#sparkr

[owcsautism]

ssg m&g

[lunaballoona09]

OSC x Clan Gen (P2)

I wanted to free up space on my laptop so im gonna put these here. First is BFDI under the cut , all the characters (except for Announcer) are kits in BFDI. Some of the cats will feature some of my personal headcanons so beware ig

Also one of Snowball's thoughts mentions a Specklekit, that would be Pin, but for some reason I have no screenshots of her sorry Pin fans.

Dreamclan masterpost

Powerclan masterpost p1, p2

Part 1 = BFDI

Part 2 = BFDI (You're here!)

Part 3 = ???

Leafy - Leafkit (ignore the fact that the first two say Leafblade, i mixed up the coding)

Match - Sparkkit (same problem as Leafy)

Needle - Needlekit

Pencil - Greykit

Snowball - Snowkit

Teardrop - Dewkit

Tennis Ball - Fuzzykit

Woody - Woodykit

[pipermca]

Hello and welcome to my pinned post.

I am Piper, and I mostly write Transformers fanfic on AO3. Bluestreak is my favourite character, and he features very prominently in a lot of my stories.

Because I have 100+ (!) stories on AO3, I wanted to do a little bibliography of a few of the stories I am most proud of. 🤗 Note the ratings and tags on these if you decide to read them, please!

I divided the list into shorter fics and longer fics.

Shorter Fics (10,000 words or less)

Do You See What I See? (Rated G, Bluestreak/Hound). I like this one because I leaned heavily into the alien aspect of the Cybertronians, which meant I got to do some fun worldbuilding.

Datastream (Rated Teen, Jazz/Prowl). I got to do (kinda) spoken-word poetry in this story, sketching out how Blaster and Prowl's minds might merge and work as one. I liked how it turned out. It has an illustration done by @myladraws!

Debts (Rated Mature for body horror, Smokescreen & Prowl). I love horror, and I played with tension and anticipation in this one. I have been told this is one of my most disturbing fics, so keep that in mind!

Longer Fics (10,001+ words)

Mind, Body, and Soul (Rated Explicit, Barricade/Bluestreak/Prowl/Smokescreen in various configurations). This is the story I am the most proud of, and it's also the longest by far. It is a sprawling tale about love and betrayal and forgiveness and perseverance, and I am so so happy with how it came out. It has fanart done by @/Klick_tack on Twitter!

The Spark Remembers (Rated Teen, Bluestreak/Hound and Bluestreak/Prowl.) I had fun weaving Bluestreak's past into what his new future was, and I loved the screaming from the readers at the reveal. 😅 This was a Big Bang story so it has artwork from @chaoswolf12 and @tfadi!

The Colour of Your Optics (Rated Explicit, Bluestreak/Sunstreaker) This is a story in my "there's a dating app called Sparkr" series with a bunch of loosely-related fics, but this one in particular is sickeningly and adorably sweet. In my opinion. :)

My ask box is almost always open, so if you want to come in and scream at me about robots, please do. ^.^

[mrsterlingeverything]

I made a variable in pyspark and pulled it out in sparkr 💦

[bestdatasciencecourseindia]

🔍 Dive into Data Science: Your Beginner's Guide 🧠

🚀 Kickstart Your Data Science Journey Today! Are you curious about Data Science but don’t know where to begin? We've got you covered!

Our latest blog, "Data Science Tutorial: Learn Data Science From Scratch", is your all-in-one guide to entering one of the most in-demand fields of the 21st century. 📊💡

🔍 In this blog, you’ll learn: ✅ Why Data Science matters in today’s digital world ✅ Who is a Data Scientist and what skills are required ✅ The key components of Data Science – from R Studio to Big Data & Hadoop ✅ Common problems solved using Machine Learning algorithms ✅ Career roles and job trends in the Data Science landscape

Whether you're a beginner or looking to strengthen your foundation, this tutorial will help you understand the tools, concepts, and career opportunities in Data Science.

📖 Read the full blog here: 👉 https://analyticsjobs.in/data-science-tutorial-learn-data-science-from-scratch

#DataScience #MachineLearning #AnalyticsJobs #CareerInDataScience #BigData #DataScienceTutorial #LearnDataScience #ArtificialIntelligence #Hadoop #SparkR

[uegub]

5 Powerful Programming Tools Every Data Scientist Needs

Data science is the field that involves statistics, mathematics, programming, and domain knowledge to extract meaningful insights from data. The explosion of big data and artificial intelligence has led to the use of specialized programming tools by data scientists to process, analyze, and visualize complex datasets efficiently.

