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1- Big Data Analytics: Summary: Leveraging advanced technologies and techniques to process and analyze large volumes of complex data efficiently. Definition: Big Data Analytics involves

1- Descriptive Statistics: Summary:Summarizing and interpreting data using measures of central tendency, variability, and distribution. Definition: Descriptive statistics involve summarizing and describing the main features

1- Supervised Learning: Summary: Training models using labeled data to make predictions or decisions without explicit programming. Definition: Supervised learning involves training a machine learning

1- Python: Summary: A versatile programming language with libraries like Pandas and NumPy for data manipulation and analysis. Definition: Python is a high-level, versatile programming

1- Bar Charts: Summary: Representing data with rectangular bars for comparing discrete categories and trends. Definition: Bar charts use rectangular bars of varying lengths to

1- Normalization: Summary: Scaling data to a standard range (typically 0-1) to ensure fair comparisons between variables. Definition: Normalization is the process of scaling numeric data

1- Errors Identification Summary: Detecting and correcting inaccuracies, missing values, and outliers to maintain data integrity and reliability. Inaccuracies Detection and Correction: Detection Methods: Visual

Sources of Data Databases: Definition: Structured collections of data. Examples: MySQL, PostgreSQL, MongoDB. Advantages: Easily queried and analyzed. Surveys: Definition: Primary data collection from individuals

Lecture 1: Introduction to Data Science   What is Data Science? Data Science is a multidisciplinary field that combines techniques, processes, and systems to extract