Data Science Connect

JANUARY 27, 2023

Data engineering is a crucial field that plays a vital role in the data pipeline of any organization. It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible.

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Data Engineering Data Engineering Data Engineer Data Engineering 130

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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Data Science Blog

SEPTEMBER 19, 2023

Streamlined Collaboration Among Teams Data Warehouse Systems in the cloud often involve cross-functional teams — data engineers, data scientists, and system administrators. This ensures that the data models and queries developed by data professionals are consistent with the underlying infrastructure.

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Data Warehouse Azure SQL Database 259

Hacker News

NOVEMBER 19, 2024

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)

Computer Science 146

Computer Science Computer Science ETL ML 146

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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Data Engineering Data Engineering Data Engineer Data Engineering 52

Data Science Dojo

JANUARY 10, 2023

All data roles are identical It’s a common data science myth that all data roles are the same. So, let’s distinguish between some common data roles – data engineer, data scientist, and data analyst. Data scientists only work on predictive modeling Another myth!

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Data Science Data Scientist Data Analyst Machine Learning 195

Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

DataSeries

AUGUST 15, 2024

Enrich data engineering skills by building problem-solving ability with real-world projects, teaming with peers, participating in coding challenges, and more. Globally several organizations are hiring data engineers to extract, process and analyze information, which is available in the vast volumes of data sets.

Data Engineer 40

Data Engineer Data Engineering Data Engineering Data Engineering 40

AWS Machine Learning Blog

NOVEMBER 13, 2024

With the integration of SageMaker and Amazon DataZone, it enables collaboration between ML builders and data engineers for building ML use cases. ML builders can request access to data published by data engineers. caveats_and_recommendations="Your model's caveats and recommendations.",

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ML ML AWS Data Preparation 115

Pickl AI

JUNE 5, 2025

In the Indian context, data scientists often work in dynamic environments such as IT services, fintech, e-commerce, healthcare, and telecom sectors. They are expected to be versatile, handling everything from data engineering and exploratory analysis to deploying machine learning models and communicating insights to business stakeholders.

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Data Scientist Data Science Power BI Machine Learning 52

AWS Machine Learning Blog

MARCH 18, 2025

The language model then generates a SQL query that incorporates the enterprise knowledge. Streamlit This open source Python library makes it straightforward to create and share beautiful, custom web apps for ML and data science. In just a few minutes you can build powerful data apps using only Python. Error app.py

SQL 112

SQL Database AI AI 112

Dataconomy

MAY 2, 2023

What do machine learning engineers do: They analyze data and select appropriate algorithms Programming skills To excel in machine learning, one must have proficiency in programming languages such as Python, R, Java, and C++, as well as knowledge of statistics, probability theory, linear algebra, and calculus.

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ML ML Machine Learning Machine Learning 110

ODSC - Open Data Science

MARCH 30, 2023

Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure Machine Learning Studio.

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Azure ML ML Data Models 52

DataRobot

FEBRUARY 22, 2021

DataRobot’s team of elite data scientists and thought leaders have created, curated, and taught rigorous courses that empower 10X Academy students to take control of their future by gaining the skills required to solve complex problems. Your Data Science Education Starts Here.

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Data Science Data Scientist Citizen Data Scientist Data Analyst 52

ODSC - Open Data Science

NOVEMBER 19, 2024

This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies. Here’s a breakdown of ten top sessions from this year’s conference that data professionals should consider.

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Deep Learning Deep Learning Data Science AI 52

ODSC - Open Data Science

APRIL 26, 2023

Though scripted languages such as R and Python are at the top of the list of required skills for a data analyst, Excel is still one of the most important tools to be used. Tools such as the mentioned are critical for anyone interested in becoming a machine learning engineer.

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Data Analyst Machine Learning Machine Learning Power BI 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Airflow for workflow orchestration Airflow schedules and manages complex workflows, defining tasks and dependencies in Python code. An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala. And you should have experience working with big data platforms such as Hadoop or Apache Spark.