Choosing the right tools is very important for anyone who wants to build a career in data science. There are many programming languages and frameworks, but some tools have gained popularity because of their robustness, ease of use, and powerful capabilities.

This article explores the top 5 programming tools in data science that every aspiring and professional data scientist should know.

Top 5 Programming Tools in Data Science

1. Python

Probably Python is the leading language used due to its versatility and simplicity together with extensive libraries. It applies various data science tasks, data cleaning, statistics, machine learning, and even deep learning applications.

Key Python Features for Data Science:

Packages & Framework: Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow, PyTorch

Easy to Learn; the syntax for programming is plain simple

High scalability; well suited for analyzing data at hand and enterprise business application

Community Support: One of the largest developer communities contributing to continuous improvement

Python's versatility makes it the go-to for professionals looking to be great at data science and AI.

2. R

R is another powerful programming language designed specifically for statistical computing and data visualization. It is extremely popular among statisticians and researchers in academia and industry.

Key Features of R for Data Science:

Statistical Computing: Inbuilt functions for complex statistical analysis

Data Visualization: Libraries like ggplot2 and Shiny for interactive visualizations

Comprehensive Packages: CRAN repository hosts thousands of data science packages

Machine Learning Integration: Supports algorithms for predictive modeling and data mining

R is a great option if the data scientist specializes in statistical analysis and data visualization.

3. SQL (Structured Query Language)

SQL is important for data scientists to query, manipulate, and manage structured data efficiently. The relational databases contain huge amounts of data; therefore, SQL is an important skill in data science.

Important Features of SQL for Data Science

Data Extraction: Retrieve and filter large datasets efficiently

Data Manipulation: Aggregate, join, and transform datasets for analysis

Database Management: Supports relational database management systems (RDBMS) such as MySQL, PostgreSQL, and Microsoft SQL Server

Integration with Other Tools: Works seamlessly with Python, R, and BI tools

SQL is indispensable for data professionals who handle structured data stored in relational databases.

4. Apache Spark

Apache Spark is the most widely utilized open-source, big data processing framework for very large-scale analytics and machine learning. It excels in performance for handling a huge amount of data that no other tool would be able to process.

Core Features of Apache Spark for Data Science:

Data Processing: Handle large datasets on high speed.

In-Memory Computation: Better performance in comparison to other disk-based systems

MLlib: A Built-in Machine Library for Scalable AI Models.

Compatibility with Other Tools: Supports Python (PySpark), R (SparkR), and SQL

Apache Spark is best suited for data scientists working on big data and real-time analytics projects.

5. Tableau

Tableau is one of the most powerful data visualization tools used in data science. Users can develop interactive and informative dashboards without needing extensive knowledge of coding.

Main Features of Tableau for Data Science:

Drag-and-Drop Interface: Suitable for non-programmers

Advanced Visualizations: Complex graphs, heatmaps, and geospatial data can be represented

Data Source Integration: Database, cloud storage, and APIs integration

Real-Time Analytics: Fast decision-making is achieved through dynamic reporting

Tableau is a very popular business intelligence and data storytelling tool used for making data-driven decisions available to non-technical stakeholders.

Data Science and Programming Tools in India

This led to India's emergence as one of the data science and AI hubs, which has seen most businesses, start-ups, and government organizations take significant investments into AI-driven solutions. The increase in demand for data scientists boosted the adoption rate of programming tools such as Python, R, SQL, and Apache Spark.

Government and Industrial Initiatives Gaining Momentum Towards Data Science Adoption in India

National AI Strategy: NITI Aayog's vision for AI driven economic transformation.

Digital India Initiative: This has promoted data-driven governance and integration of AI into public services.

AI Adoption in Enterprises: The big enterprises TCS, Infosys, and Reliance have been adopting AI for business optimisation.

Emerging Startups in AI & Analytics: Many Indian startups have been creating AI-driven products by using top data science tools.

Challenges to Data Science Growth in India

Some of the challenges in data science growth despite rapid advancements in India are:

Skill Gaps: Demand outstrips supply.

Data Privacy Issues: The emphasis lately has been on data protection laws such as the Data Protection Bill.

Infrastructure Constraint: Computational high-end resources are not accessible to all companies.

To bridge this skill gap, many online and offline programs assist students and professionals in learning data science from scratch through comprehensive training in programming tools, AI, and machine learning.

Kolkata Becoming the Next Data Science Hub

Kolkata is soon emerging as an important center for education and research in data science with its rich academic excellence and growth in the IT sector. Increasing adoption of AI across various sectors has resulted in businesses and institutions in Kolkata concentrating on building essential data science skills in professionals.

