Data Science Dojo

DECEMBER 14, 2023

It could explain how these distributions are used in different machine learning algorithms and why understanding them is crucial for data scientists. 32 datasets to uplift your skills in data science Data Science Dojo has created an archive of 32 data sets for you to use to practice and improve your skills as a data scientist.

Natural Language Processing 195

Natural Language Processing Exploratory Data Analysis Machine Learning Machine Learning 195

Towards AI

JULY 17, 2023

It involves exploratory data analysis, data cleansing, selecting the optimal set of independent variables, picking the most appropriate algorithm, implementing it efficiently, fine-tuning the parameters to predict the outcome more accurately, and a long list of other elements.

Machine Learning 98

Machine Learning Machine Learning Exploratory Data Analysis Data Analysis 98

Smart Data Collective

JUNE 4, 2021

Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models.

Data Science 133

Data Science Exploratory Data Analysis Machine Learning Machine Learning 133

Pickl AI

NOVEMBER 4, 2024

This article will guide you through effective strategies to learn Python for Data Science, covering essential resources, libraries, and practical applications to kickstart your journey in this thriving field. Key Takeaways Python’s simplicity makes it ideal for Data Analysis. in 2022, according to the PYPL Index.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

MAY 30, 2024

Summary: The Data Science and Data Analysis life cycles are systematic processes crucial for uncovering insights from raw data. Quality data is foundational for accurate analysis, ensuring businesses stay competitive in the digital landscape. billion INR by 2026, with a CAGR of 27.7%.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

AUGUST 27, 2024

Summary: Python simplicity, extensive libraries like Pandas and Scikit-learn, and strong community support make it a powerhouse in Data Analysis. It excels in data cleaning, visualisation, statistical analysis, and Machine Learning, making it a must-know tool for Data Analysts and scientists. Why Python?

Data Analysis 52

Data Analysis Data Analysis Python Data Analyst 52

Pickl AI

OCTOBER 14, 2024

Summary: This article explores different types of Data Analysis, including descriptive, exploratory, inferential, predictive, diagnostic, and prescriptive analysis. Introduction Data Analysis transforms raw data into valuable insights that drive informed decisions. What is Data Analysis?

Data Analysis 52

Data Analysis Data Analysis EDA Data Mining 52

Data Science Dojo

AUGUST 28, 2023

Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. Exploratory Data Analysis (EDA) Data collection: The first step in LLMOps is to collect the data that will be used to train the LLM.

Exploratory Data Analysis 195

Exploratory Data Analysis Data Preparation Machine Learning Machine Learning 195

Data Science Dojo

DECEMBER 14, 2023

It could explain how these distributions are used in different machine learning algorithms and why understanding them is crucial for data scientists. The data sets are categorized according to varying difficulty levels to be suitable for everyone.

Data Science 195

Data Science Natural Language Processing Machine Learning Machine Learning 195

Towards AI

FEBRUARY 26, 2024

Data Pre-Processing Handling Missing Values Encoding Categorical Variables Feature Scaling Data Splitting (Training and Validation) 4. Model Development & Model Evaluation Algorithm Selection Model Training Model Evaluation Metrics 1. You can see the code as mentioned below to gather data and to do exploratory data analysis.

Machine Learning 115

Machine Learning Machine Learning Exploratory Data Analysis Data Analysis 115

Towards AI

APRIL 19, 2024

The Use of LLMs: An Attractive Solution for Data Analysis Not only can LLMs deliver data analysis in a user-friendly and conversational format “via the most universal interface: Natural Language,” as Satya Nadella, the CEO of Microsoft, puts it, but also they can adapt and tailor their responses to immediate context and user needs.

Analytics 105

Analytics Analytics Data Analysis Data Analysis 105

IBM Data Science in Practice

NOVEMBER 21, 2022

Once you have downloaded the dataset, you can upload it to the Watson Studio instance by going to the Assets tab and then dropping the data files as shown below. Add Data You can access the data from the notebook once it has been added to the Watson Studio project. Dataframe head 2. sample(frac=0.8,

Deep Learning 130

Deep Learning Deep Learning Exploratory Data Analysis ML 130

Smart Data Collective

OCTOBER 17, 2022

Although a data pipeline can serve several functions, here are a few main use cases of them in the industry: Data Visualizations represent any data via graphics like plots, infographics, charts, and motion graphics. Data Pipeline Architecture Planning.

