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From Data to Decisions: Deep Dive into Workshop Learnings

Women in Big Data

Learning Objectives Recap: Paradigms in Data Science: We explored the two main paradigms in data science: descriptive analytics (understanding what happened in the past) and predictive analytics (using models to forecast future outcomes). This allows us to make generalizations about populations based on samples.

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Data Analysis vs. Data Visualization – More Than Just Pretty Charts

Pickl AI

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. Effective visualisation relies on accurate analytics for meaningful representation.

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Understanding Data Science and Data Analysis Life Cycle

Pickl AI

Similarly, the Data and Analytics market is set to grow at a CAGR of 12.85% , reaching 15,313.99 From identifying customer trends to predicting market demand, Data Scientists utilise their analytical skills to unlock the potential hidden within data. More to read: How is Data Visualization helpful in Business Analytics?

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Exploring Different Types of Data Analysis: Methods and Applications

Pickl AI

Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is an approach to analyse datasets to uncover patterns, anomalies, or relationships. The primary purpose of EDA is to explore the data without any preconceived notions or hypotheses. Clustering: Grouping similar data points to identify segments within the data.

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How To Learn Python For Data Science?

Pickl AI

Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesis testing, confidence intervals). Perform exploratory Data Analysis (EDA) using Pandas and visualise your findings with Matplotlib or Seaborn. These concepts help you analyse and interpret data effectively.

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Formula 1 Racing Challenge: 2024 Strategy Analysis

Ocean Protocol

By conducting exploratory data analysis (EDA), they will identify relationships between these variables and generate insights on how strategy impacts race outcomes. Participants will use EDA and statistical analysis to understand how tire management and pit stop decisions impact race outcomes.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Together, data engineers, data scientists, and machine learning engineers form a cohesive team that drives innovation and success in data analytics and artificial intelligence. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc. Excel, Tableau, Power BI, SQL Server, MySQL, Google Analytics, etc.