Academic Institutions and AI Education

Multiple institutions and private learning centers provide exclusive AI Courses Kolkata, dealing with the must-have programming skills such as Python, R, SQL, and Spark. Hands-on training sessions are provided by these courses about data analytics, machine learning, and AI.

Industries Using Data Science in Kolkata

Banking & Finance: Artificial intelligence-based risk analysis and fraud detection systems

Healthcare: Data-driven Predictive Analytics of patient care optimisation

E-Commerce & Retail: Customized recommendations & customer behavior analysis

EdTech: AI based adaptive learning environment for students.

Future Prospects of Data Science in Kolkata

Kolkata would find a vital place in India's data-driven economy because more and more businesses as well as educational institutions are putting money into AI and data science. The city of Kolkata is currently focusing strategically on technology education and research in AI for future innovations in AI and data analytics.

Conclusion

Over the years, with the discovery of data science, such programming tools like Python and R, SQL, Apache Spark, and Tableau have become indispensable in the world of professionals. They help in analyzing data, building AI models, and creating impactful visualizations.

Government initiatives and investments by the enterprises have seen India adapt rapidly to data science and AI, thus putting a high demand on skilled professionals. As a beginner, the doors are open with many educational programs to learn data science with hands-on experience using the most popular tools.

Kolkata is now emerging as a hub for AI education and innovation, which will provide world-class learning opportunities to aspiring data scientists. Mastery of these programming tools will help professionals stay ahead in the ever-evolving data science landscape.

[mvishnukumar]

What are the languages supported by Apache Spark?

Hi,

Apache Spark is a versatile big data processing framework that supports several programming languages. Here are the main languages supported:

1. Scala: Scala is the primary language for Apache Spark and is used to develop Spark applications. Spark is written in Scala, and using Scala provides the best performance and access to all of Spark’s features. Scala’s functional programming capabilities align well with Spark’s design.

2. Java: Java is also supported by Apache Spark. It’s a common choice for developers who are familiar with the Java ecosystem. Spark’s Java API allows developers to build applications using Java, though it might be less concise compared to Scala.

3. Python: Python is widely used with Apache Spark through the PySpark API. PySpark allows developers to write Spark applications using Python, which is known for its simplicity and readability. Python’s extensive libraries make it a popular choice for data science and machine learning tasks.

4. R: Apache Spark provides support for R through the SparkR package. SparkR is designed for data analysis and statistical computing in R. It allows R users to harness Spark’s capabilities for big data processing and analytics.

5. SQL: Spark SQL is a component of Apache Spark that supports querying data using SQL. Users can run SQL queries directly on Spark data, and Spark SQL provides integration with BI tools and data sources through JDBC and ODBC drivers.

6. Others: While Scala, Java, Python, and R are the primary languages supported, Spark also has limited support for other languages through community contributions and extensions.

In summary, Apache Spark supports Scala, Java, Python, and R, making it accessible to a wide range of developers and data scientists. The support for SQL further enhances its capability to work with structured data and integrate with various data sources.

[amitsaini012]

Top 10 Uses of R Programming Language in 2024

R has become one of the popular programming languages for data analysis and statistical computing. With its vast collection of packages and active user community, R offers endless possibilities for data scientists, statisticians, and analysts. As we move into 2024, here are 10 of the top uses of R programming:

1. Data Visualization 

With libraries like ggplot2, R makes it easy to create custom, publication-quality graphics to visualize data. Complex visualizations like interactive charts, maps, and network diagrams can be made with just a few lines of code.

2. Statistical Modeling 

R has a wide range of statistical and machine learning packages like stats, caret, and more. These make it possible to build and evaluate predictive models for classification, regression, clustering, time series analysis, etc. Models can be compared to select the best performers.

3. Data Wrangling 

The dplyr and tidyr packages in R provide a grammar of data manipulation, making it easy to slice, dice, reshape, join, and transform data sets before analysis. This helps automate the data-cleaning process.

4. Reporting 

R Markdown integrates prose, code, and visualizations into a single document. Reports and presentations with embedded statistical analysis can be dynamically generated from R scripts. The knit package enables code chunking to keep code separate from outputs.

5. Web Scraping 

Packages like Rvest and RSelenium enable scraping data from HTML web pages and dynamic websites. This data can then be processed using R's other analytics capabilities.

6. Bioinformatics 

Bioconductor provides over 1,600 packages for analyzing genetic data, DNA sequences, gene expression data, and more. R is heavily used in genomics research.

7. Finance 

R packages like quantmod, TTR, and PerformanceAnalytics provide capabilities for analyzing financial data, estimating risk, simulating portfolios, and calculating returns. R is popular in fintech and quantitative finance.

