Pickl AI

APRIL 26, 2024

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

SEPTEMBER 26, 2023

Analyze the obtained sample data. Cluster Sampling Definition and applications Cluster sampling involves dividing a population into clusters or groups and selecting entire clusters at random for inclusion in the sample. Select clusters randomly from the population. Analyze the obtained sample data.

Analytics 98

Analytics Analytics Clustering Data Analysis 98

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

Pickl AI

MAY 15, 2024

Dreaming of a Data Science career but started as an Analyst? This guide unlocks the path from Data Analyst to Data Scientist Architect. Data Analyst to Data Scientist: Level-up Your Data Science Career The ever-evolving field of Data Science is witnessing an explosion of data volume and complexity.

Data Analyst 52

Data Analyst Data Scientist Data Science Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

APRIL 3, 2025

Summary: Data Analysis focuses on extracting meaningful insights from raw data using statistical and analytical methods, while data visualization transforms these insights into visual formats like graphs and charts for better comprehension. Is Data Analysis just about crunching numbers?

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Dataconomy

MARCH 19, 2025

This helps facilitate data-driven decision-making for businesses, enabling them to operate more efficiently and identify new opportunities. Definition and significance of data science The significance of data science cannot be overstated. Roles of data professionals Various professionals contribute to the data science ecosystem.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91

Pickl AI

DECEMBER 14, 2023

From predicting patient outcomes to optimizing inventory management, these techniques empower decision-makers to navigate data landscapes confidently, fostering informed and strategic decision-making. It is a mathematical framework that aims to capture the underlying patterns, trends, and structures present in the data.

Data Analysis 52

Data Analysis Data Analysis Decision Trees Analytics 52

Pickl AI

AUGUST 1, 2023

It helps in discovering hidden patterns and organizing text data into meaningful clusters. Topic Modeling and Document Clustering: Build a text mining project that performs topic modeling and document clustering. Cluster similar documents based on their content and explore relationships between topics.

Data Analysis 52

Data Analysis Data Analysis Python Support Vector Machines 52

Precisely

JULY 1, 2024

It is used to classify different data in different classes. Classification is similar to clustering in a way that it also segments data records into different segments called classes. But unlike clustering, here the data analysts would have the knowledge of different classes or cluster.

Data Mining 52

Data Mining Data Mining Data Mining Clustering 52

IBM Journey to AI blog

DECEMBER 19, 2023

Supervised learning Supervised learning techniques use real-world input and output data to detect anomalies. These types of anomaly detection systems require a data analyst to label data points as either normal or abnormal to be used as training data.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Mlearning.ai

JUNE 27, 2023

A Complete Guide about K-Means, K-Means ++, K-Medoids & PAM’s in K-Means Clustering. A Complete Guide about K-Means, K-Means ++, K-Medoids & PAM’s in K-Means Clustering. To address such tasks and uncover behavioral patterns, we turn to a powerful technique in Machine Learning called Clustering. K = 3 ; 3 Clusters.

Clustering 52

Clustering Algorithm Data Analyst Machine Learning 52

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

JUNE 18, 2023

Hadoop has become a highly familiar term because of the advent of big data in the digital world and establishing its position successfully. The technological development through Big Data has been able to change the approach of data analysis vehemently. But what is Hadoop and what is the importance of Hadoop in Big Data?

Hadoop 52

Hadoop Big Data Big Data Clustering 52

Pickl AI

JUNE 7, 2023

Analysing Netflix Movies and TV Shows One of the most enticing real-world Data Science projects Github can include the project focusing to analyse Netflix movies and TV shows. Using Netflix user data, you need to undertake Data Analysis for running workflows like EDA, Data Visualisation and interpretation.

Data Science 52

Data Science Deep Learning Deep Learning Clustering 52

Pickl AI

FEBRUARY 21, 2023

Understanding the appropriate ways to use data remains critical to success in finance, education and commerce. Accordingly, data collection from numerous sources is essential before data analysis and interpretation. Classification: This technique is used to classify data or groups based on specific attributes.

Data Mining 52

Data Mining Data Mining Data Mining Data Scientist 52

Pickl AI

AUGUST 6, 2024

Introduction Data anomalies, often referred to as outliers or exceptions, are data points that deviate significantly from the expected pattern within a dataset. Identifying and understanding these anomalies is crucial for data analysis, as they can indicate errors, fraud, or significant changes in underlying processes.

Data Quality 52

Data Quality Clustering Support Vector Machines Algorithm 52

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

MAY 29, 2024

Significance of Data Visualization Data visualization plays a crucial role in data analysis and decision-making for several reasons. It is not just about making data look pretty; it’s about unlocking the full potential of data to drive understanding, communication, and action.

Data Visualization 52

Data Visualization Analytics Analytics Power BI 52

O'Reilly Media

NOVEMBER 28, 2023

AI users say that AI programming (66%) and data analysis (59%) are the most needed skills. Data analysis showed a similar pattern: 70% total; 32% using AI, 38% experimenting with it. Using generative AI tools for tasks related to programming (including data analysis) is nearly universal.

