- What every Data Scientist needs to learn from Business Leaders - Jul 10, 2020.
You've learned so much to become a Data Scientist. Now, it's time to kick it up to the next level with advanced soft skills -- because these are important to the business for which you empower to make better decisions. Learning from the business leaders you support will help you develop a broader set of enhanced skills that will boost your Data Science quality and output.
- Getting Started with TensorFlow 2 - Jul 2, 2020.
Learn about the latest version of TensorFlow with this hands-on walk-through of implementing a classification problem with deep learning, how to plot it, and how to improve its results.
- How to Build Your Data Science Competency for Post-COVID Future - Jul 1, 2020.
Data science is helping healthcare organizations and businesses navigate the current crisis more effectively. Find out how you can learn this in-demand qualification and help them with addressing complex challenges.
- Software engineering fundamentals for Data Scientists - Jun 30, 2020.
As a data scientist writing code for your models, it's quite possible that your work will make its way into a production environment to be used by the masses. But, writing code that is deployed as software is much different than writing code for exploratory data analysis. Learn about the key approaches for making your code production-ready that will save you time and future headaches.
- How Much Math do you need in Data Science? - Jun 26, 2020.
There exist so many great computational tools available for Data Scientists to perform their work. However, mathematical skills are still essential in data science and machine learning because these tools will only be black-boxes for which you will not be able to ask core analytical questions without a theoretical foundation.
- Five Cognitive Biases In Data Science (And how to avoid them) - Jun 12, 2020.
Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work. Learn more about five biases that can all too easily make your seemingly objective work become surprisingly subjective.
- If you had to start statistics all over again, where would you start? - Jun 5, 2020.
If you are just diving into learning statistics, then where do you begin? Find insight from those who have tread in these waters before, and see what they might have done differently along their personal journeys in statistics.
- How to Rock a Virtual Data Interview - May 26, 2020.
To help you truly rock your next virtual data interview, we’ve pulled together a few tips that we recommend when conducting our online interviews for The Data Incubator’s Data Science Fellowship Program.
- Appropriately Handling Missing Values for Statistical Modelling and Prediction - May 22, 2020.
Many statisticians in industry agree that blindly imputing the missing values in your dataset is a dangerous move and should be avoided without first understanding why the data is missing in the first place.
- An easy guide to choose the right Machine Learning algorithm - May 21, 2020.
There's no free lunch in machine learning. So, determining which algorithm to use depends on many factors from the type of problem at hand to the type of output you are looking for. This guide offers several considerations to review when exploring the right ML approach for your dataset.
- What they do not tell you about machine learning - May 19, 2020.
There's a lot of excitement out there about machine learning jobs. So, it's always good to start off with a healthy dose of reality and proper expectations.
- Will Machine Learning Engineers Exist in 10 Years? - May 8, 2020.
As can be common in many technical fields, the landscape of specialized roles is evolving quickly. With more people learning at least a little machine learning, this could eventually become a common skill set for every software engineer.
- Should Data Scientists Model COVID19 and other Biological Events - Apr 22, 2020.
Biostatisticians use statistical techniques that your current everyday data scientists have probably never heard of. This is a great example where lack of domain knowledge exposes you as someone that does not know what they are doing and are merely hopping on a trend.
- Peer Reviewing Data Science Projects - Apr 13, 2020.
In any technical development field, having other practitioners review your work before shipping code off to production is a valuable support tool to make sure your work is error-proof. Even through your preparation for the review, improvements might be discovered and then other issues that escaped your awareness can be spotted by outsiders. This peer scrutiny can also be applied to Data Science, and this article outlines a process that you can experiment with in your team.
- 5 Ways Data Scientists Can Help Respond to COVID-19 and 5 Actions to Avoid - Apr 6, 2020.
How can data scientists help with the COVID-19 response within their organization and more broadly? While there are many valuable and interesting opportunities to apply your skills, there can be unintended consequences even from your best attempt. So, consider this general advice for data scientists who want to help with this and any disaster response.
- A Layman’s Guide to Data Science. Part 2: How to Build a Data Project - Apr 2, 2020.
As Part 2 in a Guide to Data Science, we outline the steps to build your first Data Science project, including how to ask good questions to understand the data first, how to prepare the data, how to develop an MVP, reiterate to build a good product, and, finally, present your project.
