Discrete Probability Distributions

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MODULE 1: INTRODUCTION TO DATA SCIENCE
In this module, students will learn the fundamentals of Data Science, which combines statistics, mathematics, and computer science to extract insights from data.
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Overview of Data Science
Data Science Lifecycle
TEST ON INTRODUCTION TO DATA SCIENCE
10:00
MODULE 2: PRACTICING THE BASICS
This module will give students foundational knowledge and hands-on experience in programming for data science using Python.
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Python for Data Science
Introduction to SQL
Introducing the SELECT Statement for Retrieving Data
TEST ON PYTHON AND INTRODUCTION TO SQL
10:00
MODULE 3: STATISTICS AND PROBABILITY
This module on Statistics and Probability provides a comprehensive introduction to essential concepts and techniques.
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Introduction to Statistics
Data Collection and Sampling Techniques
Descriptive Statistics
Probability Basics
Conditional Probability
Discrete Probability Distributions
Continuous Probability Distributions
Hypothesis Testing
Correlation and Regression
TEST ON STATISTICS AND PROBABILITY
10:00
MODULE 4: MATHEMATICAL FOUNDATIONS
The Mathematical Foundations module provides a solid understanding of linear algebra and calculus concepts that are essential for data science and machine learning.
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Linear Algebra
Calculus
TEST ON MATHEMATICAL FOUNDATIONS
19:00
MODULE 5: EXPLORATORY DATA ANALYSIS (EDA)
This module on Exploratory Data Analysis (EDA) emphasizes the importance of data cleaning and visualization in the data analysis process.
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Data Cleaning
Data Visualization
QUIZ ON EXPLORATORY DATA ANALYSIS (EDA)
10:00
Introduction to Data Science and Analytics
About Lesson

Discrete Probability Distributions

Overview: This section covers discrete random variables and their probability distributions.

  • Discrete Random Variables:
    • Variables that can take on a finite number of values (e.g., the number of students in a class).
  • Common Discrete Distributions:
    • Binomial Distribution: Models the number of successes in a fixed number of independent Bernoulli trials.
    • Poisson Distribution: Models the number of events occurring in a fixed interval of time or space.
  • Real-Life Example:
    • The number of customers arriving at a store in an hour can be modeled using a Poisson distribution.
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