Towards AI

JANUARY 27, 2023

In the increasingly competitive world, understanding the data and taking quicker actions based on that help create differentiation for the organization to stay ahead! It is used to discover trends [2], patterns, relationships, and anomalies in data, and can help inform the development of more complex models [3].

Exploratory Data Analysis 98

Exploratory Data Analysis Data Analysis Data Analysis EDA 98

IBM Journey to AI blog

MAY 17, 2024

Without knowing what to look for, business analysts can miss critical insights, making dashboards less effective for exploratory data analysis and real-time decision-making. The complexity increases when trying to maintain data consistency and security across multiple platforms.

Exploratory Data Analysis 101

Exploratory Data Analysis Data Analysis Data Analysis Business Intelligence 101

Pickl AI

NOVEMBER 15, 2023

Data Lakes embrace raw, unstructured data, while Data Warehouses focus on processed, organized information. Data Lake Example Data Lakes serve as versatile repositories for a wide range of raw and unstructured data, providing organizations with the flexibility to derive valuable insights.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Pickl AI

APRIL 26, 2024

I conducted thorough data validation, collaborated with stakeholders to identify the root cause, and implemented corrective measures to ensure data integrity. I would perform exploratory data analysis to understand the distribution of customer transactions and identify potential segments.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

JULY 25, 2023

Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. They clean and preprocess the data to remove inconsistencies and ensure its quality.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Heartbeat

MAY 29, 2023

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Snorkel AI

MAY 12, 2023

Our data teams focus on three important processes. First, data standardization, then providing model-ready data for data scientists, and then ensuring there’s strong data governance and monitoring solutions and tools in place. For example, where verified data is present, the latencies are quantified.

Machine Learning 52

Machine Learning Machine Learning ML ML 52