Data Science Dojo

APRIL 29, 2025

Data Collection Once the problem is defined, the next step in the data workflow is collecting relevant data. In football analytics, this could mean pulling data from several sources, including event and player performance data. Tracking Data: Player movements and positioning.

Power BI 195

Power BI Analytics Analytics EDA 195

Dataconomy

AUGUST 16, 2023

A model builder: Data scientists create models that simulate real-world processes. These models can predict future events, classify data into categories, or uncover relationships between variables, enabling better decision-making.

Data Scientist 172

Data Scientist Data Science Machine Learning Machine Learning 172

Snorkel AI

JULY 3, 2023

While there are a lot of benefits to using data pipelines, they’re not without limitations. Traditional exploratory data analysis is difficult to accomplish using pipelines given that the data transformations achieved at each step are overwritten by the proceeding step in the pipeline. JG : Exactly.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Data Scientist Machine Learning 52

Pickl AI

APRIL 3, 2025

It involves handling missing values, correcting errors, removing duplicates, standardizing formats, and structuring data for analysis. Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) EDA: Calculate overall churn rate.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Pickl AI

JULY 20, 2023

Diagnostic Analytics Projects: Diagnostic analytics seeks to determine the reasons behind specific events or patterns observed in the data. It involves deeper analysis and investigation to identify the root causes of problems or successes. Root cause analysis is a typical diagnostic analytics task.

Analytics 40

Analytics Analytics Big Data Big Data 40

Snorkel AI

JULY 3, 2023

While there are a lot of benefits to using data pipelines, they’re not without limitations. Traditional exploratory data analysis is difficult to accomplish using pipelines given that the data transformations achieved at each step are overwritten by the proceeding step in the pipeline. JG : Exactly.

Data Pipeline 52

Data Pipeline Exploratory Data Analysis Data Scientist Machine Learning 52

Snorkel AI

JULY 3, 2023

While there are a lot of benefits to using data pipelines, they’re not without limitations. Traditional exploratory data analysis is difficult to accomplish using pipelines given that the data transformations achieved at each step are overwritten by the proceeding step in the pipeline. JG : Exactly.

Data Pipeline 52

Data Pipeline Exploratory Data Analysis Data Scientist Machine Learning 52