How to learn Python for Data Science in 2024 - Comprehensive Guide

Data science is all about studying data to find important information for businesses. It uses ideas from math, stats, AI, and computer engineering to analyze big amounts of data.In today's times , data science is a popular career choice. To begin your data science journey, learn linear algebra, math, statistics, and, crucially, a programming language. Python, with its simplicity, versatility, and rich libraries, is a top programming language choice for data science. This guide introduces Python for data science, providing a stepwise approach to build a strong foundation from basic to advanced topics.

Data Science Careers in 2024: Why are they so Popular?

Thinking about a career in data science in 2024? Jobs have increased by 46% since 2019, with Python playing a big role. The average salary of a data scientist in India is ₹14 LPA according to Glassdoor. Through this blog find out how Python helps in learning data science and begin your journey to become a data scientist.

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Why choose Python for data science?

Python has been popular in data science for a while, and recent surveys confirm its top position in TIOBE and PYPL Index. Here's why:

1. Python is a free and open-source language
2. Easy & beginner friendly 
3. It is portable, so your code works on different computers without changes.
4. It offers many libraries for data tasks: Pandas for manipulation, NumPy for math, Matplotlib for visualization and scikit-learn for machine learning.
5.  Ability to cover the entire pipeline of Data Science
6. Has a huge active community support.

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A Step by Step Guide to learn Python for Data Science 

Step 1: Learn the Basics 

1. Prepare your workspace 

Before you begin your Python-based data science journey, ensure your local machine has an Integrated Development Environment (IDE) installed. Some commonly used platforms include:

• Anaconda
• Google Colab
• PyCharm

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2. Learn the Basic Concepts of Python Language

•  Basic Python syntax(Writing Basic Programs)
• Operators(Arithmetic, Relational, Logical, Membership)
•  Variables and  data types(Numeric,Boolean,Binary,String)
• Control statements and loops(If - else statement,for loop, nested loop)
• Functions (Lambda Functions,Map, Filter and Reduce,Higher Order Functions)

Resources to learn Python Basics: 

• Course : Python for Everybody by Dr. Charles Severance on edX - Free Course
• Book : Python Crash Course by Eric Matthes - Free Book
• YouTube Channel : Corey Schafer 

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Step 2: Understand OOP Concepts 

In Python, Object-Oriented Programming (OOP) organizes code using objects and classes, connecting data and functions for better control and preventing unauthorized access to data.Important OOPs concept to learn:

• Class
• Objects
• Polymorphism
• Encapsulation
• Inheritance
• Data Abstraction

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Step 3: Learn Data Structure and Algorithms(DSA) 

These are like the building blocks for programming. DSA knowledge is important  to write codes efficiently. Familiarize yourself with:

• Lists
• Tuples
• Sets & Dictionaries
• Stacks & Queues
• Sorting (eg. Merge Sort)
• Searching(eg. Linear search)

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Step4: Familiarize Yourself with Data Science Libraries

Python comes with powerful data science tools like NumPy, Pandas, and Matplotlib. These tools help you work with data and create visuals. Spend some time getting acquainted with these libraries and understanding how they work.

1. Pandas: Learn Data Analysis Skills Using Pandas

Pandas is a widely used Python library for data analysis, enabling data import, manipulation, and analysis. Practice data analysis with Pandas using real-world datasets for hands-on experience.Learn about : 

• Introduction to Pandas and its data structures
• Reading and writing data from various sources
• Data cleaning and preprocessing
• Data wrangling and transformation
• Data aggregation and group by operations

Resources to learn data analysis with pandas: 

Course: Easier data analysis in Python with pandas by Kevin Markham on Data School - Free Course

Book : Python for Data Analysis by Wes McKinney - Free Book

YouTube Channel : Data School

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2. Gain proficiency in Data Visualization using Matplotlib & Seaborn.

A wide range of visualizations are possible with this library, including line plots, scatter plots, and histograms. Utilize Matplotlib to create visualizations with data from your analysis or publicly available data. For advanced statistical  visualizations explore the seaborn library.   

Resources to learn data visualization: 

1. Course: Visualizing Data with Python By IBM on edX - Free Course

2. Book: Python Data Science Handbook by Jake VanderPlas - Free Book

3. YouTube Channel: PyData

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3. NumPy:

It is used for array operations, including functions for matrices, Fourier transforms, and linear algebra operations.

