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His expertise in ArtificialIntelligence and Machine Learning and engaging teaching style made the workshop an enriching experience. HypothesisTesting in Action: We learned how to formulate a null hypothesis (no difference exists) and an alternative hypothesis (a difference exists) and use statistical tests to evaluate their validity.
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.
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.
Together, data engineers, data scientists, and machine learning engineers form a cohesive team that drives innovation and success in data analytics and artificialintelligence. Statistical Analysis: Hypothesistesting, probability, regression analysis, etc. Data Visualization: Matplotlib, Seaborn, Tableau, etc.
In Inferential Statistics, you can learn P-Value , T-Value , HypothesisTesting , and A/B Testing , which will help you to understand your data in the form of mathematics. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.
A/B Testing: A statistical method for comparing two versions of a variable to determine which one performs better. ArtificialIntelligence (AI): A branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence.
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