Data Science Blog

FEBRUARY 23, 2023

Along with the rapid progress of deep learning mentioned above, a lot of hypes and catchphrases regarding big data and machine learning were made, and an interesting one is “Data is the new oil.” ” That might have been said only because big data is sources of various industries.

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Supervised Learning Machine Learning Machine Learning Deep Learning 264

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Mlearning.ai

MAY 23, 2023

Blind 75 LeetCode Questions - LeetCode Discuss Data Manipulation and Analysis Proficiency in working with data is crucial. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA).

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

AWS Machine Learning Blog

AUGUST 12, 2024

Figure 7: Using SageMaker Data Wrangler’s chat for data prep to run SQL statements Check for data quality SageMaker Canvas also provides exploratory data analysis (EDA) capabilities that allow you to gain deeper insights into the data prior to the ML model build step.

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Pickl AI

JULY 20, 2023

Defining clear objectives and selecting appropriate techniques to extract valuable insights from the data is essential. Here are some project ideas suitable for students interested in big data analytics with Python: 1. Sentiment Analysis on Social Media Data: Gather tweets or reviews from a social media platform using APIs.

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Analytics Analytics Big Data Big Data 40

DrivenData Labs

APRIL 10, 2024

Combining deep and practical understanding of technology, computer vision and AI with experience in big data architectures. A data geek by heart. What motivated you to compete in this challenge?

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Deep Learning Deep Learning Machine Learning Machine Learning 130

Mlearning.ai

JULY 11, 2023

For instance, feature engineering and exploratory data analysis (EDA) often require the use of visualization libraries like Matplotlib and Seaborn. In the data science industry, effective communication and collaboration play a crucial role. Moreover, tools like Power BI and Tableau can produce remarkable results.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40

Pickl AI

FEBRUARY 12, 2024

Course Topics: Introduction to Data Science Data Acquisition and Cleaning Exploratory Data Analysis (EDA) Statistical Analysis Programming for Data Science Machine Learning Basics Supervised Learning Algorithms Unsupervised Learning Algorithms Introduction to Deep Learning Big Data and Cloud Computing Top Data Science Interview Questions and Expert (..)

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Pickl AI

SEPTEMBER 12, 2024

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.

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Data Analyst Data Science Machine Learning Machine Learning 52

ODSC - Open Data Science

JULY 19, 2023

Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow. A typical workflow is illustrated here from data ingestion, EDA (Exploratory Data Analysis), experimentation, model development and evaluation, to the registration of a candidate model for production.

Machine Learning 52

Machine Learning Machine Learning Azure Data Science 52