills and strengthen your portfolio.

  https://www.kdnuggets.com/2020/12/14-data-science-projects-improve-skills.html

• 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.

  https://www.kdnuggets.com/2020/11/data-science-history-overview.html

• TabPy: Combining Python and Tableau">TabPy: Combining Python and Tableau

  This article demonstrates how to get started using Python in Tableau.

  https://www.kdnuggets.com/2020/11/tabpy-combining-python-tableau.html

• 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.

  https://www.kdnuggets.com/2020/11/fraud-eyes-machine.html

• 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.

  https://www.kdnuggets.com/2020/11/learn-machine-learning-algorithms-effectively.html

• 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.

  https://www.kdnuggets.com/2020/11/udacity-data-science-programs-beginners.html

• 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.

  https://www.kdnuggets.com/2020/11/data-scientists-communicating-management.html

• AI and Automation meets BI">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.

  https://www.kdnuggets.com/2020/11/ai-automation-meets-bi.html

• 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.

  https://www.kdnuggets.com/2020/11/5-useful-machine-learning-tools.html

• Facebook Open Sourced New Frameworks to Advance Deep Learning Research">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.

  https://www.kdnuggets.com/2020/11/facebook-open-source-frameworks-advance-deep-learning-research.html

• Is Data Science for Me? 14 Self-examination Questions to Consider">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.

  https://www.kdnuggets.com/2020/11/data-science-14-self-examination-questions.html

• 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.

  https://www.kdnuggets.com/2020/11/algorithms-for-advanced-hyper-parameter-optimization-tuning.html

• Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision">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.

  https://www.kdnuggets.com/2020/11/top-python-libraries-deep-learning-natural-language-processing-computer-vision.html

• How to Acquire the Most Wanted Data Science Skills">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.

  https://www.kdnuggets.com/2020/11/acquire-most-wanted-data-science-skills.html

• 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.

  https://www.kdnuggets.com/2020/11/mastering-tensorflow-tensors-5-easy-steps.html

• Every Complex DataFrame Manipulation, Explained & Visualized Intuitively">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.

  https://www.kdnuggets.com/2020/11/dataframe-manipulation-explained-visualized.html

• 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.

  https://www.kdnuggets.com/2020/11/data-science-online-learning-journey-coursera.html

• 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?

  https://www.kdnuggets.com/2020/11/interpretability-explainability-machine-learning.html

• Building Deep Learning Projects with fastai — From Model Training to Deployment

  A getting started guide to develop computer vision application with fastai.

  https://www.kdnuggets.com/2020/11/building-deep-learning-projects-fastai-model-training-deployment.html

• Getting A Data Science Job is Harder Than Ever – How to turn that to your advantage

  Although many aspiring Data Scientists are finding it is becoming more difficult to land a job than it was in previous years, understanding what has changed in the hiring landscape can be used to to your advantage in matching with the best organization for your goals and interests.

  https://www.kdnuggets.com/2020/10/getting-data-science-job-harder.html

• Behavior Analysis with Machine Learning and R: The free eBook

  Check out this new free ebook to learn how to leverage the power of machine learning to analyze behavioral patterns from sensor data and electronic records using R.

  https://www.kdnuggets.com/2020/10/behavior-analysis-machine-learning-r-free-ebook.html

• 10 Underrated Python Skills

  Tips for feature analysis, hyperparameter tuning, data visualization and more.

  https://www.kdnuggets.com/2020/10/10-underrated-python-skills.html

• fastcore: An Underrated Python Library">fastcore: An Underrated Python Library

  A unique python library that extends the python programming language and provides utilities that enhance productivity.

  https://www.kdnuggets.com/2020/10/fastcore-underrated-python-library.html

• How to ace the data science coding challenge">How to ace the data science coding challenge

  Preparing to interview for a Data Scientist position takes preparation and practice, and then it could all boil down to a final review of your skills. Based on personal experience, these tips on how to approach such a review will help you excel in the coding challenge project for your next interview.

  https://www.kdnuggets.com/2020/10/ace-data-science-coding-challenge.html

• Top September Stories: Free From MIT: Intro to Computer Science and Programming in Python; Best Online MS in AI, Analytics, Data Science, Machine Learning