8. Machine Learning 

Packages like caret, mlbench, Keras, and H2O provide R users with tools like regression, naïve Bayes classification, random forests, gradient boosting machines, neural networks, and more.

9. Big Data Analytics 

Packages like ff, big memory, and SparkR allow the handling and analysis of large datasets that are larger than a computer's RAM. This enables scalable data analysis with R.

10. Mapping & Spatial Analysis 

Many R packages like ggmap, geospatial, leaflet, and sf support working with geospatial data. This makes R useful for creating maps, spatial data visualizations, and location analytics.

Conclusion

R has evolved into a powerful, versatile tool for data analysis and statistical computing. In 2024, R will continue to be valued for its extensibility via user-contributed packages, interactivity for iterative analysis, and visualization capabilities. Key strengths like effective data wrangling, modeling, reporting, and machine learning will ensure R remains popular across domains, including academia, finance, technology, genomics, and more. 

While other languages like Python, Julia, and MATLAB will also have niche uses, R's focused support for statistical analysis gives it an edge for data science applications. The low-cost and open-source nature of R is attractive to both students and companies. With new packages expanding R's capabilities all the time, it is certain that R will be a highly utilized programming language in 2024 and remain a staple of data scientists' toolkits for years to come.

[dataplusweb-blog]

Dataiku : tout savoir sur la plateforme d'IA "made in France"

Dataiku :

tout savoir sur la plateforme d'IA "made in France"

Antoine Crochet-Damais

JDN

 

Dataiku est une plateforme d'intelligence artificielle créée en France en 2013. Elle s'est imposée depuis parmi les références mondiales des studios de data science et de machine learning.

SOMMAIRE

Dataiku, c’est quoi ?

Dataiku DSS, qu'est-ce que c'est ?

Quelles sont les fonctionnalités de Dataiku ?

Quel est le prix de Dataiku ?

Qu’est-ce que Dataiku Online ?

Dataiku Academy : formation / certification

Dataiku vs DataRobot

Dataiku vs Alteryx

Dataiku vs Databricks

Dataiku Community

Dataiku, c’est quoi ?

Dataiku est une plateforme de data science d'origine française. Elle se démarque historiquement par son caractère très packagé et intégré. Ce qui la met à la portée aussi bien des data scientists confirmés que débutants. Grâce à son ergonomie, elle permet de créer un modèle en quelques clics, tout en industrialisant en toile de fonds l'ensemble de la chaine de traitement : collecte, préparation des données…

Co-fondée en 2013 à Paris par Florian Douetteau, son CEO actuel, et Clément Stenac (tous deux anciens d'Exalead) aux côtés de Thomas Cabrol et Marc Batty, Dataiku affiche une croissance fulgurante. Dès 2015, la société s'implante aux Etats-Unis. Après une levée de 101 millions de dollars en 2018, Dataiku boucle un tour de table de 400 millions de dollars en 2021 pour une valorisation de 4,6 milliards de dollars. L'entreprise compte plus de 1000 salariés et plus de 300 clients parmi les plus grands groupes mondiaux. Parmi eux figurent les sociétés françaises Accor, BNP Paribas, Engie ou encore SNCF.

Dataiku DSS, qu'est-ce que c'est ?

Dataiku DSS (pour Dataiku Data Science Studio) est le nom de la plateforme d'IA de Dataiku.

Quelles sont les fonctionnalités de Dataiku ?

La plateforme de Dataiku compte environ 90 fonctionnalités que l'on peut regrouper en plusieurs grands domaines :

L'intégration. La plateforme s'intègre à Hadoop, Spark, mais aussi aux services des clouds AWS, Azure, Google Cloud. Au total, la plateforme est équipée de plus de 25 connecteurs. 

Les plugins. Une galerie de plus de 100 plugins permet de bénéficier d'applications tierces dans de nombreux domaines : traduction, NLG, météo, moteur de recommandation, import/export de données...

La data préparation / data ops. Une console graphique gère la préparation des données. Les time series et données géospatiales sont supportées. Plus de 90 data transformers prépackagés sont disponibles. 

Le développement. Dataiku prend en charge les notebooks Jupyter, les langages Python, R, Scala, SQL, Hive, Pig, Impala. Il supporte PySpark, SparkR et SparkSQL.

Le machine Learning. La plateforme inclut un moteur d'automatisation du machine learning (auto ML), une console de visualisation pour l'entrainement des r��seaux de neurones profonds, le support de Scikit-learn et XGBoost, etc.

La collaboration. Dataiku intègre des fonctionnalités de gestion de projet, de chat, de wiki, de versioning (via Git)...

