ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Knowing how spaCy works means little if you don’t know how to apply core NLP skills like transformers, classification, linguistics, question answering, sentiment analysis, topic modeling, machine translation, speech recognition, named entity recognition, and others.

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Analytics Vidhya

FEBRUARY 17, 2023

Introduction Are you curious about the latest advancements in the data tech industry? Perhaps you’re hoping to advance your career or transition into this field. In that case, we invite you to check out DataHour, a series of webinars led by experts in the field.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Being able to discover connections between variables and to make quick insights will allow any practitioner to make the most out of the data. Analytics and Data Analysis Coming in as the 4th most sought-after skill is data analytics, as many data scientists will be expected to do some analysis in their careers.

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Women in Big Data

OCTOBER 9, 2024

These procedures are central to effective data management and crucial for deploying machine learning models and making data-driven decisions. The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline?

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Data Science Dojo

JULY 3, 2024

Here’s a list of key skills that are typically covered in a good data science bootcamp: Programming Languages : Python : Widely used for its simplicity and extensive libraries for data analysis and machine learning. R : Often used for statistical analysis and data visualization.

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phData

JANUARY 5, 2023

Python is the top programming language used by data engineers in almost every industry. Python has proven proficient in setting up pipelines, maintaining data flows, and transforming data with its simple syntax and proficiency in automation. Truly a must-have tool in your data engineering arsenal!

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AWS Machine Learning Blog

AUGUST 12, 2024

Data is presented to the personas that need access using a unified interface. For example, it can be used to answer questions such as “If patients have a propensity to have their wearables turned off and there is no clinical telemetry data available, can the likelihood that they are hospitalized still be accurately predicted?”

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AWS Machine Learning Blog

AUGUST 8, 2024

The LookML view and model information is brought into Amazon S3 through a separate data pipeline (not shown). About the Authors Apurva Gawad is a Senior Data Engineer at Twilio specializing in building scalable systems for data ingestion and empowering business teams to derive valuable insights from data.

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phData

FEBRUARY 21, 2025

A data warehouse enables advanced analytics, reporting, and business intelligence. The data warehouse emerged as a means of resolving inefficiencies related to data management, data analysis, and an inability to access and analyze large volumes of data quickly.

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phData

JUNE 26, 2024

Cleaning and preparing the data Raw data typically shouldn’t be used in machine learning models as it’ll throw off the prediction. Data engineers can prepare the data by removing duplicates, dealing with outliers, standardizing data types and precision between data sets, and joining data sets together.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Pickl AI

MAY 15, 2024

As a Data Analyst, you’ve honed your skills in data wrangling, analysis, and communication. But the allure of tackling large-scale projects, building robust models for complex problems, and orchestrating data pipelines might be pushing you to transition into Data Science architecture.

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The MLOps Blog

FEBRUARY 5, 2023

We will also get familiar with tools that can help record this data and further analyse it. In the later part of this article, we will discuss its importance and how we can use machine learning for streaming data analysis with the help of a hands-on example. What is streaming data? Happy Learning!

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Pickl AI

SEPTEMBER 5, 2024

Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. Collaborating with Teams: Working with data engineers, analysts, and stakeholders to ensure data solutions meet business needs. Other valuable certifications include Microsoft Certified: Azure AI Engineer Associate.

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Mlearning.ai

JUNE 16, 2023

GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API. The data would be interesting to analyze. From Data Engineering to Prompt Engineering Prompt to do data analysis BI report generation/data analysis In BI/data analysis world, people usually need to query data (small/large).

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ODSC - Open Data Science

APRIL 4, 2024

Find out how to weave data reliability and quality checks into the execution of your data pipelines and more. More Speakers and Sessions Announced for the 2024 Data Engineering Summit Ranging from experimentation platforms to enhanced ETL models and more, here are some more sessions coming to the 2024 Data Engineering Summit.

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ODSC - Open Data Science

JANUARY 28, 2025

While traditional roles like data scientists and machine learning engineers remain essential, new positions like large language model (LLM) engineers and prompt engineers have gained traction. LLM Engineers: With job postings far exceeding the current talent pool, this role has become one of the hottest inAI.

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Mlearning.ai

JULY 11, 2023

This includes important stages such as feature engineering, model development, data pipeline construction, and data deployment. For instance, feature engineering and exploratory data analysis (EDA) often require the use of visualization libraries like Matplotlib and Seaborn.

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DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

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O'Reilly Media

SEPTEMBER 15, 2021

A platform, clearly, but a platform for building data pipelines that’s qualitatively different from a platform like Ray, Spark, or Hadoop. Data and AI professionals—a rubric under which we include data scientists, data engineers, and specialists in AI and ML—are well-paid, reporting an average salary just under $150,000.

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ODSC - Open Data Science

DECEMBER 3, 2024

While the concept of data mesh as a data architecture model has been around for a while, it was hard to define how to implement it easily and at scale. Two data catalogs went open-source this year, changing how companies manage their data pipeline. The departments closest to data should own it.

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Pickl AI

JULY 25, 2024

Analysts and business users can easily comprehend and interact with the schema, which simplifies the process of generating reports and performing data analysis. Star Schema’s design prioritises simplicity and performance, making it advantageous for various data warehousing and business intelligence needs.

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IBM Journey to AI blog

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

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ODSC - Open Data Science

DECEMBER 9, 2024

This allows iterative data analysis workflows rather than rigid scripts. Python forms a common lingua franca for open data science thanks to its flexibility and the breadth of domain-specific packages continuously expanded by the active community. Additionally, no-code automated machine learning (AutoML) solutions like H20.ai

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Pickl AI

OCTOBER 10, 2024

Below, we explore five popular data transformation tools, providing an overview of their features, use cases, strengths, and limitations. Apache Nifi Apache Nifi is an open-source data integration tool that automates system data flow.

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The MLOps Blog

MARCH 20, 2023

However, in scenarios where dataset versioning solutions are leveraged, there can still be various challenges experienced by ML/AI/Data teams. Data aggregation: Data sources could increase as more data points are required to train ML models. Existing data pipelines will have to be modified to accommodate new data sources.

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The MLOps Blog

DECEMBER 7, 2022

Knowing what needs to be done and in what order (the whole process and management side of data) is often overlooked , and we know sometimes keeping everyone up to date can be a bit tedious in its own way, but if you can orchestrate pipelines with dozens of steps in your sleep, you surely can take a moment to write what you’re up to, right?

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Mlearning.ai

JULY 10, 2023

Without partitioning, daily data activities will cost your company a fortune and a moment will come where the cost advantage of GCP BigQuery becomes questionable. Using Scheduled Queries is a smart choice for regular reporting, data analysis, and other processing tasks.

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Snorkel AI

MAY 12, 2023

The reason is that most teams do not have access to a robust data ecosystem for ML development. billion is lost by Fortune 500 companies because of broken data pipelines and communications. Publishing standards for data and governance of that data is either missing or very widely far from an ideal.

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Snorkel AI

MAY 12, 2023

The reason is that most teams do not have access to a robust data ecosystem for ML development. billion is lost by Fortune 500 companies because of broken data pipelines and communications. Publishing standards for data and governance of that data is either missing or very widely far from an ideal.

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Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines. Thats where data engineering tools come in!

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Dataconomy

MARCH 19, 2025

This helps facilitate data-driven decision-making for businesses, enabling them to operate more efficiently and identify new opportunities. Definition and significance of data science The significance of data science cannot be overstated. Machine learning engineer: Focuses on the development of predictive models.

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