  Also: Introduction to Time Series Analysis in Python; Automating Every Aspect of Your Python Project

  https://www.kdnuggets.com/2020/10/top-stories-2020-sep.html

• Uber Open Sources the Third Release of Ludwig, its Code-Free Machine Learning Platform

  The new release makes Ludwig one of the most complete open source AutoML stacks in the market.

  https://www.kdnuggets.com/2020/10/uber-open-source-ludwig-code-free-machine-learning-platform.html

• Comparing the Top Business Intelligence Tools: Power BI vs Tableau vs Qlik vs Domo

  How smart are your organizations’ decisions? Do you have the right information to make those decisions in the first place?

  https://www.kdnuggets.com/2020/10/comparing-top-business-intelligence-tools.html

• The Best Free Data Science eBooks: 2020 Update">The Best Free Data Science eBooks: 2020 Update

  The author has updated their list of best free data science books for 2020. Read on to see what books you should grab.

  https://www.kdnuggets.com/2020/09/best-free-data-science-ebooks-2020-update.html

• How AI is Driving Innovation in Astronomy

  In this blog, we look at a disruptive AI program - Morpheus - developed by Researchers at UC Santa Cruz that can analyze astronomical image data and classify galaxies and stars with surgical precision. If you're reading this with "starry" eyes, we bet we've got you hooked.

  https://www.kdnuggets.com/2020/09/ai-driving-innovation-astronomy.html

• Geographical Plots with Python">Geographical Plots with Python

  When your data includes geographical information, rich map visualizations can offer significant value for you to understand your data and for the end user when interpreting analytical results.

  https://www.kdnuggets.com/2020/09/geographical-plots-python.html

• The Online Courses You Must Take to be a Better Data Scientist

  These select courses have proved to be precious online resources which helped make the author a better data scientist today.

  https://www.kdnuggets.com/2020/09/online-courses-better-data-scientist.html

• Introduction to Time Series Analysis in Python">Introduction to Time Series Analysis in Python

  Data that is updated in real-time requires additional handling and special care to prepare it for machine learning models. The important Python library, Pandas, can be used for most of this work, and this tutorial guides you through this process for analyzing time-series data.

  https://www.kdnuggets.com/2020/09/introduction-time-series-analysis-python.html

• MathWorks Deep learning workflow: tips, tricks, and often forgotten steps

  Getting started in deep learning – and adopting an organized, sustainable, and reproducible workflow – can be challenging. This blog post will share some tips and tricks to help you develop a systematic, effective, attainable, and scalable deep learning workflow as you experiment with different deep learning models, datasets, and applications.

  https://www.kdnuggets.com/2020/09/mathworks-deep-learning-workflow.html

• Implementing a Deep Learning Library from Scratch in Python">Implementing a Deep Learning Library from Scratch in Python

  A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.

  https://www.kdnuggets.com/2020/09/implementing-deep-learning-library-scratch-python.html

• Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities">Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities

  We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.

  https://www.kdnuggets.com/2020/09/online-certificates-ai-data-science-machine-learning-top.html

• Here’s what you need to look for in a model server to build ML-powered services

  More applications are being infused with machine learning while MLOps processes and best practices are becoming well established. Critical to these software and systems are the servers that run the models, which should feature key capabilities to drive successful enterprise-scale productionizing of machine learning.

  https://www.kdnuggets.com/2020/09/model-server-build-ml-powered-services.html

• Feature Engineering for Numerical Data

  Data feeds machine learning models, and the more the better, right? Well, sometimes numerical data isn't quite right for ingestion, so a variety of methods, detailed in this article, are available to transform raw numbers into something a bit more palatable.

  https://www.kdnuggets.com/2020/09/feature-engineering-numerical-data.html

• KDnuggets™ News 20:n34, Sep 9: Top Online Data Science Masters Degrees; Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills

  Also: Creating Powerful Animated Visualizations in Tableau; PyCaret 2.1 is here: What's new?; How To Decide What Data Skills To Learn; How to Evaluate the Performance of Your Machine Learning Model

  https://www.kdnuggets.com/2020/n34.html

• Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills">Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills

  We analyze the results of the Data Science Skills poll, including 8 categories of skills, 13 core skills that over 50% of respondents have, the emerging/hot skills that data scientists want to learn, and what is the top skill that Data Scientists want to learn.

