Data Collection and Sampling Techniques

Course Content
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.
0/4
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.
0/3
Data Cleaning
Data Visualization
QUIZ ON EXPLORATORY DATA ANALYSIS (EDA)
10:00
Introduction to Data Science and Analytics
About Lesson

Data Collection and Sampling Techniques

Overview: This section covers methods for collecting data and various sampling techniques.

  • Data Collection:
    • The process of gathering information to analyze and draw conclusions. Data can be collected through surveys, experiments, or observational studies.
  • Sampling Techniques:
    • Random Sampling: Every member of the population has an equal chance of being selected.
    • Stratified Sampling: The population is divided into subgroups (strata), and samples are drawn from each stratum.
    • Convenience Sampling: Samples are taken from a group that is easily accessible.
  • Real-Life Example:
    • A political poll may use random sampling to ensure that every voter has an equal chance of being included, leading to more accurate results.
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