Data Science Fundamentals and Key Python Libraries

Posted on Feb 27, 2026 in Other university degrees and diplomas

Data Science Fundamentals

Data science is the field that uses data to find insights, patterns, and solutions that help people and organizations make better decisions. In simple terms, data science turns raw data into useful information.

What Data Scientists Do

They work with data to:

  • Collect and clean data (remove errors, missing values)
  • Analyze data to find patterns and trends
  • Build models that can predict outcomes
  • Communicate results using charts, reports, or dashboards

Key Parts of Data Science

  1. Statistics & Math: To understand data and make predictions.
  2. Programming: Mainly Python or R.
  3. Data Analysis: Exploring and summarizing data.
  4. Machine Learning: Teaching computers to learn from data.
  5. Data Visualization: Presenting insights clearly.

Essential Python Libraries for Data Science

Here are two commonly used libraries in Data Science, explained with examples:

1. NumPy (Numerical Python)

NumPy is a Python library used for fast mathematical and numerical operations, especially when working with large amounts of data.

Why NumPy is Used

  • Works with arrays (lists of numbers).
  • Faster than normal Python lists.
  • Supports mathematical, statistical, and linear algebra operations.

NumPy Example: Calculating Average Marks

Problem: Find the average marks of students.

Without NumPy (normal Python):

With NumPy:

Explanation:

  • np.array() creates a NumPy array.
  • np.mean() calculates the average.
  • Code is shorter, cleaner, and faster.

NumPy makes numerical calculations in Python fast, easy, and efficient.

If you want, I can explain:

  • NumPy vs Python list
  • 2D arrays (matrices)
  • NumPy for data science beginners

2. Pandas

Pandas is a library used for data manipulation and analysis.

Key Features of Pandas

  • Works with structured data (tables).
  • Provides DataFrames (rows and columns).
  • Easy handling of missing data.

Data Science Applications Across Domains

Data science is applied in many fields (domains) to solve real-world problems using data. Below are the major domains of data science, explained clearly with examples.

Healthcare 🏥

Purpose: Improve patient care and medical decisions

Applications:
  • Disease prediction (diabetes, cancer).
  • Medical image analysis (X-rays, MRI).
  • Patient monitoring systems.

Example: Predicting heart disease from patient data.

Finance & Banking 💰

Purpose: Manage risk and improve financial security

Applications:
  • Fraud detection.
  • Credit scoring.
  • Stock market prediction.
  • Risk analysis.

Example: Detecting fraudulent credit card transactions.

E-commerce & Retail 🛒

Purpose: Increase sales and customer satisfaction

Applications:
  • Product recommendation systems.
  • Customer behavior analysis.
  • Demand forecasting.
  • Price optimization.

Example: Amazon recommending products based on browsing history.

Marketing & Advertising 📢

Purpose: Target the right customers

Applications:
  • Customer segmentation.
  • Personalized ads.