Master Python for Data Science: A Comprehensive Guide for Beginners

Python is also the mainstay in another field: Data Science and AI. It is used to build machine learning models for prediction, classification, segmentation, and complex deep learning models for tasks like forecasting website traffic, speech-to-text, audio classification, etc.

69% of machine-learning developers and data scientists are extensively using Python.

If you’re wondering why you should learn Python, its benefits, difficulty, or how long it takes, you’re in the right place! This article will address whether Python is worth learning, how to learn it quickly, and the proficiency levels needed. Let’s begin learning!

What is Python Programming?

Python is a versatile, high-level programming language used for general-purpose software engineering. As a server-side language, it handles processing logic for user inputs, interacts with databases, and communicates with other servers.

Python was developed in the late 1980s by Guido Van Rossum. It has coexisted for decades alongside other server-side languages like Java, and C. Van Rossum designed Python to be intuitive and easy to read and write, drawing inspiration from the English language and minimizing complex syntax.

Python, an open-source language, has seen a surge in popularity, particularly in data science. The language boasts a vibrant community focused on machine learning, data modeling, data analysis, and artificial intelligence (AI), with a wealth of resources and libraries to support these fields.

Before going forward, we have a learning opportunity for you to help you excel in your Python Programming skills:

Top 7 Reasons to Learn Python

Learning Python offers numerous benefits and opportunities, from its versatile applications to its beginner-friendly nature. If you’re considering exploring programming or enhancing your skill set, Python is a powerful and valuable language to learn. Here are some compelling reasons why Python should be on your radar:

#1 High Demand & Rapid Growth

Python is one of the most popular and fastest-growing programming languages. It is extensively used in domains such as automation data science, AI, machine learning, deep learning, and web development, and across industries and sectors.

Some stalwarts that use Python are NASA, Google, Facebook, Netflix, Amazon, Dropbox, Spotify, Slack, Capital One, and Intel. Python developers are in high demand, and the demand for skilled Python resources will continue to grow. 

Over 75% of hiring managers believe that Python programming language is crucial for data professionals, regardless of their experience level, according to the latest Data Science Skills Survey 2024 Report by AnalytixLabs. Hence, you can be sure there’ll be plenty of career opportunities for Python programming skills.

#2 Beginner-Friendly and Easy Comprehension

Python is a very user-friendly language, i.e., it is straightforward to read and learn. It follows the rule of thumb: “Write less, do more”. It has very concise syntax in plain English. You can understand this by an example below to print “Hi! Welcome to the Python course!”:

In Java:

class WelcomeToPythonCourse {

    public static void main( String args[] ) {

        System.out.println( “Hi! Welcome to the Python course!” );

    }

In Python: 

print “Hi! Welcome to the Python course!”

Such handy and simple lines of code improve the user’s learning curve and reduce Python learning time.

#3 Python is Versatile

Python is a very versatile and multifaceted language. It can perform diverse tasks ranging from small to complex and across different industries. It has use cases in data mining, data analysis, data science, AI, machine learning, web development, software development, gaming, graphic design applications, testing, automation, and scripting.

#4 Higher Salaries 

Python offers very lucrative and high-paying jobs. As per Glassdoor.com, the average entry-level salary of a Python developer in India is₹4L – ₹8L/yr. There are immense job opportunities for people skilled in Python programming, which offers high growth and well-paying projects.

#5 Extensive Open-Source Libraries

Python provides a wide range of packages, libraries, and modules. A Python library has pre-written codes: functions and classes that can be reused without the need to write the code again.

These libraries, modules, and packages are open-source and can be easily installed using a shell command. These frameworks have wide applications, from data manipulation, visualization, statistical computations, machine learning, deep learning, neural networks, web processing, and software development.

Over 137,000 Python libraries exist in the Python Package Index (PyPI). You need not write every code from scratch in Python as you can leverage its libraries and modules. For a beginner, Python’s inbuilt libraries are a blessing. Some of the most popular and widely used Python libraries are listed below:

• Pandas and Numpy: They are used for importing, munging, manipulating, and analyzing relational and labeled data.
• SciPy: It is applied to scientific and technical computing. It consists of statistics, optimization, linear algebra, integration, signal, and image processing modules.
• Matplotlib, Seaborn, & Plotly: They are applicable for data visualization and plotting of the graphs.
• