Course Description

This course on Statistics for Data Science equips you with the fundamental statistical methods necessary for data analysis. Aimed at practical application, it covers essential topics such as data gathering, descriptive statistics, data visualization, probability distributions, and various forms of hypothesis testing. Emphasizing a hands-on approach, it utilizes Python and Jupyter Notebooks—key tools for data scientists and analysts—to carry out statistical analysis. By the end of the course, you'll undertake a comprehensive project that involves solving a data-science problem derived from a real-world scenario.

What Students Will Learn

• Utilization of central tendency and dispersion measures on different types of data.
• Skills to summarize, present, and visualize data effectively, offering practical insights.
• Ability to identify and apply the correct hypothesis tests for varied datasets.
• Expertise in conducting thorough hypothesis testing, correlation, and regression analysis.
• Proficiency in using Python and Jupyter Notebooks for statistical analysis.

Pre-requisites or Skills Necessary

While no specific computer science or statistics background is required, familiarity with Python, Jupyter notebooks, and basic library functions is recommended. An optional Python refresher is available, and we strongly advise taking the Python for Data Science course before starting this course.

Course Coverage

• Data Gathering Techniques
• Descriptive Statistics
• Data Visualization
• Probability Distributions
• Hypothesis Testing
• ANOVA and Regression Analysis
• Hands-on projects and real-life scenarios
Who This Course Is For
1. Aspiring Data Scientists and Data Analysts
2. Business Analysts and Statisticians
3. Researchers and academic professionals
4. Anyone interested in statistics and data-driven decision-making

Application of Skills in the Real Worl

On completion of this course, learners will be equipped to apply statistical principles to analyze real-world data. This can lead to enhanced data-driven decision-making in various sectors like business, healthcare, finance, and technology. Competencies gained from this course are crucial in roles involving big data, analytics, and research where making informed decisions based on data analysis is vital.