- Nine lessons learned during my first year as a Data Scientist - Mar 20, 2020.
What is it like to be a Data Scientist? There can be many hats to wear, and so many problems to solve that are fed with data, churned by data science, and guided by business results. Find out about lessons learned from one Data Scientist about how best to work and perform in the role.
- Data Science Curriculum for self-study - Feb 26, 2020.
Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary for the job.
- Learning from 3 big Data Science career mistakes - Feb 25, 2020.
Thinking of data science as merely a technical profession, like programming, may take you away from your goals. We explain big mistakes to avoid, including not understanding the 2 cultures of statistics, and not understanding the shift to industrial focus.
- Scaling the Wall Between Data Scientist and Data Engineer - Feb 17, 2020.
The educational and research focuses of machine learning tends to highlight the model building, training, testing, and optimization aspects of the data science process. To bring these models into use requires a suite of engineering feats and organization, a standard for which does not yet exist. Learn more about a framework for operating a collaborative data science and engineering team to deploy machine learning models to end-users.
- Why Did I Reject a Data Scientist Job? - Feb 12, 2020.
Snagging that job as a Data Scientist might not be exactly what you were expecting. Consider this advice on carefully considering job titles with what the position might really be like day-to-day.
- How to learn data science on your own: a practical guide - Feb 11, 2020.
While much focus today is on the rise in working from home and the challenges experienced, not as much is said about learning from home. For those lone wolfs studying Data Science in a self-directed way, a range of issues can get in the way of your goal. Learn about these common problems to prepare to focus yourself all the way to your educational goals.
- How to land a Data Scientist job at your dream company - Jan 31, 2020.
Job hunting for anyone just starting out as a data scientist can require grit, passion, and perseverance before finding the best opportunity. Follow this career search journey to learn what it took -- and the learning resources used -- to land the dream job.
- 2 Questions for a Junior Data Scientist - Jan 24, 2020.
Academic credentials and experience with previous machine learning projects are important for kicking off a data science career. However, landing your first job out of school will require you to extend your thinking about projects and problems. Learn how one interviewer honed in on desired skills by considering these two questions.
- I wanna be a data scientist, but… how? - Jan 20, 2020.
It’s easy to say "I wanna be a data scientist," but... where do you start? How much time is needed to be desired by companies? Do you need a Master’s degree? Do you need to know every mathematical concept ever derived? The journey might be long, but follow this plan to help you keep moving forward toward your career goal.
- 7 Resources to Becoming a Data Engineer - Jan 7, 2020.
An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.
- Accuracy vs Speed – what Data Scientists can learn from Search - Jan 2, 2020.
Delivering accurate insights is the core function of any data scientist. Navigating the development road toward this goal can sometimes be tricky, especially when cross-collaboration is required, and these lessons learned from building a search application will help you negotiate the demands between accuracy and speed.
- What is the most important question for Data Science (and Digital Transformation) - Dec 31, 2019.
With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams.
- How To “Ultralearn” Data Science: summary, for those in a hurry - Dec 30, 2019.
For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science.
- How To “Ultralearn” Data Science: deep understanding and experimentation, Part 4 - Dec 27, 2019.
In this fourth and final part of the ultralearning data science series, it's time to take the final steps toward developing a deep understanding of the fundamentals and learning how to experiment -- the two aspects that are the ultimate keys to ultralearning.
- How To “Ultralearn” Data Science: optimization learning, Part 3 - Dec 20, 2019.
This third part in a series about how to "ultralearn" data science will guide you through how to optimize your learning through five valuable techniques.
- The 4 fastest ways NOT to get hired as a data scientist - Dec 18, 2019.
Ready to try to get hired as a data scientist for the first time? Avoiding these common mistakes won’t guarantee an offer, but not avoiding them is a sure fire way for your application to be tossed into the trash bin.
- Open Source Projects by Google, Uber and Facebook for Data Science and AI - Nov 28, 2019.
Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.
- KDnuggets™ News 19:n45, Nov 27: Interpretable vs black box models; Advice for New and Junior Data Scientists - Nov 27, 2019.
