Data Science Tech Brief By HackerNoon – Détails, épisodes et analyse

This story was originally published on HackerNoon at: https://hackernoon.com/98percent-of-data-strategies-fail-lets-fix-it.
Learn how to fix failing data strategies using the '5 W's' framework. Transform your approach to KPIs and drive real business value with actionable insights.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-strategy, #kpi-management, #business-intelligence, #data-driven-decisions, #executive-leadership, #analytics-roi, #data-roi, #data-governance, and more.

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

Even the most well-equipped organizations can find themselves serving up a mess instead of actionable insights. Here's a step-by-step process of fixing your data strategy, ensuring that you're serving up actionable data instead of a recipe for disaster. In the following sections, we'll dive into the common data strategy nightmares.

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-measure-the-results-of-in-app-events-when-onelinks-dont-work.
How To Measure The Results Of In-App Events When Onelinks Don’t Work
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #analytics, #onelink, #inapp-events, #marketing, #app-store, #mobile-apps, #digital-marketing, #good-company, and more.

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

Many app developers and marketing managers face the challenge of accurately measuring the impact of In-App Events (IAEs) on the App Store. While IAEs have proven effective for re-engaging users, attracting new downloads, and increasing revenue, traditional tracking methods like OneLink don’t actually include IAEs. Major mobile attribution platforms confirm that currently there is no way to track IAEs properly. At Social Discovery Group, our portfolio of 60+ dating and entertainment brands is supported by a team of over 100 marketers dedicated to app growth and development. We’re used to measuring all our marketing efforts in terms of financial value. Eventually, we’ve managed to develop our own composite way to evaluate IAEs, and are going to share it with you.

This story was originally published on HackerNoon at: https://hackernoon.com/when-and-when-not-to-use-apache-kafka-as-a-database.
Discover how Apache Kafka’s data retention and querying capabilities make it similar to a database and learn when to use Kafka for database-like use cases.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #apache-kafka, #kafka-vs-database, #kafka-as-a-database, #real-time-data-processing, #database-management, #kafka-querying-capabilities, #open-source-event-streaming, #apache-kafka-for-data-storage, and more.

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

Apache Kafka, while not a traditional database, has database-like properties such as data retention and querying capabilities. This article explores when Kafka can be used for database-like purposes and when it is best suited as a streaming platform.

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.

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!

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.

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.

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.

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.

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.