Pickl AI

JULY 25, 2023

At the core of Data Science lies the art of transforming raw data into actionable information that can guide strategic decisions. Role of Data Scientists Data Scientists are the architects of data analysis. 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

Mlearning.ai

JULY 11, 2023

For example, when it comes to deploying projects on cloud platforms, different companies may utilize different providers like AWS, GCP, or Azure. For instance, feature engineering and exploratory data analysis (EDA) often require the use of visualization libraries like Matplotlib and Seaborn.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40

Pickl AI

JULY 12, 2024

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

JULY 20, 2023

Kaggle datasets) and use Python’s Pandas library to perform data cleaning, data wrangling, and exploratory data analysis (EDA). Extract valuable insights and patterns from the dataset using data visualization libraries like Matplotlib or Seaborn.

Analytics 40

Analytics Analytics Big Data Big Data 40

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

AWS Machine Learning Blog

AUGUST 12, 2024

Furthermore, the democratization of AI and ML through AWS and AWS Partner solutions is accelerating its adoption across all industries. For example, a health-tech company may be looking to improve patient care by predicting the probability that an elderly patient may become hospitalized by analyzing both clinical and non-clinical data.

ML 117

ML ML AWS AI 117

AWS Machine Learning Blog

SEPTEMBER 12, 2023

Solution overview Scalable Capital’s ML infrastructure consists of two AWS accounts: one as an environment for the development stage and the other one for the production stage. The following diagram shows the workflow for our email classifier project, but can also be generalized to other data science projects. Use Version 2.x

Data Science 123

Data Science Data Scientist AWS ML 123

Mlearning.ai

JUNE 16, 2023

GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API. The data would be interesting to analyze. From Data Engineering to Prompt Engineering Prompt to do data analysis BI report generation/data analysis In BI/data analysis world, people usually need to query data (small/large).

AI 52

AI AI Data Analysis Data Analysis 52

Towards AI

JANUARY 22, 2025

The role of a data scientist is changing so fast that often schools cant keep up. Universities still mostly focus on things like EDA, data cleaning, and building/fine-tune models. Simply put, focusing solely on data analysis, coding or modeling will no longer cuts it for most corporate jobs. What to do then?

Data Scientist 105

Data Scientist EDA Exploratory Data Analysis AI 105