Learn from fundamentals to advanced concepts with hands-on projects
Master the mathematical and statistical foundations of machine learning
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Login to EnrollMaster essential statistical concepts required for machine learning, from basic descriptive statistics to advanced inferential methods
Login to EnrollMaster SQL basics essential for data analysis and database management
Login to EnrollComplex queries, performance optimization, and advanced database concepts
Login to EnrollMaster SQL programming from fundamentals to advanced database querying, data manipulation, and database design
Login to EnrollIn this course, you will learn the fundamentals of data science in R
Login to EnrollIn this course, you will learn R topics that will equip with the skills needed to conduct data preprocessing, data preparation, and data analysis in R
Login to EnrollExplore machine learning, time series analysis, Shiny applications, text mining, package development, and performance optimization.
Login to EnrollCreating data, libraries and datasets, variable attributes, importing data, manipulating data, and type conversion — the foundational sequence for working with data in SAS.
Login to EnrollThe SAS procedures used for statistics, modeling, and analysis: descriptive statistics and reporting (MEANS, PRINT, REPORT, SQL), statistical modeling (REG, GLM, LOGISTIC, GENMOD), survival analysis (LIFETEST, PHREG, LIFEREG), and an introduction to SAS/IML.
Login to EnrollSAS functions in depth: numeric, missing-value, and conditional functions, advanced text-processing functions, and working with SAS dates.
Login to EnrollThe SAS macro language from the basics through intermediate/advanced programming: macro variables, %MACRO, conditional and iterative macro logic, and data-driven code generation.
Login to EnrollBuilding visualizations in SAS with PROC SGPLOT and related procedures.
Login to EnrollUsing dictionary tables and SASHELP metadata views to programmatically discover datasets and variables, building macro-driven loops that apply the same operation across an entire library, batch file/path management, and detecting and cleaning non-ASCII characters at scale.
Login to EnrollGenerating simulated data in SAS: the RAND function family, reproducible random-number seeds, building full simulated datasets, Monte Carlo methods, and bootstrap resampling.
Login to EnrollTechniques for handling missing data in SAS: Last Observation Carried Forward (LOCF) and simple substitution for longitudinal and cross-sectional data, and multiple imputation with PROC MI and PROC MIANALYZE.
Login to EnrollConnecting SAS to external SQL databases: LIBNAME database engines (ODBC, and dedicated engines like SQLSVR/ORACLE), implicit pass-through, and explicit PROC SQL pass-through for native SQL and performance-critical queries.
Login to EnrollAn overview of CDISC clinical trial data standards for SAS programmers: SDTM vs ADaM, Implementation Guides, controlled terminology, the SDTM Findings-domain pattern (--TESTCD/--ORRES/--STRESC/--STRESN), and ADaM BDS analysis datasets (PARAM/PARAMCD/AVAL/AVALC, baseline derivation, change from baseline).
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