Advanced Certificate in STEM Data Analysis: Statistical Techniques
-- ViewingNowThe Advanced Certificate in STEM Data Analysis: Statistical Techniques is a comprehensive course that equips learners with essential skills in data analysis, with a focus on statistical techniques in STEM fields. This course is critical for those looking to advance their careers in data analysis, as it provides hands-on experience with industry-standard tools and techniques, including Python, R, and SAS.
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⢠Advanced Regression Analysis: This unit will cover various regression models and techniques to analyze the relationship between dependent and independent variables. It will include multiple linear regression, logistic regression, and polynomial regression.
⢠Time Series Analysis: This unit will focus on techniques for analyzing time series data, including decomposition, autocorrelation, moving averages, and ARIMA models.
⢠Machine Learning Techniques for Data Analysis: This unit will introduce students to various machine learning techniques, such as decision trees, random forests, and support vector machines, and how to apply them to data analysis.
⢠Multivariate Analysis: This unit will cover techniques for analyzing data with multiple dependent variables, including factor analysis, discriminant analysis, and cluster analysis.
⢠Applied Data Analysis with Python: This unit will teach students how to use the Python programming language for data analysis, including data cleaning, visualization, and statistical modeling.
⢠Data Visualization: This unit will cover best practices for data visualization, including creating effective charts and graphs, and using visualization tools like matplotlib, seaborn, and Tableau.
⢠Experimental Design and Analysis: This unit will cover the principles of experimental design, including randomization, blocking, and replication, and how to analyze experimental data using ANOVA and other techniques.
⢠Bayesian Data Analysis: This unit will introduce students to Bayesian data analysis, including Bayes' theorem, prior and posterior distributions, and Markov chain Monte Carlo methods.
⢠Big Data Analytics: This unit will cover the challenges and opportunities of analyzing large, complex datasets, including distributed computing, parallel processing, and data mining techniques.
⢠Ethics and Privacy in Data Analysis: This unit will cover the ethical and privacy considerations of data analysis, including data ownership, informed consent, and data security.
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