Data Pipeline 145

Data Pipeline Data Warehouse ETL Exploratory Data Analysis 145

Mlearning.ai

MAY 5, 2023

These models, which are based on artificial intelligence and machine learning algorithms, are designed to process vast amounts of natural language data and generate new content based on that data. It wasn’t until the development of deep learning algorithms in the 2000s and 2010s that LLMs truly began to take shape.

Natural Language Processing 98

Natural Language Processing Deep Learning Deep Learning Exploratory Data Analysis 98

Towards AI

FEBRUARY 3, 2024

From Predicting the behavior of a customer to automating many tasks, Machine learning has shown its capacity to convert raw data into actionable insights. Even though converting raw data into actionable insights, it is not determined by ML algorithms alone. This process is called Exploratory Data Analysis(EDA).

Machine Learning 122

Machine Learning Machine Learning Exploratory Data Analysis Data Scientist 122

Towards AI

AUGUST 16, 2023

We can apply a data-centric approach by using AutoML or coding a custom test harness to evaluate many algorithms (say 20–30) on the dataset and then choose the top performers (perhaps top 3) for further study, being sure to give preference to simpler algorithms (Occam’s Razor).

AI 98

AI AI Machine Learning Machine Learning 98

Pickl AI

MARCH 26, 2024

Summary: The KNN algorithm in machine learning presents advantages, like simplicity and versatility, and challenges, including computational burden and interpretability issues. Nevertheless, its applications across classification, regression, and anomaly detection tasks highlight its importance in modern data analytics methodologies.

K-nearest Neighbors 52

K-nearest Neighbors Machine Learning Machine Learning Algorithm 52

Dataconomy

AUGUST 16, 2023

Data scientists are the master keyholders, unlocking this portal to reveal the mysteries within. They wield algorithms like ancient incantations, summoning patterns from the chaos and crafting narratives from raw numbers. Model development : Crafting magic from algorithms!

Data Scientist 172

Data Scientist Data Science Machine Learning Machine Learning 172

Dataconomy

JUNE 29, 2023

Python machine learning packages have emerged as the go-to choice for implementing and working with machine learning algorithms. These libraries, with their rich functionalities and comprehensive toolsets, have become the backbone of data science and machine learning practices. Why do you need Python machine learning packages?

Machine Learning 113

Machine Learning Machine Learning Deep Learning Deep Learning 113

IBM Journey to AI blog

DECEMBER 20, 2023

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Flipboard

JANUARY 20, 2025

The existing algorithms were not efficient. He was director of science at Zilliant when he left to join the Gap, where he oversees three data science subteams: price optimization, inventory management, and fulfillment optimization. There are eight of what he calls spokes in data science.

Data Science 143

Data Science Data Scientist Exploratory Data Analysis Machine Learning 143

Dataconomy

JULY 28, 2023

In the digital age, the abundance of textual information available on the internet, particularly on platforms like Twitter, blogs, and e-commerce websites, has led to an exponential growth in unstructured data. Text data is often unstructured, making it challenging to directly apply machine learning algorithms for sentiment analysis.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. It entails developing computer programs that can improve themselves on their own based on expertise or data. What is Unsupervised Machine Learning?

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

Ocean Protocol

DECEMBER 12, 2023

Abstract This research report encapsulates the findings from the Curve Finance Data Challenge , a competition that engaged 34 participants in a comprehensive analysis of the decentralized finance protocol. Part 1: Exploratory Data Analysis (EDA) MEV Over 25,000 MEV-related transactions have been executed through Curve.

Exploratory Data Analysis 52

Exploratory Data Analysis Predictive Analytics EDA Data Analysis 52

Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Through Exploratory Data Analysis , imputation, and outlier handling, robust models are crafted. Time features Objective: Extracting valuable information from time-related data.