La gouvernance. La plateforme propose une console de monitoring des modèles, d'audit, ainsi qu'un feature store.

Le MLOps. Dataiku gère le déploiement de modèles. Il prend en charge les architecture Kubernetes mais aussi les offres de Kubernetes as a Service d'AWS, Azure et Google Cloud.

La data visualisation. Une interface de visualisation statistique est complétée par 25 graphiques de data visualisation pour identifier les relations et aperçus au sein des jeux de données.

Dataiku est conçu pour gérer graphiquement des pipelines de machine learning. © JDN / Capture

Quel est le prix de Dataiku ?

Dataiku propose une édition gratuite de sa plateforme à installer soi-même. Baptisée Dataiku Free, elle se limite à trois utilisateurs, mais donne accès à la majorité des fonctionnalités. Elle est disponible pour Windows, Linux, MacOS, Amazon EC2, Google Cloud et Microsoft Azure. 

Pour aller plus loin, Dataiku commercialise trois éditions dont les prix sont disponibles sur demande : Dataiku Discover pour les petites équipes, Dataiku Business pour les équipes de taille moyenne, et Dataiku Enterprise pour déployer la plateforme à l'échelle d'une grande entreprise.

Qu’est-ce que Dataiku Online ?

Principalement conçu pour de petites structures, Dataiku Online permet de gérer les projets de data science à une échelle modérée. Il s’agit d’un dispositif de type SaaS (Software as a Service). Les fonctionnalités sont similaires à Dataiku, mais le paramétrage et le lancement de l’application sont plus rapides.

Dataiku Academy : formation et certification Dataiku

La Dataiku Academy regroupe une série de formations en ligne à la plateforme de Dataiku. Elle propose un programme Quicks Start qui permet de commencer à utiliser la solution en quelques heures, mais aussi des sessions Learning Paths pour acquérir des compétences plus avancées. Chaque programme permet de décrocher une certification Dataiku : Core Designer Certificate, ML Practitioner Certificate, Advanced Designer Certificate, Developer Certificate et MLOps Practitioner Certificate.

Dataiku prend en charge les time series et données géospatiales. © JDN / Capture

Dataiku vs DataRobot

Créé en 2012, l'américain DataRobot peut être considéré comme le pure player historique du machine learning automatisé (auto ML). Un terrain sur lequel Dataiku s'est positionne plus tard. Au fur et à mesure de leur développement, les deux plateformes tendent désormais à être de plus en plus comparables.

Face à DataRobot, Dataiku se distingue cependant sur le front de la collaboration. L'éditeur multiplie les fonctionnalités dans ce domaine : wiki, partage de tableaux de bord de résultats, système de gestion des rôles et de traçabilité des actions, etc.

Dataiku vs Alteryx

Alors que Dataiku est avant tout une plateforme de data science orientée machine learning, Alteryx, lui, se positionne comme un solution d'intelligence décisionnelle ciblant potentiellement tout décideur d'entreprise, bien au-delà des équipes de data science.

La principale valeur ajoutée d'Alteryx est d'automatiser la création de tableaux de bord analytics. Des tableaux de bord qui pourront inclure des indicateurs prédictifs basés sur des modèles de machine learning. Dans cet optique, Alteryx intègre des fonctionnalités de machine learning automatisé (auto ML) pour permettre aux utilisateurs de générer ce type d'indicateur. C'est son principal point commun avec Dataiku.

Dataiku vs Databricks

Dataiku et Databricks sont des plateformes très différentes. La première s'oriente vers la data science, la conception et le déploiement de modèles de machine learning. La seconde se présente sous la forme d'une data platform universelle répondant à la fois aux cas d'usage orientés entrepôt de données et BI, data lake, mais aussi streaming de données et calcul distribué.

Reste que Databricks s'enrichit de plus en plus de fonctionnalités orientées machine learning. La société de San Francisco a acquis l'environnement de data science low-code / no code 8080 Labs en octobre 2021, puis la plateforme de MLOps Cortex Labs en avril 2022. Deux technologies qu'elle est en train d'intégrer. 

Dataiku Community : tutoriels et documentation

Dataiku Community est un espace d'échange et de documentation pour parfaire ses connaissances sur Dataiku et ses champs d'application. Après inscription, il est possible d'intégrer le forum de discussions.

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[pipermca]

A friend point out to me that I have 99 stories on AO3! Wow! And then they asked what I was planning to post for story #100.

I have no idea. So, have a poll! (I'll run this for a week to see what we get.)

[letmegetsomesleep]

[burn3dgirl]

[kitnicks]

[thaisstefngp]