Global Certificate in Data Science Essentials: Analytical Techniques
-- ViewingNowThe Global Certificate in Data Science Essentials: Analytical Techniques is a comprehensive course that imparts critical data science skills in high demand by today's industries. This program equips learners with essential knowledge in data manipulation, visualization, and analytical techniques using real-world data, empowering them to derive valuable insights and make data-driven decisions.
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⢠Data Collection Techniques: An introduction to various data collection methods, including surveys, web scraping, interviews, and experiments. Emphasis on selecting the appropriate method based on the research question and data type.
⢠Data Cleaning and Pre-processing: Techniques for cleaning and preparing datasets for analysis, such as handling missing values, outliers, and inconsistent data.
⢠Data Visualization: An overview of data visualization techniques, including chart types, color theory, and best practices for creating effective visualizations.
⢠Exploratory Data Analysis: Methods for exploring and summarizing datasets, including measures of central tendency, variability, and correlation.
⢠Statistical Inference: Foundational concepts of statistical inference, including hypothesis testing, confidence intervals, and p-values.
⢠Regression Analysis: Techniques for modeling relationships between variables using linear and logistic regression. Includes an introduction to assumptions, diagnostics, and model selection.
⢠Machine Learning Fundamentals: Overview of machine learning approaches, including supervised, unsupervised, and reinforcement learning.
⢠Experimental Design and Causal Inference: Methods for designing experiments to test causal relationships, including randomized controlled trials and natural experiments.
⢠Ethics in Data Science: Discussion of ethical considerations in data science, including privacy, bias, and fairness in algorithmic decision-making.
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