Data Science Tech Brief By HackerNoon

Data Science Tech Brief By HackerNoon

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Data Science Tech Brief By HackerNoon episodes

  • How to Fix 'zsh: command not found: python'

    This story was originally published on HackerNoon at: https://hackernoon.com/how-to-fix-zsh-command-not-found-python.


    This can happen on any system but does occur slightly more commonly on macOS since they removed native python support in macOS 12.3.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #python-programming, #python, #python-tutorials, #programming, #debug, #python-tips, #python-developers, #python3, #hackernoon-es, and more.


    This story was written by: @smpnjn. Learn more about this writer by checking @smpnjn's about page,
    and for more stories, please visit hackernoon.com.


    MacOS has removed native python support in MacOS 12.3.3. The issue is easy to fix. Make sure Python is installed and add python to zsh so that it will run upon typing the `python` command. If you are still facing issues, ensure that `python=$` where the $ sign should equal the path `Python` is installed on. If you still face the same issue, move to step 2. Add python to your zsh profile to run `/usr/bin/python3` when `python is run.

    2 min
  • Random Forest Regression in R: Code and Interpretation

    This story was originally published on HackerNoon at: https://hackernoon.com/random-forest-regression-in-r-code-and-interpretation.


    This story looks into random forest regression in R, focusing on understanding the output and variable importance.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-science, #random-forest, #regression, #variable-importance, #decision-tree, #ensemble-modeling, #blogging-fellowship, #hackernoon-top-story, #hackernoon-es, and more.


    This story was written by: @nikolao. Learn more about this writer by checking @nikolao's about page,
    and for more stories, please visit hackernoon.com.


    Random forest is one of the most popular algorithms for multiple machine learning tasks. This story looks into random forest regression in R, focusing on understanding the output and variable importance. The package with the original implemetation is called randomForest.

    5 min
  • 9 Best Data Engineering Courses You Should Take in 2023

    This story was originally published on HackerNoon at: https://hackernoon.com/9-best-data-engineering-courses-you-should-take-in-2022.


    In this listicle, you'll find some of the best data engineering courses, and career paths that can help you jumpstart your data engineering journey!
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-engineering, #data-warehouses, #aws-certification, #data-engineering-courses, #data-science, #artificial-intelligence, #hackernoon-top-story, #blogging-fellowship, and more.


    This story was written by: @balapriya. Learn more about this writer by checking @balapriya's about page,
    and for more stories, please visit hackernoon.com.


    Recently, data engineering has become an increasingly coveted space. With an average salary of over 112K USD, the demand for skilled data engineers is growing with every passing day. Data engineers combine their data and software engineering expertise to facilitate the data infrastructure of an organization.
    Are you an aspiring data engineer, or someone with experience in the data space—looking to pivot into data engineering? 
    In this list, you'll find some of the best data engineering courses and career paths that can help you jumpstart your data engineering journey!

    9 min
  • A Beginner's Guide to Understanding Unstructured Data Analysis with LangChain and DeepInfra

    This story was originally published on HackerNoon at: https://hackernoon.com/a-beginners-guide-to-understanding-unstructured-data-analysis-with-langchain-and-deepinfra.


    Let's learn how to extract insights from unstructured data with LangChain and DeepInfra.

    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-science, #ai, #artificial-intelligence, #guide, #tut, #python, #programming, #big-data, and more.


    This story was written by: @mikeyoung44. Learn more about this writer by checking @mikeyoung44's about page,
    and for more stories, please visit hackernoon.com.


    LangChain and DeepInfra are powerful tools for unstructured data analysis. We'll explore their capabilities, understand the importance of data-driven decisions, and learn how to extract valuable insights. Get ready to uncover hidden patterns and make informed choices using these powerful tools.

    6 min
  • How To Plot A Decision Boundary For Machine Learning Algorithms in Python

    This story was originally published on HackerNoon at: https://hackernoon.com/how-to-plot-a-decision-boundary-for-machine-learning-algorithms-in-python-3o1n3w07.


    Classification algorithms learn how to assign class labels to examples (observations or data points), although their decisions can appear opaque.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-science, #machine-learning, #python3, #python-programming, #python, #python-top-story, #python-tutorials, #python-developers, #hackernoon-es, and more.


    This story was written by: @kvssetty. Learn more about this writer by checking @kvssetty's about page,
    and for more stories, please visit hackernoon.com.


