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 Course
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FAQs
- Designed for beginners, no prior Python experience necessary.
- Introduces statistical concepts progressively using Python.
- Includes hands-on exercises in Jupyter notebooks for practical learning.
- Covers exploratory data analysis, hypothesis testing, and regression modeling.
- Ideal for learners aiming to build a foundation in data analysis and statistical thinking.
- Covers histograms, box plots, and scatter plots for univariate and multivariate data.
- Teaches best practices for exploratory data visualization.
- Includes hands-on exercises with Pandas, Matplotlib, and Seaborn.
- Helps learners summarize datasets and identify patterns effectively.
- Prepares learners to communicate insights clearly in data-driven projects.
- Builds skills for Data Analyst, Data Scientist, and BI Analyst roles.
- Strengthens analytical thinking and evidence-based decision-making.
- Prepares learners to handle real-world datasets and apply statistical models.
- Valuable across healthcare, finance, tech, and government research sectors.
- Supports progression to advanced data science or machine learning courses.
- 3 modules, approximately 4 weeks each.
- Covers data visualization, inferential statistics, and model fitting.
- Self-paced learning allows flexible scheduling.
- Includes exercises, quizzes, and projects for each module.
- Suitable for learners seeking structured yet flexible data science training.
- Teaches classical inference: confidence intervals, hypothesis testing.
- Covers regression modeling: linear, logistic, and multilevel models.
- Introduces Bayesian frameworks for probabilistic reasoning.
- Provides hands-on exercises aligning methods with research questions.
- Prepares learners to analyze and interpret data using multiple statistical paradigms.
