Smart Data Collective

NOVEMBER 8, 2020

Unlike the old days where data was readily stored and available from a single database and data scientists only needed to learn a few programming languages, data has grown with technology. This will enable you to leverage the right algorithms to create good, well structured, and performing software. Understand the Databases.

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

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Smart Data Collective

AUGUST 26, 2021

Also, it extracts historical weather data from various databases. Hadoop has also helped considerably with weather forecasting. Instead, it uses AI-powered algorithms to process weather data and generates real-time weather forecasts. from various sources. Real-Time Weather Insights.

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ODSC - Open Data Science

MAY 31, 2023

Commonly used technologies for data storage are the Hadoop Distributed File System (HDFS), Amazon S3, Google Cloud Storage (GCS), or Azure Blob Storage, as well as tools like Apache Hive, Apache Spark, and TensorFlow for data processing and analytics. Yes, many people still need a data lake (for their relevant data, not all enterprise data).

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AWS Machine Learning Blog

DECEMBER 18, 2023

MongoDB’s robust time series data management allows for the storage and retrieval of large volumes of time-series data in real-time, while advanced machine learning algorithms and predictive capabilities provide accurate and dynamic forecasting models with SageMaker Canvas. Setup the Database access and Network access.

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Clustering AWS Database ML 118

IBM Journey to AI blog

SEPTEMBER 19, 2023

Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists. And you should have experience working with big data platforms such as Hadoop or Apache Spark. Those who work in the field of data science are known as data scientists.

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Data Science 101

JUNE 10, 2019

Data scientists are the bridge between programming and algorithmic thinking. They are responsible for managing database systems, scaling data architecture to multiple servers, and writing complex queries to sift through the data. Hadoop, SQL, Python, R, Excel are some of the tools you’ll need to be familiar using.

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IBM Journey to AI blog

MARCH 21, 2024

GPUs (graphics processing units) and TPUs (tensor processing units) are specifically designed to handle complex mathematical computations central to AI algorithms, offering significant speedups compared with traditional CPUs. Additionally, using in-memory databases and caching mechanisms minimizes latency and improves data access speeds.

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Smart Data Collective

MARCH 30, 2022

They are able to utilize Hadoop-based data mining tools to improve their market research capabilities and develop better products. There are detailed databases of business names that you can use for inspiration and avoid trademark issues. These algorithms are getting better all the time.

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Pickl AI

NOVEMBER 4, 2024

Mathematics is critical in Data Analysis and algorithm development, allowing you to derive meaningful insights from data. Linear algebra is vital for understanding Machine Learning algorithms and data manipulation. Scikit-learn covers various classification , regression , clustering , and dimensionality reduction algorithms.

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Pickl AI

DECEMBER 9, 2024

Familiarise yourself with essential tools like Hadoop and Spark. Variety Data comes in multiple forms, from highly organised databases to messy, unstructured formats like videos and social media text. What are the Main Components of Hadoop? What is the Role of a NameNode in Hadoop ? What is a DataNode in Hadoop?

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Pickl AI

AUGUST 9, 2024

Variety It encompasses the different types of data, including structured data (like databases), semi-structured data (like XML), and unstructured formats (such as text, images, and videos). It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing.

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Dataconomy

JULY 24, 2023

Concepts such as linear algebra, calculus, probability, and statistical theory are the backbone of many data science algorithms and techniques. Coding skills are essential for tasks such as data cleaning, analysis, visualization, and implementing machine learning algorithms. Specializing can make you stand out from other candidates.

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AWS Machine Learning Blog

APRIL 19, 2024

We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) 7b) model to generate text responses to user questions based on the most similar slide retrieved from the vector database. OpenSearch Serverless is an on-demand serverless configuration for Amazon OpenSearch Service.

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Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineering 52

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Pickl AI

JANUARY 13, 2025

Machine Learning Engineer Machine Learning Engineers develop algorithms and models that enable machines to learn from data. Strong understanding of data preprocessing and algorithm development. They explore new algorithms and techniques to improve machine learning models. Strong knowledge of AI algorithms and architectures.

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Smart Data Collective

SEPTEMBER 26, 2019

This is an organized set of data that can be processed, stored, and retrieved from a database in an orderly format using a simplified search engine algorithm. For example, you can organize an employee table in a database in a structured manner to capture the employee’s details, job positions, salary, etc. Structured. Velocity.