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Data Science Analytics Analytics Data Scientist 94

Mlearning.ai

JUNE 16, 2023

Game changer ChatGPT in Software Engineering: A Glimpse Into the Future | HackerNoon Generative AI for DevOps: A Practical View - DZone ChatGPT for DevOps: Best Practices, Use Cases, and Warnings. diagram Using ChatGPT to build system diagrams — Part II Generate C4 diagrams using mermaid.js

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AI AI Data Analysis Data Analysis 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

NOVEMBER 18, 2024

Introduction to Containers for Data Science/Data Engineering Michael A Fudge | Professor of Practice, MSIS Program Director | Syracuse University’s iSchool In this hands-on session, you’ll learn how to leverage the benefits of containers for DS and data engineering workflows.

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Deep Learning Deep Learning Data Science Database 40

DagsHub

DECEMBER 14, 2023

Why Version Control is Essential in ML Version control is an indispensable practice in machine learning (ML) for several crucial reasons: Reproducibility: ML projects are often iterative and involve numerous experiments with different data, models, and hyperparameters.

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Machine Learning Machine Learning ML ML 52

IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

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Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

AUGUST 23, 2023

Consider enrolling in a “Data Science for stock market” course, which can provide insights into the specific techniques, tools, and datasets relevant to financial markets. Project-based Learning Hands-on experience is invaluable when it comes to Data Science.

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Pickl AI

APRIL 10, 2023

Integration Data Visualisation is an essential part of Data Analytics which help in presenting the data in visual or graphical format, enabling executives to understand the data better. Volume of Data Within organisations, volumes of data in the records are impossible to manage because of the large number of datasets.

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Data Analyst SQL Data Analysis Data Analysis 52

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. A data catalog can assist directly with every step, but model development. Explore the Data.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

Pickl AI

JANUARY 16, 2024

Join me in understanding the pivotal role of Data Analysts , where learning is not just an option but a necessity for success. Key takeaways Develop proficiency in Data Visualization, Statistical Analysis, Programming Languages (Python, R), Machine Learning, and Database Management.

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Data Analyst Data Analysis Data Analysis Analytics 52

Pickl AI

DECEMBER 4, 2023

. ₹ 6,20000 Analytical skills, proficiency in Data Analysis tools (e.g., Data Scientist Involves advanced analysis of complex datasets to extract insights and create predictive models. Machine Learning Engineer Designs and develops algorithms that enable computers to learn from and make predictions or decisions based on data.

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DagsHub

OCTOBER 23, 2024

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

JUNE 7, 2024

Integration: Airflow integrates seamlessly with other data engineering and Data Science tools like Apache Spark and Pandas. Scalability: Being a cloud-based service, Azure Data Factory offers scalability to meet changing data processing demands. Read Further: Azure Data Engineer Jobs.

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Hacker News

JULY 1, 2025

dustanbower 7 minutes ago | next [–] Location: Virginia, United States Remote: Yes (have worked exclusively remotely for past 14 years) Willing to relocate: No I've been doing backend work for the past 14 years, with Python, Django, and Django REST Framework. Interested in Python work or full-stack with Python.

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Python AWS SQL ML 56

Alation

FEBRUARY 17, 2023

In one shop we built out one story for each function and used that to gain support and propel the idea of data governance forward. IT , at times, may seem to think that they drive data governance. And for good rason: many data governance jobs postings seek skills like Python, programming skills, etc.

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Data Governance Data Quality Data Analyst Data Pipeline 52

Hacker News

JULY 1, 2025

Senior/Staff+ Engineer. Good at Go, Kubernetes (Understanding how to manage stateful services in a multi-cloud environment) We have a Python service in our Recommendation pipeline, so some ML/Data Science knowledge would be good. Python/Django deeply internalized; ideally Vue (or React) skills as well.

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Python AWS ML ML 78

Snorkel AI

MAY 12, 2023

Model-ready data refers to a feature library. For example, where verified data is present, the latencies are quantified. It enables users to aggregate, compute, and transform data in some scripted way, thereby promoting feature engineering, innovation, and reuse of data. It is essentially a Python library.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

Model-ready data refers to a feature library. For example, where verified data is present, the latencies are quantified. It enables users to aggregate, compute, and transform data in some scripted way, thereby promoting feature engineering, innovation, and reuse of data. It is essentially a Python library.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

The MLOps Blog

OCTOBER 3, 2023

How did you manage to jump from a more analytical, scientific type of role to a more engineering one? I actually did not pick up Python until about a year before I made the transition to a data scientist role. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps.

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