Pickl AI

DECEMBER 25, 2024

Business Analytics requires business acumen; Data Science demands technical expertise in coding and ML. Descriptive analytics is a fundamental method that summarizes past data using tools like Excel or SQL to generate reports. Big data platforms such as Apache Hadoop and Spark help handle massive datasets efficiently.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

O'Reilly Media

SEPTEMBER 15, 2021

Given the difficulty of hiring expertise from outside, we expect an increasing number of companies to grow their own ML and AI talent internally using training programs. Languages like Python and SQL are table stakes: an applicant who can’t use them could easily be penalized, but competence doesn’t confer any special distinction.

AI 145

AI AI Azure AWS 145

The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams. DVC Git LFS neptune.ai

ML 52

ML ML Data Lakes Machine Learning 52

Mlearning.ai

MAY 23, 2023

Familiarity with libraries like pandas, NumPy, and SQL for data handling is important. In spite of all this, over the next few years I do expect the requirement for entry-level DS/ML roles to go down, as it did with SDE-role. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA).

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

DagsHub

DECEMBER 11, 2023

DVC tracks ML models and data sets (source: Iterative website ) Strengths Open source, and compatible with all major cloud platforms and storage types. Dolt Created in 2019, Dolt is an open-source tool for managing SQL databases that uses version control similar to Git. Most developers are familiar with Git for source code versioning.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98

Pickl AI

JANUARY 13, 2025

Proficiency in programming languages like Python and SQL. Familiarity with SQL for database management. Machine Learning (ML) Knowledge Understand various ML techniques, including supervised, unsupervised, and reinforcement learning. Hadoop , Apache Spark ) is beneficial for handling large datasets effectively.

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

APRIL 6, 2023

In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. Having a solid understanding of ML principles and practical knowledge of statistics, algorithms, and mathematics. Hands-on experience working with SQLDW and SQL-DB. Knowledge in using Azure Data Factory Volume.

Azure 52

Azure Data Engineering Data Engineering Data Engineering 52

IBM Journey to AI blog

JULY 6, 2023

Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. What is machine learning?

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. Views Views in GCP BigQuery are virtual tables defined by SQL query that can display the results of a query or be used as the base for other queries.

SQL 52

SQL Database Apache Hadoop Data Science 52

DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. This article will discuss managing unstructured data for AI and ML projects. You will learn the following: Why unstructured data management is necessary for AI and ML projects. How to properly manage unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Alation

JANUARY 25, 2022

This “analysis” is made possible in large part through machine learning (ML); the patterns and connections ML detects are then served to the data catalog (and other tools), which these tools leverage to make people- and machine-facing recommendations about data management and data integrations.

DataOps 52

DataOps SQL ML ML 52

Mlearning.ai

MARCH 15, 2023

Familiarity with Databases; SQL for structured data, and NOSQL for unstructured data. Knowledge of big data platforms like; Hadoop and Apache Spark. Experience with machine learning frameworks for supervised and unsupervised learning. Experience with cloud platforms like; AWS, AZURE, etc.

Data Science 52

Data Science Analytics Analytics Data Analyst 52

ODSC - Open Data Science

JANUARY 7, 2025

While knowing Python, R, and SQL is expected, youll need to go beyond that. As MLOps become more relevant to ML demand for strong software architecture skills will increase aswell. As MLOps become more relevant to ML demand for strong software architecture skills will increase aswell. Register now for only$299!

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

Pickl AI

DECEMBER 4, 2024

The rise of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML) , and Big Data analytics is reshaping industries and creating new opportunities for Data Scientists. Focus on Python and R for Data Analysis, along with SQL for database management. Here are five key trends to watch.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

APRIL 28, 2023

Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability Big Data: (..)

Data Scientist 52

Data Scientist Data Science Hypothesis Testing Decision Trees 52

IBM Journey to AI blog

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Pickl AI

JULY 8, 2024

Database Extraction: Retrieval from structured databases using query languages like SQL. Must Read Blogs: Elevate Your Data Quality: Unleashing the Power of AI and ML for Scaling Operations. API Integration: Accessing data through Application Programming Interfaces (APIs) provided by external services.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

DagsHub

AUGUST 23, 2024

Even the most sophisticated ML models, neural networks, or large language models require high-quality data to learn meaningful patterns. Then, that data was used as input for the ML model responsible for ad placement, which allowed users to monetize their games. It is SQL-based and integrates well with modern data warehouses.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

MAY 31, 2023

You should be skilled in using a variety of tools including SQL and Python libraries like Pandas. Proficiency in ML is understood when these are not just present in the aspirant in conceptual ways but also in terms of its applications in solving business problems. It is critical for knowing how to work with huge data sets efficiently.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Alation

FEBRUARY 20, 2020

Alation catalogs and crawls all of your data assets, whether it is in a traditional relational data set (MySQL, Oracle, etc), a SQL on Hadoop system (Presto, SparkSQL,etc), a BI visualization or something in a file system, such as HDFS or AWS S3. With Alation, you can search for assets across the entire data pipeline.

SQL 52

SQL Hadoop Analytics Analytics 52

How to Learn Machine Learning

MAY 3, 2025

Built on Google’s ML Infrastructure The same infrastructure that powers Google’s own AI applications is at your fingertips! Seamless Integration GCP AI Platform works harmoniously with popular ML frameworks like TensorFlow , scikit-learn , XGBoost, and PyTorch. Let’s break it down: 1.

Machine Learning 59

Machine Learning Machine Learning AI AI 59