  https://www.kdnuggets.com/2020/09/modern-data-science-skills.html

• Book Chapter: The Art of Statistics: Learning from Data

  Get a free book chapter from "The Art of Statistics: Learning from Data" by a leading researcher Sir David John Spiegelhalter. This excerpt takes a forensic look at data surrounding the victims of the UK most prolific serial killer and shows how a simple search for patterns reveals critical details.

  https://www.kdnuggets.com/2020/09/jmp-art-statistics-learning-from-data.html

• Top Online Masters in Analytics, Business Analytics, Data Science – Updated">Top Online Masters in Analytics, Business Analytics, Data Science – Updated

  We provide an updated list of best online Masters in AI, Analytics, and Data Science, including rankings, tuition, and duration of the education program.

  https://www.kdnuggets.com/2020/09/best-online-masters-data-science-analytics-online.html

• Data is everywhere and it powers everything we do!

  In this article I would like to focus on how companies can start their data-centric strategies and how to achieve success in their data transformation journeys. Have tried to share my thoughts why companies have to consider data at its epitome for their growth, for being competitive, for being smarter, innovative and be prepared for any unforeseen market surprises.

  https://www.kdnuggets.com/2020/08/data-everywhere-powers-everything.html

• Explainable and Reproducible Machine Learning Model Development with DALEX and Neptune

  With ML models serving real people, misclassified cases (which are a natural consequence of using ML) are affecting peoples’ lives and sometimes treating them very unfairly. It makes the ability to explain your models’ predictions a requirement rather than just a nice to have.

  https://www.kdnuggets.com/2020/08/explainable-reproducible-machine-learning-model-development-dalex-neptune.html

• How to Optimize Your CV for a Data Scientist Career">How to Optimize Your CV for a Data Scientist Career

  As the number of data science positions continues to grow dramatically, so does the number of data scientists in the marketplace. Follow these expert tips and examples to help make your resume and job applications stand out in an increasingly competitive field.

  https://www.kdnuggets.com/2020/08/optimize-cv-data-scientist-career.html

• Getting Started with Feature Selection

  For machine learning, more data is always better. What about more features of data? Not necessarily. This beginners' guide with code examples for selecting the most useful features from your data will jump start you toward developing the most effective and efficient learning models.

  https://www.kdnuggets.com/2020/08/getting-started-feature-selection.html

• Data Science Tools Illustrated Study Guides

  These data science tools illustrated guides are broken up into four distinct categories: data retrieval, data manipulation, data visualization, and engineering tips. Both online and PDF versions of these guides are available.

  https://www.kdnuggets.com/2020/08/data-science-tools-illustrated-study-guides.html

• Data Science Meets Devops: MLOps with Jupyter, Git, and Kubernetes

  An end-to-end example of deploying a machine learning product using Jupyter, Papermill, Tekton, GitOps and Kubeflow.

  https://www.kdnuggets.com/2020/08/data-science-meets-devops-mlops-jupyter-git-kubernetes.html

• Rapid Python Model Deployment with FICO Xpress Insight

  The biggest hurdle in the use of data to create business value, is indeed the ability to operationalize analytics throughout the organization. Xpress Insight is geared to reduce the burden on IT and address their critical requirements while empowering business users to take ownership of decisions and change management.

  https://www.kdnuggets.com/2020/08/fico-xpress-insight-python-deployment.html

• Build Your Own AutoML Using PyCaret 2.0

  In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs.

  https://www.kdnuggets.com/2020/08/build-automl-pycaret.html

• These Data Science Skills will be your Superpower">These Data Science Skills will be your Superpower

  Learning data science means learning the hard skills of statistics, programming, and machine learning. To complete your training, a broader set of soft skills will round out your capabilities as an effective and successful professional Data Scientist.

  https://www.kdnuggets.com/2020/08/data-science-skills-superpower.html

• The List of Top 10 Lists in Data Science">The List of Top 10 Lists in Data Science

  The list of Top 10 lists that Data Scientists -- from enthusiasts to those who want to jump start a career -- must know to smoothly navigate a path through this field.

  https://www.kdnuggets.com/2020/08/top-10-lists-data-science.html