Big Data 101

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Pickl AI

SEPTEMBER 17, 2024

In addition to traditional structured data (like databases), there is a wealth of unstructured and semi-structured data (such as emails, videos, images, and social media posts). This section will highlight key tools such as Apache Hadoop, Spark, and various NoSQL databases that facilitate efficient Big Data management.

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Dataconomy

JULY 25, 2023

data visualization tools, machine learning algorithms, and statistical models to uncover valuable information hidden within data. Finance: In the financial sector, data science is used for fraud detection, risk assessment, algorithmic trading, and personalized financial advice.

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Pickl AI

MAY 29, 2024

These skills encompass proficiency in programming languages, data manipulation, and applying Machine Learning Algorithms , all essential for extracting meaningful insights and making data-driven decisions. SQL is indispensable for database management and querying. It forms the basis of predictive modelling and risk assessment.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

The BigBasket team was running open source, in-house ML algorithms for computer vision object recognition to power AI-enabled checkout at their Fresho (physical) stores. We used FSx for Lustre and Amazon Relational Database Service (Amazon RDS) for fast parallel data access. Split data into train, validation, and test sets.

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Women in Big Data

SEPTEMBER 7, 2024

Advances in big data technology like Hadoop, Hive, Spark and Machine Learning algorithms have made it possible to interpret and utilize this variety of data effectively. Structured Structured data is quantitative and highly organized, typically managed within relational databases.

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IBM Journey to AI blog

SEPTEMBER 25, 2023

With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price. They stood up a file-based data lake alongside their analytical database. Uber has made the Presto query engine connect to real-time databases.

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Below, we explore some of the most widely used algorithms in ML.

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Pickl AI

FEBRUARY 28, 2023

Further, Data Scientists are also responsible for using machine learning algorithms to identify patterns and trends, make predictions, and solve business problems. Furthermore, they must be highly efficient in programming languages like Python or R and have data visualization tools and database expertise.

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Pickl AI

APRIL 6, 2023

In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. Sound knowledge of relational databases or NoSQL databases like Cassandra. Having a solid understanding of ML principles and practical knowledge of statistics, algorithms, and mathematics. What is Polybase?

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Pickl AI

JULY 18, 2023

SQL: Mastering Data Manipulation Structured Query Language (SQL) is a language designed specifically for managing and manipulating databases. While it may not be a traditional programming language, SQL plays a crucial role in Data Science by enabling efficient querying and extraction of data from databases.

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DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g., Hadoop, Spark).

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DagsHub

NOVEMBER 11, 2024

Unlike structured data, unstructured data doesn’t fit neatly into predefined models or databases, making it harder to analyse using traditional methods. While sensor data is typically numerical and has a well-defined format, such as timestamps and data points, it only fits the standard tabular structure of databases.

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Pickl AI

JANUARY 28, 2025

They encompass all the origins from which data is collected, including: Internal Data Sources: These include databases, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and flat files within an organization. databases), semi-structured (e.g., Data can be structured (e.g.,

Business Intelligence 52

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DagsHub

OCTOBER 23, 2024

Data can come from different sources, such as databases or directly from users, with additional sources, including platforms like GitHub, Notion, or S3 buckets. Vector Databases Vector databases help store unstructured data by storing the actual data and its vector representation. mp4,webm, etc.), and audio files (.wav,mp3,acc,

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Pickl AI

AUGUST 21, 2024

Crawlers then store this information in a database for indexing. Advanced crawling algorithms allow them to adapt to new content and changes in website structures. Precision: Advanced algorithms ensure they accurately categorise and store data. Structured data can be easily imported into databases or analytical tools.

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Pickl AI

JUNE 19, 2024

Computer Science A computer science background equips you with programming expertise, knowledge of algorithms and data structures, and the ability to design and implement software solutions – all valuable assets for manipulating and analyzing data. Databases and SQL Data doesn’t exist in a vacuum.

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Pickl AI

DECEMBER 4, 2024

The field has evolved significantly from traditional statistical analysis to include sophisticated Machine Learning algorithms and Big Data technologies. Issues such as algorithmic bias, data privacy, and transparency are becoming critical topics of discussion within the industry.

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The MLOps Blog

MAY 31, 2023

From an algorithmic perspective, Learning To Rank (LeToR) and Elastic Search are some of the most popular algorithms used to build a Seach system. We can collect and use user-product historical interaction data to train recommendation system algorithms. are some examples. Let’s understand this with an example.

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