This week: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead; Advice for New and Junior Data Scientists; Python Tuples and Tuple Methods; Can Neural Networks Develop Attention? Google Thinks they Can; Three Methods of Data Pre-Processing for Text Classification
- Would you buy insights from this guy? (How to assess and manage a Data Science vendor) - Nov 25, 2019.
With all the hype from data science vendors selling "actionable insights" to boost your company's bottom line, selecting your analytics partner should proceed through the same, careful process as any traditional business endeavor. Follow these questions and best practices to ensure you manage accordingly.
- Advice for New and Junior Data Scientists - Nov 22, 2019.
If you are a new Data Scientist early in your professional journey, and you’re a bit confused and lost, then follow this advice to figure out how to best contribute to your company.
- How I Got Better at Machine Learning - Nov 13, 2019.
Check out this author's collection of tips and tricks that I learned over the years to get better at Machine Learning.
- Bye Data Scientists, Hello AI? Not Likely! - Oct 22, 2019.
AI is becoming more mainstream. The fact that computers/robots will learn after being built and will surpass a human's intelligence is terrifying.
- 5 Tips for Novice Freelance Data Scientists - Oct 18, 2019.
If you want to launch your data science skills into freelance work, then check out these important tips to help you kick start your next adventure in data.
- 8 Paths to Getting a Machine Learning Job Interview - Oct 10, 2019.
While you may be focused on your performance during your next job interview, landing that interview can be just as hard. Check out these tips for finding and securing an interview for a machine learning job.
- 6 bits of advice for Data Scientists - Sep 25, 2019.
As a data scientist, you can get lost in your daily dives into the data. Consider this advice to be certain to follow in your work for being diligent and more impactful for your organization.
- Classification vs Prediction - Sep 12, 2019.
It is important to distinguish prediction and classification. In many decision-making contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions.
- KDnuggets™ News 19:n34, Sep 11: I wasn’t getting hired as a Data Scientist. So I sought data on who is - Sep 11, 2019.
How one person overcame rejections applying to Data Scientist positions by getting actual data on who is getting hired; Advice from Andrew Ng on building ML career and reading research papers; 10 Great Python resources for Data Scientists; Python Libraries for Interpretable ML.
- Starting out in Data Science? Top tips and advice from DataScienceGO Speakers - Sep 3, 2019.
DataScienceGO returns to San Diego Sep 27-29, for a three-day career-focused conference designed to unite newcomers, practitioners, managers and executives under one umbrella, speakers weigh in on how to forge the best teams, increase your hiring chances, and prepare for the future.
- 6 Tips for Building a Training Data Strategy for Machine Learning - Sep 2, 2019.
Without a well-defined approach for collecting and structuring training data, launching an AI initiative becomes an uphill battle. These six recommendations will help you craft a successful strategy.
- The secret sauce for growing from a data analyst to a data scientist - Aug 27, 2019.
Despite the increasing demand and appetite for experienced data scientists, the job is ambiguously described most of the times. Also, the delineation between data science and data analytics or engineering is still loosely defined by a lot of hiring managers.
- Manual Coding or Automated Data Integration – What’s the Best Way to Integrate Your Enterprise Data? - Aug 19, 2019.
What’s the best way to execute your data integration tasks: writing manual code or using ETL tool? Find out the approach that best fits your organization’s needs and the factors that influence it.
- How to Become More Marketable as a Data Scientist - Aug 16, 2019.
As a data scientist, you are in high demand. So, how can you increase your marketability even more? Check out these current trends in skills most desired by employers in 2019.
- Statistical Modelling vs Machine Learning - Aug 14, 2019.
At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.
- Ten more random useful things in R you may not know about - Jul 31, 2019.
I had a feeling that R has developed as a language to such a degree that many of us are using it now in completely different ways. This means that there are likely to be numerous tricks, packages, functions, etc that each of us use, but that others are completely unaware of, and would find useful if they knew about them.
- 12 Things I Learned During My First Year as a Machine Learning Engineer - Jul 23, 2019.
Learn about the day-in-the-life of one machine learning engineer and the important lessons learned for being successful in that role.
- How to Showcase the Impact of Your Data Science Work - Jul 10, 2019.
