Global Certificate in Data Analysis Essentials: Analytical Methods
-- ViewingNowThe Global Certificate in Data Analysis Essentials: Analytical Methods is a comprehensive course that equips learners with essential data analysis skills demanded by various industries. This certification program focuses on teaching analytical methods that enable data-driven decision-making, a critical skill in today's data-driven world.
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⢠Introduction to Data Analysis: Defining data analysis, understanding its importance, and exploring various data analysis techniques and tools.
⢠Data Collection Methods: Exploring different data collection methods, including surveys, interviews, and observation, and understanding how to choose the appropriate method for a given scenario.
⢠Data Cleaning and Preparation: Learning how to clean, prepare, and transform data for analysis, including handling missing values, outliers, and data formatting.
⢠Descriptive Statistics: Understanding measures of central tendency, dispersion, and association, and how to calculate and interpret these measures for a given dataset.
⢠Inferential Statistics: Learning how to make inferences about a population based on a sample, including hypothesis testing, confidence intervals, and p-values.
⢠Data Visualization: Exploring various data visualization techniques, including charts, graphs, and maps, and understanding how to choose the appropriate visualization for a given dataset.
⢠Regression Analysis: Understanding the principles of regression analysis, including simple linear regression, multiple regression, and logistic regression, and how to apply these techniques to real-world datasets.
⢠Time Series Analysis: Learning how to analyze data over time, including understanding trends, seasonality, and cyclical patterns, and how to forecast future values.
⢠Data Mining and Machine Learning: Exploring various data mining and machine learning techniques, including clustering, classification, and prediction, and understanding how to apply these techniques to real-world datasets.
⢠Ethics and Data Analysis: Understanding the ethical considerations of data analysis, including data privacy, confidentiality, and informed consent.
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