Probability & Statistics
- Created By raju2006
- Last Updated November 22nd, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Probability and statistics are the backbone for Data Science. The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in this type of analysis, and that’s where probability and statistics come in: they provide the mathematical foundation for such reasoning! In this Probability & Statistics course, deepen your knowledge of probability and statistics with lessons in both the theory and application of Bayesian probability, statistical testing, and model comparison. The Probability & Statistics course will then advance to understanding of DEA ( Data exploration and Analysis) which is integral stage for any Data Science. Finally learning about industry use cases relevant to data analysis with partial hands-on labs.
Course Code/Duration:
BDT250 / 1 – Day
Learning Objectives:
- Understand Statistics and Probability
- Understand statistics fundamentals and central tendency
- Use Python Libraries such as Numpy and Pandas
- Learn about inferential Statistics Fundamentals
- Understand Bayesian approach in Probability
- For anyonea interested in Data exploration and Analysis among few titles like Data Analyst, Data Scientist, Data Engineer, Business Intelligence and Analytics Engineer, DevOps, Data Wrangler etc.
- Non-technical professionals who want to start a career in Machine Learning.
- For anyone interested in Data exploration and Analysis. Non-technical professionals who want to start a career in Machine Learning.
- For anyone interested in Data exploration and Analysis. Non-technical professionals who want to start a career in Machine Learning.
Course Outline:
Statistical Analysis for Data Science
- Course Introduction
- Overview of Statistical Analysis
- Installing Anaconda
- Overview of Data Science
- The Data Science Process
- Descriptive Statistics Fundamentals
- Central Tendency
- Mean
- Median
- Mode
- Spread of the Data
- Variance
- Standard Deviation
- Range
- Relative Standing
- Percentile
- Quartile
- Interquartile range
- Data Libraries
- Numpy
- Pandas
Probability and Bayesian Approach
- Describe the differences between frequentist and Bayesian approaches to probability
- Inferential Statistics Fundamentals
- Normal Distribution
- Central Limit Theorem
- Standard Error
- Confidence Intervals
- Samples
- Hypothesis Testing
- Significance Testing
- P-value
- Z-score
- T-test
- Statistical Tests on Data
- Run advanced statistical tests and build the associated confidence intervals
- Design experiments and analyze resulting A/B test data
Conclusion
- Industry Use Case Studies
- Next steps