You're a Data Scientist -- or preparing to land your first job -- and communicating your work to others, especially employers, so they understand your impact is essential. These five tips will help you help others appreciate your data science.
- What’s wrong with the approach to Data Science? - Jul 10, 2019.
The job ‘Data Scientist’ has been around for decades, it was just not called “Data Scientist”. Statisticians have used their knowledge and skills using machine learning techniques such as Logistic Regression and Random Forest for prediction and insights for longer than people actually realize.
- Why you’re not a job-ready data scientist (yet) - Jul 9, 2019.
Trying to snag a dream Data Science job, but can't seem to land one? Check out these four skills that companies really want and be prepared for your next interview.
- Nvidia’s New Data Science Workstation — a Review and Benchmark - Jul 3, 2019.
Nvidia has recently released their Data Science Workstation, a PC that puts together all the Data Science hardware and software into one nice package. The workstation is a total powerhouse machine, packed with all the computing power — and software — that’s great for plowing through data.
- Top KDnuggets Tweets, Jun 19 – 25: Learn how to efficiently handle large amounts of data using #Pandas; The biggest mistake while learning #Python for #datascience - Jun 26, 2019.
Also: Data Science Jobs Report 2019; Harvard CS109 #DataScience Course, Resources #Free and Online; Google launches TensorFlow; Mastering SQL for Data Science
- The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph - Jun 25, 2019.
Before being able to develop a Data Fabric we need to build a Knowledge-Graph. In this article I’ll set up the basis on how to create it, in the next article we’ll go to the practice on how to do this.
- Data Literacy: Using the Socratic Method - Jun 20, 2019.
How can organizations and individuals promote Data Literacy? Data literacy is all about critical thinking, so the time-tested method of Socratic questioning can stimulate high-level engagement with data.
- Ten random useful things in R that you might not know about - Jun 20, 2019.
Because the R ecosystem is so rich and constantly growing, people can often miss out on knowing about something that can really help them in a task that they have to complete
- Top KDnuggets Tweets, Jun 12 – 18: The biggest mistake while learning #Python for #datascience; 5 practical statistical concepts for data scientists - Jun 19, 2019.
Also: Resources for developers transitioning into data science; Best Data Visualization Techniques for small and large data; Top Data Science and Machine Learning Methods Used in 2018, 2019
- How to Learn Python for Data Science the Right Way - Jun 14, 2019.
The biggest mistake you can make while learning Python for data science is to learn Python programming from courses meant for programmers. Avoid this mistake, and learn Python the right way by following this approach.
- Show off your Data Science skills with Kaggle Kernels - Jun 14, 2019.
Kaggle is not just about data science competitions. They also have a platform called Kaggle Kernels, using which you can build a stellar data science portfolio.
- All Models Are Wrong – What Does It Mean? - Jun 12, 2019.
During your adventures in data science, you may have heard “all models are wrong.” Let’s unpack this famous quote to understand how we can still make models that are useful.
- The Data Fabric for Machine Learning Part 1-b – Deep Learning on Graphs - Jun 11, 2019.
Deep learning on graphs is taking more importance by the day. Here I’ll show the basics of thinking about machine learning and deep learning on graphs with the library Spektral and the platform MatrixDS.
- If you’re a developer transitioning into data science, here are your best resources - Jun 11, 2019.
This article will provide a background on the data scientist role and why your background might be a good fit for data science, plus tangible stepwise actions that you, as a developer, can take to ramp up on data science.
- Using the ‘What-If Tool’ to investigate Machine Learning models - Jun 6, 2019.
The machine learning practitioner must be a detective, and this tool from teams at Google enables you to investigate and understand your models.
- How to choose a visualization - Jun 4, 2019.
Visualizations based on the structure of data are needed during analysis, which might be different than for the end user. A new guide for choosing the right visualization helps you flexibly understand the data first.
- Data Scientists Are Thinkers: Execution vs. exploration and what it means for you - Jun 4, 2019.
Data scientists serve a very technical purpose, but one that is vastly different from other individual contributors. Unlike engineers, designers, and project managers, data scientists are exploration-first, rather than execution-first.
- Becoming a Level 3.0 Data Scientist - May 29, 2019.
Want to be a Junior, Senior, or Principal Data Scientists? Find out what you need to do to navigate the Data Science Career Game.
