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.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Pickl AI

DECEMBER 2, 2024

Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets. Processing frameworks like Hadoop enable efficient data analysis across clusters. It is known for its high fault tolerance and scalability.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

SEPTEMBER 24, 2024

Read More: Use of AI and Big Data Analytics to Manage Pandemics Overview of Uber’s Data Analytics Strategy Uber’s Data Analytics strategy is multifaceted, focusing on real-time data collection, predictive analytics, and Machine Learning.

Analytics 52

Analytics Analytics Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 25, 2024

Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets. Processing frameworks like Hadoop enable efficient data analysis across clusters. It is known for its high fault tolerance and scalability.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

DECEMBER 4, 2024

A key aspect of this evolution is the increased adoption of cloud computing, which allows businesses to store and process vast amounts of data efficiently. According to recent statistics, 56% of healthcare organisations have adopted predictive analytics to improve patient outcomes.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

APRIL 2, 2024

There are three main types, each serving a distinct purpose: Descriptive Analytics (Business Intelligence): This focuses on understanding what happened. Think of it as summarizing past data to answer questions like “Which products are selling best?” ” or “What are our customer demographics?”

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52

Pickl AI

MAY 29, 2024

Understanding Data Science Data Science is a multidisciplinary field that combines statistics, mathematics, computer science, and domain-specific knowledge to extract insights and wisdom from structured and unstructured data. Data Science has been critical in providing insights and solutions based on Data Analysis.

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Data Scientist Data Science Machine Learning Machine Learning 52

IBM Journey to AI blog

JULY 6, 2023

Machine learning can then “learn” from the data to create insights that improve performance or inform predictions. Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

JANUARY 28, 2025

Effective data modeling enhances the usability of the BI system by making it easier to navigate through complex datasets. Data Analysis At this stage, organizations use various analytical techniques to derive insights from the stored data: Descriptive Analytics: Provides insights into past performance by summarizing historical data.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

JULY 20, 2023

It involves deeper analysis and investigation to identify the root causes of problems or successes. Root cause analysis is a typical diagnostic analytics task. 3. Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes.

Analytics 40

Analytics Analytics Big Data Big Data 40

Pickl AI

OCTOBER 10, 2024

It integrates well with cloud services, databases, and big data platforms like Hadoop, making it suitable for various data environments. Typical use cases include ETL (Extract, Transform, Load) tasks, data quality enhancement, and data governance across various industries.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52