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Pickl AI

DECEMBER 3, 2024

In Python, commonly used libraries include: Pandas: For data manipulation and analysis, particularly for handling structured data. Scikit-learn: For Machine Learning algorithms and preprocessing utilities. Matplotlib/Seaborn: For data visualization. NumPy: For numerical operations and handling arrays.

Python 52

Python ML ML Exploratory Data Analysis 52

DataRobot

DECEMBER 20, 2021

it’s possible to build a robust image recognition algorithm with high accuracy. Who Can Benefit from the Visual Data? Submit Data. After Exploratory Data Analysis is completed, you can look at your data. Image recognition is one of the most relevant areas of machine learning.

Clustering 97

Clustering Deep Learning Deep Learning Exploratory Data Analysis 97

Ocean Protocol

MARCH 11, 2024

METAR, Miami International Airport (KMIA) on March 9, 2024, at 15:00 UTC In the recently concluded data challenge hosted on Desights.ai , participants used exploratory data analysis (EDA) and advanced artificial intelligence (AI) techniques to enhance aviation weather forecasting accuracy.

Exploratory Data Analysis 52

Exploratory Data Analysis Machine Learning Machine Learning EDA 52

Towards AI

FEBRUARY 21, 2023

First of all, HR needs to collect comprehensive data about an employee, such as education, salary, experience… We also need data from supervisors such as performance, relationships, promotions… After that, HR can use this information to predict employees’ tendency to leave and take preventive action. TRAIN ==Staying Rate: 83.87%Leaving

Machine Learning 98

Machine Learning Machine Learning Python Exploratory Data Analysis 98

Becoming Human

MAY 15, 2023

These communities will help you to be updated in the field, because there are some experienced data scientists posting the stuff, or you can talk with them so they will also guide you in your journey. Data Analysis After learning math now, you are able to talk with your data.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

FEBRUARY 22, 2024

Summary: Dive into programs at Duke University, MIT, and more, covering Data Analysis, Statistical quality control, and integrating Statistics with Data Science for diverse career paths. offer modules in Statistical modelling, biostatistics, and comprehensive Data Science bootcamps, ensuring practical skills and job placement.

Hypothesis Testing 52

Hypothesis Testing Data Science Data Analysis Data Analysis 52

Pickl AI

JANUARY 29, 2024

Summary: In the tech landscape of 2024, the distinctions between Data Science and Machine Learning are pivotal. Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation. billion by 2029.

Data Science 52

Data Science ML ML Machine Learning 52

DataRobot Blog

DECEMBER 15, 2022

If your dataset is not in time order (time consistency is required for accurate Time Series projects), DataRobot can fix those gaps using the DataRobot Data Prep tool , a no-code tool that will get your data ready for Time Series forecasting. Prepare your data for Time Series Forecasting. Perform exploratory data analysis.

Exploratory Data Analysis 81

Exploratory Data Analysis AI AI Machine Learning 81

Pickl AI

JULY 18, 2023

By transitioning from computer science to data science, you can tap into a broader range of job opportunities and potentially increase your earning potential. Leveraging existing skills: Computer science provides a strong foundation in programming, algorithms, and problem-solving, which are highly valuable in data science.

Computer Science 52

Computer Science Computer Science Data Science Machine Learning 52

AWS Machine Learning Blog

JUNE 23, 2023

We use this extracted dataset for exploratory data analysis and feature engineering. You can choose to sample the data from Snowflake in the SageMaker Data Wrangler UI. Another option is to download complete data for your ML model training use cases using SageMaker Data Wrangler processing jobs.

ML 98

ML ML Database AWS 98

Pickl AI

APRIL 26, 2024

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. How would you segment customers based on their purchasing behaviour?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Mlearning.ai

APRIL 10, 2023

With the emergence of data science and AI, clustering has allowed us to view data sets that are not easily detectable by the human eye. Thus, this type of task is very important for exploratory data analysis. 3 feature visual representation of a K-means Algorithm.

Clustering 52

Clustering Internet of Things Algorithm Machine Learning 52