- The Data Fabric for Machine Learning – Part 1 - May 21, 2019.
How the new advances in semantics and the data fabric can help us be better at Machine Learning
- What’s Going to Happen this Year in the Data World - May 14, 2019.
"If we wish to foresee the future of mathematics, our proper course is to study the history and present condition of the science." Henri Poncairé.
- What my first Silver Medal taught me about Text Classification and Kaggle in general? - May 13, 2019.
A first-hand account of ideas tried by a competitor at the recent kaggle competition 'Quora Insincere questions classification', with a brief summary of some of the other winning solutions.
- Data Science vs. Decision Science - May 7, 2019.
Data science and decision science are related but still separate fields, so at some points, it might be hard to compare them directly. We attempted to show our vision of the commonalities, differences, and specific features of data science and decision science.
- The Third Wave Data Scientist - May 6, 2019.
An extensive look at what skills are needed to make up the portfolio of the third wave of data scientists.
- The 3 Biggest Mistakes on Learning Data Science - May 6, 2019.
Data science or whatever you want to call it is not just knowing some programming languages, math, statistics and have “domain knowledge” and here I show you why.
- Was it Worth Studying a Data Science Masters? - Apr 23, 2019.
As I started to apply for Data Science roles it quickly became apparent that I was lacking two key skills: applying Machine Learning and coding
- How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists with Serious Guides - Apr 22, 2019.
To learn ALL the skills sets in data science is next to impossible as the scope is way too wide. There’ll always be some skills (technical/non-technical) that data scientists don’t know or haven’t learned as different businesses require different skill sets.
- Data Visualization in Python: Matplotlib vs Seaborn - Apr 19, 2019.
Seaborn and Matplotlib are two of Python's most powerful visualization libraries. Seaborn uses fewer syntax and has stunning default themes and Matplotlib is more easily customizable through accessing the classes.
- 3 Big Problems with Big Data and How to Solve Them - Apr 18, 2019.
We discuss some of the negatives of using big data, including false equivalences and bias, vulnerability to security breaches, protecting against unauthorized access and the lack of international standards for data privacy regulations.
- How to build a technology narrative for early career data and analytics talent acquisition - Apr 11, 2019.
We provide advice for companies in industries still going through a digital transformation on how they can start to understand the problem that Data and Analytics professionals can help solve.
- Advice for New Data Scientists - Apr 8, 2019.
We provide advice for junior data scientists as they begin their career, with tips and commentary from a tech lead at Airbnb.
- The Deep Learning Toolset — An Overview - Mar 28, 2019.
We are observing an increasing number of great tools that help facilitate the intricate process that is deep learning, making it both more accessible and more efficient.
- Data Science for Decision Makers: A Discussion with Dr Stelios Kampakis - Mar 26, 2019.
This article contains an interview veteran data scientist, Dr Stylianos (Stelios) Kampakis, in which he discusses his career, and how he helps decision makers across a range of businesses understand how data science can benefit them.
- My Best Tips for Agile Data Science Research - Mar 21, 2019.
This post demonstrates how to bring maximum value in minimal time using agile methods in data science research.
- How To Work In Data Science, AI, Big Data - Mar 18, 2019.
There are many facets to working in Data Science. Your role will depend greatly on the industry you pick and the area of Data Science you want to pursue. A Data Science career is very dynamic and requires a team effort to succeed.
- What no one will tell you about data science job applications - Mar 1, 2019.
For every person who has a question relating to a data science job application, and asks it, there are ten people who have the same question, but don’t ask it. If you’re one of those ten, then this post is for you.
- Learn How to Listen: One of the hardest parts of being a data scientist - Feb 15, 2019.
Listen, Be Humble, Be Present and Transform ideas. A Data Scientist will spend a large amount of their time in meetings where you can understand the business, the goals of the area, their KPIs, and their requirements.
- The Best and Worst Data Visualizations of 2018 - Feb 8, 2019.
We reflect on some of the best examples of Data Visualization throughout 2018, before focussing on some of the not-so-good and how these can be improved.
- 6 Data Visualization Disasters – How to Avoid Them - Feb 5, 2019.
