Summary Foundations of Data Science - CS3352 (CS3352)

Foundations of Data Science
TOPICS :-
1) Introduction to Data Science: Exploring Real-World Problems
2) Mathematical Foundations: Statistics, Probability, and Calculus
3) Data Manipulation and Visualization: Pandas and Matplotlib
4) Machine Learning Algorithms
5) SQL and Database Management: Data Storage and Handling
6) Projects and Portfolio Building: Kaggle and Github
7) Advanced Topics: Specialization in Big Data, Algorithms, or Business




1)Introduction to Data Science: Exploring Real-World Problems (Focusing on Data
Manipulation and Visualization)

Data Manipulation and Visualization

Pandas

 Introduction to Pandas: A powerful data manipulation library for Python.

 Key features:

 DataFrames: 2-dimensional labeled data structure with columns of potentially
different types.

 Series: One-dimensional labeled array capable of holding any data type.

 Basic Operations:

 Reading and writing data.

 Filtering and slicing data.

 Merging, joining, and concatenating data.

 Grouping and summarizing data.

Matplotlib

 Introduction to Matplotlib: A plotting library for Python.

 Key features:

 Static, animated, and interactive plots.

 Various plot types: line, scatter, bar, histogram, etc.

,  Basic Operations:

 Creating basic plots.

 Customizing plots (labels, titles, legends, etc.).

 Subplots and multi-figure layouts.

Advanced Topics

 Specialization in Big Data:

 Handling and processing massive datasets.

 Distributed computing: Apache Hadoop, Spark, etc.

 Algorithms:

 Machine learning algorithms for regression, classification, clustering, etc.

 Optimization techniques for large-scale data.

 Business:

 Quantitative analysis: statistical methods, risk management, etc.

 Decision-making: data-driven insights, resourcing, etc.

SQL and Database Management

Data Storage and Handling

 Introduction to SQL (Structured Query Language): A language for interacting with
relational databases.

 Key features:

 Defining data structures.

 Inserting, querying, updating, and deleting data.

 Basic Operations:

 Creating and managing databases and tables.

 Writing, reading, and modifying data using SELECT, INSERT, UPDATE, DELETE
statements.

Projects and Portfolio Building

 Kaggle: A platform for predictive modelling and analytics competitions.

 GitHub: A platform for hosting and sharing code, documentation, and projects.

Introduction to Data Science: Exploring Real-World Problems (Cont.)