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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.
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.
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?
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.
Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesistesting, 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.
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 artificial intelligence. Statistical Analysis: Hypothesistesting, probability, regression analysis, etc. Excel, Tableau, Power BI, SQL Server, MySQL, Google Analytics, 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.
Step 2: Exploratory Data Analysis (EDA): Before running Regression Analysis, it’s essential to perform EDA to visualise data distributions and identify any outliers or patterns that may influence results. This data can come from various sources such as surveys, experiments, or historical records.
Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses. Inferential Statistics: A branch of statistics that makes inferences about a population based on a sample, allowing for hypothesistesting and confidence intervals.
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