If you intend to use data visualizations in a presentation or publication, be certain that your audience will understand and trust the information. Here are six mistakes you will want to avoid.
- Aspiring Researchers, Engineers, and Entrepreneurs interested in data: This Book is for You - Feb 1, 2019.
Making Databases Work is a collection of chapters written by leading database researcher and enterpreneur Michael Stonebraker and 38 of his collaborators: world-leading database researchers, world-class systems engineers, and business partners.
- 6 Goals Every Wannabe Data Scientist Should Make for 2019 - Nov 22, 2018.
Looking to embark on a new path as a data scientist? That goal may be worthy, but it's essential for people to also set goals for 2019 that will help them get closer to that broader aim.
- Select Your Analytics Adventure – Analytics On-boarding - Oct 15, 2018.
Lower the barriers to productivity by employing a “Choose your own adventure” approach to on-boarding your new analytics team members.
- Seven Practical Ideas For Beginner Data Scientists - Aug 7, 2018.
As someone who has been there, I’d like to outline a few practical ideas to help junior data scientists get started at a small software company. The steps were drawn from my personal journey and that of others before me.
- How to Build a Data Science Portfolio - Jul 25, 2018.
This post will include links to where various data science professionals (data science managers, data scientists, social media icons, or some combination thereof) and others talk about what to have in a portfolio and how to get noticed.
- How to spot a beginner Data Scientist - Jun 15, 2018.
When beginning life as a data scientist, there are some clear signs that give it away...
- Advice For Applying To Data Science Jobs - Jun 13, 2018.
A comprehensive guide to applying for a job in data science, covering the application, interview and offer stage.
- 6 Tips for Effective Visualization with Tableau - May 29, 2018.
We analyse principles for effective data visualization in Tableau, including color gradients, avoiding crowded dashboards, Tableau marks and more.
- 6 Proven Steps to Land a Job in Data Science - May 21, 2018.
What are the critical steps to get a job in data science? We share the proven formula that helped many data enthusiasts secure job offers as data scientist/analyst, data engineer and machine learning engineer.
- KDnuggets™ News 18:n19, May 9: KDnuggets Poll: What tools you used for Analytics/Data Science Projects? 8 Useful Advices for Aspiring Data Scientists - May 9, 2018.
Also: Boost your data science skills. Learn linear algebra; Apache Spark: Python vs. Scala; Getting Started with spaCy for Natural Language Processing.
- 8 Useful Advices for Aspiring Data Scientists - May 4, 2018.
I recently read Sebastian Gutierrez’s “Data Scientists at Work”, in which he interviewed 16 data scientists. I want to share the best answers that these data scientists gave for the question: "What advice would you give to someone starting out in data science?"
- To Kaggle Or Not - May 2, 2018.
Kaggle is the most well known competition platform for predictive modeling and analytics. This article looks into the different aspects of Kaggle and the benefits it can bring to data scientists.
- Hedge Yourself From a Risky Data Science Job - Apr 18, 2018.
This article covers why it's important to consider all the factors when being hired as a data scientist.
- Don’t learn Machine Learning in 24 hours - Apr 13, 2018.
When it comes to machine learning, there's no quick way of teaching yourself - you're in it for the long haul.
- Onboarding Your Machine Learning Program - Apr 12, 2018.
Machine Learning's popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it's full potential.
- Why Data Scientists Must Focus on Developing Product Sense - Apr 6, 2018.
Data Scientists should focus on developing product sense to move fast and systematically, create models that are relevant and to able to know when to stop.
- KDnuggets™ News 18:n14, Apr 4: How Do I Get My First Data Science Job? A “Weird” Intro to Deep Learning; Top 20 DL papers - Apr 4, 2018.
Also: A Day in the Life of a Data Scientist: Part 4; Understanding Feature Engineering: Deep Learning Methods for Text Data.
- How To Choose The Right Chart Type For Your Data - Apr 3, 2018.
The power of charts to assist in accurate interpretation is massive and that's why it is vital to select the correct type when you are trying to visualize data.
- How Do I Get My First Data Science Job? - Apr 2, 2018.
Here are the steps you need to obtain your first job in data science, including details on how to create a good portfolio, key networking tips, getting the right education and managing expectations.
- A Day in the Life of a Data Scientist: Part 4 - Apr 2, 2018.
