What will you learn in Statistics with Python Specialization Course
Identify data types, implement exploratory data visualization, and manage study design considerations using Python.
Execute statistical inference including confidence intervals, hypothesis testing, and regression modeling (linear, logistic, multilevel).
Interpret results using both classical and Bayesian frameworks, and apply techniques like modeling and sampling to real-world datasets.
Program Overview
Module 1: Understanding and Visualizing Data with Python
⏳ 4 weeks
• Topics: Data types, exploratory visualization (histograms, box-plots), summary statistics, sampling methods
• Hands-on: Use Jupyter notebooks to identify variables, create visual summaries, and implement sampling strategies in Python
Module 2: Inferential Statistical Analysis with Python
⏳ 4 weeks
• Topics: Construct confidence intervals, run hypothesis tests, distinguish between one- and two-sample analysis
• Hands-on: Perform inference procedures in Python using Pandas, Statsmodels, and Seaborn across real sample datasets
Module 3: Fitting Statistical Models to Data with Python
⏳ 4 weeks
• Topics: Linear regression, logistic regression, multilevel models, Bayesian inference techniques
• Hands-on: Fit, evaluate, and interpret statistical models using Python, aligning insights with research questions and statistical frameworks
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Job Outlook
Equips learners with statistical programming skills essential for Data Analyst, Data Scientist, Research Statistician, and BI Analyst roles.
Python-based statistics are increasingly valued across healthcare, finance, government research, and tech sectors.
Builds a strong foundation for careers in data-driven decision-making and advanced analytics.
Specification: Statistics with Python Specialization
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