What will you learn in Data Analysis with R Specialization Course
Build a deep understanding of statistical reasoning and data analysis
Perform exploratory data analysis and apply statistical methods using Python
Conduct hypothesis testing and construct confidence intervals
Apply linear regression and build statistical models for real-world datasets
Utilize Python libraries like NumPy, pandas, matplotlib, and statsmodels for analysis
Program Overview
Course 1: Understanding and Visualizing Data with Python
⏳ 2 weeks
Topics: Data types, summary statistics, visualizations, probability
Hands-on: Creating histograms, box plots, scatter plots using Python
Course 2: Inferential Statistical Analysis with Python
⏳ 2 weeks
Topics: Central Limit Theorem, hypothesis testing, p-values, confidence intervals
Hands-on: Conducting statistical inference and A/B testing
Course 3: Fitting Statistical Models to Data with Python
⏳ 3 weeks
Topics: Linear regression, model diagnostics, categorical variables, ANOVA
Hands-on: Building and interpreting regression models with Python
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Job Outlook
High demand for data-literate professionals in roles such as Data Analyst, Business Analyst, and Researcher
Strong foundation for further study in Data Science and Machine Learning
Salary range: $65,000–$120,000/year depending on experience and specialization
Valuable in industries like healthcare, finance, tech, academia, and marketing
Freelance and consulting opportunities in statistical modeling and data storytelling
Specification: Data Analysis with R Specialization
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FAQs
- R was built specifically for statistics and data analysis.
- Has a wide range of packages like ggplot2, dplyr, caret.
- Easier to use for quick statistical modeling.
- Offers strong visualization capabilities.
- Widely used in academia and research communities.
- No prior coding required; concepts are explained clearly.
- Step-by-step introduction to R programming basics.
- Includes practical datasets for guided learning.
- Progresses from visualization to advanced models gradually.
- Supportive structure for absolute beginners.
- Teaches data cleaning, visualization, and modeling.
- Builds statistical reasoning for business and research.
- Prepares you for roles like Data Analyst or Business Analyst.
- Skills transferable to finance, healthcare, and marketing.
- Strengthens resume with an in-demand specialization.
- Builds a strong statistical foundation.
- Prepares you for machine learning by covering regression models.
- Encourages progression into predictive modeling.
- Helps you understand data distributions and testing.
- Serves as a stepping stone to advanced courses.
- Essential for social science and medical research.
- Simplifies reproducible research with R Markdown.
- Helps analyze survey, experimental, and clinical data.
- Provides credibility when publishing in data-driven fields.
- Enhances interdisciplinary collaboration.
