1. Objective of This Tutorial

This Data Science tutorial from Debugshala introduces you to the world of data science using Python. We’ll cover essential concepts including:

What is Data Science

A brief history of Data Science

Methodologies in Data Science

Applications of Data Science

Business Intelligence vs Data Science

Life Cycle of a Data Science Project

Python Libraries used in Data Science

Let’s dive into it!

2. What is Data Science?

Before we start, let’s define what data science really means.

Data Science is the process of extracting hidden patterns and insights from raw data using various algorithms, machine learning techniques, and scientific methods. It works with both structured and unstructured data, much like data mining. It combines data analysis, statistics, and machine learning to uncover valuable insights.

3. A Brief History of Data Science

While the term “Data Science” has gained popularity in recent years, the practices have been around for decades. Here's a quick timeline:

In the 90s:

1960 – Peter Naur uses "Data Science" as a substitute for computer science.

1974 – Naur includes it in a survey on data processing.

1996 – IFCS includes the term in their conference title.

1997 – Professor C.F. Jeff Wu gives a lecture titled “Statistics = Data Science?”

In the 2000s:

2001 – William S. Cleveland positions Data Science as a separate discipline.

2002–2003 – Data Science journals are published by ICSU and Columbia University.

2005 – National Science Board defines “data scientists.”

2007 – Jim Gray sees data-driven science as a new scientific paradigm.

2012 – DJ Patil and Jeff Hammerbacher popularize the term.

2013–2015 – IEEE and Springer launch dedicated conferences and journals.

4. Methodologies in Data Science

a. Machine Learning for Pattern Discovery

Clustering, an unsupervised learning method, helps in identifying hidden patterns without predefined labels. For example, telecom companies use clustering to determine optimal tower locations.

b. Machine Learning for Predictions

Supervised learning is used when training data is available. Models trained this way can predict future trends based on historical data.

c. Predictive Causal Analytics

This method predicts the likelihood of events based on cause-effect relationships, such as predicting loan repayment based on a customer's financial history.

d. Prescriptive Analytics

Prescriptive analytics suggests actions based on predictive models, adapting based on real-time data. Example: Google's self-driving cars making decisions like when to stop, turn, or accelerate.

5. Applications of Data Science

a. Image Recognition

Face detection on Facebook, barcode scanning on WhatsApp Web, and Google’s reverse image search all use image recognition powered by data science.

b. Speech Recognition

Voice assistants like Alexa, Siri, and Google Assistant use data science for converting speech into commands.

c. Internet Search

Search engines like Google use data science to fetch the most relevant results quickly.

d. Digital Advertisements

Data science tailors advertisements based on user behavior, improving targeting and engagement.

e. Recommender Systems

E-commerce and video platforms recommend content based on your past behavior and preferences.

f. Price Comparison Websites

Sites like PriceDekho aggregate prices using APIs and data feeds to help users find the best deals.

g. Gaming

Games adapt to a player's skill level using machine learning, offering customized difficulty.

h. Delivery Logistics

Logistics companies optimize delivery routes, timing, and methods using GPS data and analytics.

i. Fraud and Risk Detection

Banks assess risk and prevent fraud through customer behavior analysis and transaction history.

6. Business Intelligence vs Data Science

Here’s how Business Intelligence (BI) differs from Data Science:

7. Life-Cycle of a Data Science Project

a. Discovery

Identify project needs, define business problems, and form initial hypotheses.

b. Data Preparation

Extract, transform, and load data into a sandbox environment for analysis.

c. Model Planning

Use visualization and statistics to understand data relationships (EDA).

d. Model Building

Create and train machine learning models using techniques like classification or clustering.

e. Communicating Results

Document findings, assess goal completion, and report outcomes to stakeholders.

f. Operationalize

Deploy the model, generate technical documentation, and present reports.

8. Why Python for Data Science?

Python is a top choice for data science due to its:

Open-source nature

Easy-to-understand syntax

Fewer lines of code

Strong community support

Vast library ecosystem

Cross-platform portability

Excellent performance

9. Python 2.x or 3.x – Which One?

Python 3.x is the recommended version as support for Python 2 ended in 2020. Python 3 is also faster and more efficient. While Python 2 had a large community, the future clearly belongs to Python 3.

10. Python Libraries for Data Science

a. Pandas

Used for data cleaning and manipulation, especially for structured datasets.

b. SciPy

Built on NumPy, used for advanced scientific computations.

c. NumPy

Helps in handling arrays and performing linear algebra and statistical operations.

d. Matplotlib

Used to create graphs like bar charts, pie charts, and histograms.

e. Scikit-learn

A library for implementing machine learning models – classification, clustering, regression, etc.

f. Seaborn

Built on top of Matplotlib, used for advanced data visualization.

g. Scrapy

Used for web scraping and crawling to collect online data for analysis.

11. Before You Start

Brush up on the following Python basics before diving deeper:

Variables

Operators

Dictionaries

Strings

Lists

Tuples

12. Conclusion

In this Debugshala tutorial, you learned:

What Data Science is

Its historical background

Core methodologies and applications

Differences between BI and Data Science

The lifecycle of a Data Science project

Python’s role and the libraries you’ll need

This is just the beginning of your journey with Python in the world of Data Science. Keep learning and practicing!