AI 138

AI AI Data Analysis Data Analysis 138

Dataconomy

FEBRUARY 23, 2023

They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

Pickl AI

JUNE 12, 2023

How Professionals Can Use Tableau for Data Science? Tableau is a powerful data visualization and business intelligence tool that can be effectively used by professionals in the field of data science. This helps in understanding the underlying patterns, trends, and relationships within the data.

Tableau 52

Tableau Data Science Data Scientist Data Analysis 52

Pickl AI

SEPTEMBER 30, 2024

By reducing complex, high-dimensional data into two or three dimensions, MDS helps researchers uncover patterns and relationships that might otherwise go unnoticed. Its importance in Data Analysis lies in its ability to simplify complex datasets, making them easier to interpret and analyse. How Multidimensional Scaling Works?

Data Analysis 52

Data Analysis Data Analysis Python Algorithm 52

Pickl AI

SEPTEMBER 30, 2024

Summary: A Box Plot is a graphical representation summarising data distribution through key statistics like quartiles and outliers. It visualises central tendencies and variability, making it invaluable for Data Analysis. Box Plots help detect patterns by showing how data clusters around the median.

Data Analysis 52

Data Analysis Data Analysis Data Analyst Tableau 52

ODSC - Open Data Science

APRIL 13, 2023

5 Industries Using Synthetic Data in Practice Here’s an overview of what synthetic data is and a few examples of how various industries have benefited from it. Here are a few examples, including health record analysis and protein discovery. Here’s how. Learn more here. Here’s how to get there.

ML 52

ML ML Data Science Machine Learning 52

Pickl AI

JULY 10, 2023

The practise of picking a subset of the information from a larger set of information to analyse and make implications about the whole population is referred to as sampling in the field of data analysis. Furthermore, they can draw insights without studying the entire collection of data.

Clustering 40

Clustering Data Analysis Data Analysis Data Science 40

Pickl AI

JULY 18, 2023

Dealing with large datasets: With the exponential growth of data in various industries, the ability to handle and extract insights from large datasets has become crucial. Data science equips you with the tools and techniques to manage big data, perform exploratory data analysis, and extract meaningful information from complex datasets.

Computer Science 52

Computer Science Computer Science Data Science Machine Learning 52

Pickl AI

NOVEMBER 11, 2024

By identifying a clear, unimodal mode, researchers can quickly determine the most frequent or typical behaviour in the dataset, aiding in decision-making and trend analysis. Bimodal distributions are useful when the data has two peaks or clusters, reflecting two dominant groups within a single dataset.

Data Analysis 52

Data Analysis Data Analysis Hypothesis Testing Clustering 52

Pickl AI

SEPTEMBER 23, 2024

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. Introduction Artificial Intelligence (AI) and Data Science are revolutionising how we analyse data, make decisions, and solve complex problems.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Pickl AI

SEPTEMBER 23, 2024

Understanding their differences helps you choose the right tool for identifying hidden factors or simplifying data in fields like psychology, market research, genomics, and finance. Introduction Factor Analysis (FA) and Principal Component Analysis (PCA) are powerful statistical techniques use for Data Analysis.

Data Analysis 52

Data Analysis Data Analysis Exploratory Data Analysis EDA 52

Pickl AI

JULY 18, 2023

While it may not be a traditional programming language, SQL plays a crucial role in Data Science by enabling efficient querying and extraction of data from databases. SQL’s powerful functionalities help in extracting and transforming data from various sources, thus helping in accurate data analysis.

Data Science 52

Data Science SQL Data Scientist Python 52

DagsHub

JULY 4, 2024

Tool Application within AI: Scikit-learn is primarily used for traditional machine learning tasks such as classification, regression, clustering, and dimensionality reduction. Moreover, thanks to its user-friendly interface, experimenting with different models is often as simple as changing a single line of code.

Deep Learning 52

Deep Learning Deep Learning AI AI 52

Flipboard

MARCH 21, 2025

This approach eliminates any data duplication or data movement. Solution overview In this post, we explore two scenarios for sharing data between different teams (retail, marketing, and data analysts). Data analysts discover the data and subscribe to the data.

SQL 139

SQL Data Analyst Data Warehouse AWS 139

Pickl AI

NOVEMBER 19, 2024

Mastering this skill will give you deeper insights into datasets, making statistical analysis more accessible and actionable. Key Takeaways: Frequency measures how often values appear in a dataset, aiding data analysis. Visualise frequency data with bar charts and histograms for better understanding.

Python 52

Python Data Analyst Big Data Big Data 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

Mlearning.ai

MAY 23, 2023

What is the difference between data analytics and data science? Data science involves the task of transforming data by using various technical analysis methods to extract meaningful insights using which a data analyst can apply to their business scenarios. What is the main advantage of sampling?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

DrivenData Labs

FEBRUARY 3, 2025

Most solvers were data science professionals, professors, and students, but there were also many data analysts, project managers, and people working in public health and healthcare. Recently, I became interested in machine learning, so I was enrolled in the Yandex School of Data Analysis and Computer Science Center.

Machine Learning 130

Machine Learning Machine Learning Data Science Natural Language Processing 130