Understanding and applying statistical methods is essential in data science, analytics, and research. This course provides a hands-on approach to statistical analysis using Python, equipping learners with the ability to interpret data, draw meaningful conclusions, and build predictive models.
Starting with fundamental concepts, You will learn how to create data visualizations using Python libraries like Matplotlib and Seaborn. The course progresses to inferential statistics, covering hypothesis testing and confidence intervals. Learners will then explore statistical modeling techniques such as linear regression, logistic regression, and multilevel models. Additionally, an introduction to Bayesian inference will provide a deeper perspective on probabilistic data analysis.
This course is ideal for anyone looking to strengthen their statistical understanding, whether for academic research, business analytics, or machine learning applications. No prior experience in statistics is required, though basic Python knowledge will be beneficial.
Sections Included:
- Introduction to Statistics and Python – Overview of statistical thinking, Python libraries for analysis, and setting up Your data environment.
- Data Visualization with Python – Creating and interpreting plots using Matplotlib, Seaborn, and Pandas.
- Descriptive and Inferential Statistics – Understanding distributions, measures of central tendency, hypothesis testing, and confidence intervals.
- Linear and Logistic Regression – Building predictive models, assessing model performance, and interpreting results.
- Multilevel and Advanced Regression Models – Handling hierarchical data and improving model accuracy.
- Bayesian Inference Techniques – Introduction to Bayesian probability, priors, and posterior distributions.
- Applying Statistics to Real-World Problems – Case studies and hands-on projects analyzing business, healthcare, and social science datasets.
Benefits Upon Completion:
- Build statistical models using Python and apply them to real-world data.
- Develop expertise in visualization, regression, and inferential statistics.
- Learn to choose the right statistical techniques for different research questions.
- Gain practical experience with hands-on projects.
- Enhance career prospects in data science, analytics, and research.