Resources to learn Numpy:

1. Course: Numpy Course with Free Online Certificate - Great Learning- Free Cou

2. Book: NumPy for Beginners: First Step to learn Data Science -Free ebook

3. Youtube: Python NumPy Tutorial for Beginners

Step 5: Statistical analysis with Python 

Extract meaningful insights from your data using statistical analysis with the help of  Numpy library. Learn 

• Introduction to probability and its concepts

• Descriptive statistics and summary metrics

• Inferential statistics and hypothesis testing

• Probability distributions and their applications

• Bayesian statistics and its applications

Resources to learn Statistical Analysis

‍Course: 1. Intro to Statistics By Udacity - Free Course

               2. Intro to Descriptive statistics By Udacity - Free Course

               3. Intro to Inferential Statistics By Udacity - Free Course

               4. Bayesian Statistics: From Concept to Data Analysis By Coursera - Free

‍Book: Think Stats by Allen B. Downey - Free Book

YouTube Channel: StatQuest with Josh Starmer

Step 6: Learn  Machine Learning with Scikit-Learn

Machine learning is simplified with Scikit-Learn in Python. It also provides tools for preprocessing data and engineering features, as well as tools for classification, regression, and clustering. Using Scikit-Learn, you can explore machine learning once you have a good understanding of Python and data analysis.

Course: Machine learning with Scikit-learn | Udemy

Youtube: Scikit-Learn Course - Machine Learning in Python Tutorial

Step 7: Learn advanced topics 

Now that you've covered various basic techniques, it's time to explore advanced concepts and Deep Learning for more complex modeling and analysis.

1. Advanced topics:

• File Handling

• Exception Handling

• Regular Expressions

• Big Data Processing with Apache Spark

• Data Engineering and Pipeline Development

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2. Deep Learning with TensorFlow or PyTorch

• Introduction to deep learning and neural networks

• Building and training simple neural networks with TensorFlow or PyTorch

• Convolutional neural networks for image classification

• Recurrent neural networks for sequence modeling

• Advanced topics in deep learning, such as transfer learning and reinforcement learning

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Learning Resources for Deep Learning: 

1. Course: Deep Learning Specialization by Andrew Ng on Coursera - Free Audit

2. Book: Deep Learning with Python by François Chollet - Free Book

3. YouTube Channel: TensorFlow

Step 8: Build your portfolio 

For those aiming to be data scientists, having a portfolio is really important because hiring managers really want to see it. Your Python data science portfolio should display unique insights gained from working with various datasets in each assignment. It should include the following types of projects:

1. A Data Visualization Project:

‍Good visualizations need programming and design skills. Successful analysis with eye-catching visuals enhances your portfolio, making your work more impactful.

Sample datasets: 2015 Flight Delays and Cancellation

Amazon Top 50 Bestselling Books 2009 - 2019

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2. A Machine Learning project:

‍Create multiple projects, each emphasizing a unique algorithm. These demonstrate your commitment to learning Python and crucial programming skills to recruiters.

Sample datasets: Credit Card Fraud Detection | Kaggle

Wine Quality Prediction | Imbalanced Data | Kaggle

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3. Data Cleaning & Exploratory Data Analysis Project:

‍These projects provide a comprehensive understanding of the data, guide subsequent analytical steps, and contribute to making informed decisions in various domains.

Sample Datasets: Detailed exploratory data analysis with python | Kaggle

GitHub - SouRitra01/Exploratory-Data-Analysis-EDA-in-Banking-Python-Project

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Learn more here on Building Projects:

Course: Applied Data Science Capstone by IBM on Coursera - Free Audit

Book: Data Science Projects with Python by Stephen Klosterman

YouTube Channel: Kaggle

Step 9: Certification 

Get professional  certifications to increase credibility and advance your  career. Here are some of the best certifications to go for: 

For Beginners: 

1. PCAP – Certified Associate in Python Programming

2. Microsoft Certified: Python Developer Associate

3. Google IT Automation with Python Professional Certificate | Coursera

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For Professionals:

1. Python for Machine Learning Certification by IBM

2. Applied Data Science with Python | Michigan Online

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In summary, learning Python for data science is a rewarding investment in your career. Follow this guide, practice with real-world datasets, and build practical skills in data analysis and machine learning.