    How To Plot A Decision Boundary For Machine Learning Algorithms in Python is a popular diagnostic for understanding the decisions made by a classification algorithm is the decision surface. This is a plot that shows how a trained machine learning algorithm predicts a coarse grid across the input feature space. A decision surface plot is a powerful tool for understanding how a given model ‘sees’ the prediction task and how it has decided to divide up the feature space by class label. The complete source code is available at my git repository.

    11 min
  • Demystifying Dimensional Modelling: Unveiling the What, Why, and Who's

    This story was originally published on HackerNoon at: https://hackernoon.com/demystifying-dimensional-modelling-unveiling-the-what-why-and-whos.


    An Introduction to the art and science of dimensional modeling with relational databases
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-science, #data, #database, #data-engineering, #big-data, #dimensional-modeling, #kimball, #relational-database, and more.


    This story was written by: @disa. Learn more about this writer by checking @disa's about page,
    and for more stories, please visit hackernoon.com.


    Dimensional modelling is a database design philosophy. It is the most widely used style of relational database. It has all the basic ingredients of a relational database i.e Primary keys, Foreign Keys and multiple tables. It’s different from your 3NF relational database majorly because of it's ease of understanding and its superior query performance.

    5 min
  • Who is a Data Engineer and What Do They Do

    This story was originally published on HackerNoon at: https://hackernoon.com/who-is-a-data-engineer-and-what-do-they-do.


    As a data engineer, your job involves handling lots of information (we call it data).
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-engineer, #data-engineer-role, #data-engineer-responsbility, #big-data-engineer, #data, #data-science, #big-data, #database, and more.


    This story was written by: @satyapasupuleti. Learn more about this writer by checking @satyapasupuleti's about page,
    and for more stories, please visit hackernoon.com.


    As a data engineer, your job involves handling lots of information (we call it data). You need to think about where all this information is coming from, what it looks like, and how it might need to be changed or fixed up. You also need to think about where it's going and what questions it can help answer.

    5 min
  • From Crashing to Lift-Off: How to Thrive as the First Data Scientist in a Startup

    This story was originally published on HackerNoon at: https://hackernoon.com/from-crashing-to-lift-off-how-to-thrive-as-the-first-data-scientist-in-a-startup.


    This article draws from the game Factorio to illustrate the journey of a data scientist in a startup - from the initial, hands-on stage, moving towards automati
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-science, #startup, #professional-development, #startup-advice, #tech-careers, #career-advice, #team-productivity, #personal-development, and more.


    This story was written by: @breus. Learn more about this writer by checking @breus's about page,
    and for more stories, please visit hackernoon.com.


    This piece utilizes the game Factorio as a metaphor for a data scientist's progression in a startup, spanning four stages: Manual/Foundation, Initial Automation, Scale, and Flight. Each stage represents different facets of the journey - from scrappy, hands-on work, automating routine tasks, scaling for growth, to evolving in response to changing landscapes.

    18 min
  • Data-driven Marketing: Unleashing the Power of Big Data for Targeted Campaigns

    This story was originally published on HackerNoon at: https://hackernoon.com/data-driven-marketing-unleashing-the-power-of-big-data-for-targeted-campaigns.


    In today's digital era, the abundance of data has transformed the way businesses approach marketing.
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #big-data, #data-driven-marketing, #digital-marketing, #targeted-advertisement, #targeted-campaign, #power-of-happy-customers, #data-science, #data-analytics, and more.


    This story was written by: @mubaywrites. Learn more about this writer by checking @mubaywrites's about page,
    and for more stories, please visit hackernoon.com.


    Big data provides marketers with a treasure trove of information. By tapping into this wealth of data, businesses can better understand their customers, make informed decisions, and develop targeted campaigns. The benefits of data-driven marketing are far-reaching, from increased customer engagement and loyalty to improved conversion rates.

    18 min
  • The AI Hierarchy of Needs

    This story was originally published on HackerNoon at: https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007.


    As is usually the case with fast-advancing technologies, AI has inspired massive FOMO , FUD and feuds. Some of it is deserved, some of it not — but the industry is paying attention. From stealth hardware startups to fintech giants to public institutions, teams are feverishly working on their AI strategy. It all comes down to one crucial, high-stakes question: ‘How do we use AI and machine learning to get better at what we do?’
    Check more stories related to data-science at: https://hackernoon.com/c/data-science.
    You can also check exclusive content about #data-science, #artificial-intelligence, #machine-learning, #big-data, #ai, #hackernoon-es, and more.


    This story was written by: @mrogati. Learn more about this writer by checking @mrogati's about page,
    and for more stories, please visit hackernoon.com.

    8 min

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