Computer Science Data Quality SQL 
Pickl AI
DECEMBER 25, 2024
Descriptive analytics is a fundamental method that summarizes past data using tools like Excel or SQL to generate reports. Techniques such as data cleansing, aggregation, and trend analysis play a critical role in ensuring data quality and relevance. Data Scientists rely on technical proficiency.
Pickl AI
APRIL 21, 2025
Real-World Example: Healthcare systems manage a huge variety of data: structured patient demographics, semi-structured lab reports, and unstructured doctor’s notes, medical images (X-rays, MRIs), and even data from wearable health monitors. Ensuring data quality and accuracy is a major challenge.
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Pickl AI
MARCH 3, 2025
Summary: Business Intelligence Analysts transform raw data into actionable insights. They use tools and techniques to analyse data, create reports, and support strategic decisions. Key skills include SQL, data visualization, and business acumen. Introduction We are living in an era defined by data.
Pickl AI
JULY 25, 2023
Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.
AWS Machine Learning Blog
NOVEMBER 1, 2024
These tasks include summarization, classification, information retrieval, open-book Q&A, and custom language generation such as SQL. Our experiments demonstrate that careful attention to data quality, hyperparameter optimization, and best practices in the fine-tuning process can yield substantial gains over base models.
Dataconomy
SEPTEMBER 27, 2024
Could you share the key milestones that have shaped your career in data analytics? My journey began at NUST MISiS, where I studied Computer Science and Engineering. I studied hard and was a very active student, which made me eligible for an exchange program at Häme University of Applied Sciences (HAMK) in Finland.
Dataconomy
JULY 25, 2023
Data science can be understood as a multidisciplinary approach to extracting knowledge and actionable insights from structured and unstructured data. It combines techniques from mathematics, statistics, computer science, and domain expertise to analyze data, draw conclusions, and forecast future trends.
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