Interested in what a data scientist does on a typical day of work? Each data science role may be different, but these contributors have insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
- 8 Common Pitfalls That Can Ruin Your Prediction - Mar 21, 2018.
A good prediction can help your work and make it easier. But how can you be sure that your prediction is good? Here are some common pitfalls that you should avoid.
- Your free 70-page guide to a career in data science - Mar 16, 2018.
To help you become a Data Scientist, we put together a guide with answers to: how do you break into the profession? What skills do you need to become a data scientist? Where are best data science jobs?
- So, How Many Machine Learning Models You Have NOT Built? - Mar 14, 2018.
Investigating how data scientists approach machine learning and applying this to the 'ship repair man' analogy.
- 5 Things to Know Before Rushing to Start in Data Science - Mar 13, 2018.
Strong math understanding, computing skills, critical thinking and presentations skills provide a strong foundation for a career in Data Science.
- The Two Sides of Getting a Job as a Data Scientist - Mar 7, 2018.
Are you a Data Scientist looking for a Job? Are you a Recruiter looking for a Data Scientist? If you answered yes or NO to this questions you need to read this.
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- How to Survive Your Data Science Interview - Mar 1, 2018.
There are many wonderful things about data science. It’s extreme breadth is not one of them. The title of data scientist means something different at every company
- Want a Job in Data? Learn This - Feb 19, 2018.
Why mastering a 50-year-old programming language is the key to getting a data science job.
- KDnuggets™ News 18:n05, Jan 31: Feynman Technique to become a Data Scientist; 4 Big Data Trends for 2018; Data Scientist – best job in America - Jan 31, 2018.
Also How To Grow As A Data Scientist; A Beginner Guide to Data Engineering; Exclusive Interview: Doug Laney on Big Data and Infonomics
- How to Make Life Easy for a Newly Hired Data Scientist - Jan 30, 2018.
In this post, I am going to describe the life of a newly hired data scientist. The use case is that the data scientist is given a project where he needs to build an online learning model.
- 5 Key Data Science Job Market Trends - Jan 26, 2018.
As a data scientist — or someone interested in the field — you know the industry is constantly evolving. If you want to remain competitive, you need to keep up with popular trends.
- How To Grow As A Data Scientist - Jan 25, 2018.
In order for a data scientist to grow, they need to be challenged beyond the technical aspects of their jobs. They need to question their data sources, be concise in their insights, know their business and help guide their leaders.
- Want to Become a Data Scientist? Try Feynman Technique - Jan 24, 2018.
Get over the impostor syndrome by developing a strong understanding about the various Data Science topics using the Feynman Technique
- Becoming a Data Scientist - Jan 9, 2018.
This article contains a lot of links to resources that I think are very helpful in getting you started to "think like a data scientist" which in my opinion is the most important step of the transition. I hope that you find this useful.
- Data Science for Laymen: 5 Writers Who Speak Your Language - Dec 28, 2017.
Here are 5 excellent Data Scientists who are also very good at explaining concepts and interacting with you.
- Yet Another Day in the Life of a Data Scientist - Dec 25, 2017.
Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these four individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
- Another Day in the Life of a Data Scientist - Dec 11, 2017.
Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
- Using Deep Learning to Solve Real World Problems - Dec 4, 2017.
Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.
- Stop Doing Fragile Research - Nov 17, 2017.
If you develop methods for data analysis, you might only be conducting gentle tests of your method on idealized data. This leads to “fragile research,” which breaks when released into the wild. Here, I share 3 ways to make your methods robust.
- A Day in the Life of a Data Scientist - Nov 13, 2017.
Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
- KDnuggets™ News 17:n43, Nov 8: Peak Demand for Data Scientists/Machine Learning Experts – When? Advice For New and Junior Data Scientists - Nov 8, 2017.
Also: 3 different types of machine learning; Want to know how Deep Learning works? Here's a quick guide to Deep Learning; Blockchain Key Terms, Explained.
- Advice For New and Junior Data Scientists - Nov 2, 2017.
This article is for people who are already in the field but are just starting out. My goal is to not only use this post as a reminder to myself about the important things that I have learned, but also to inspire others as they embark onto their DS careers!
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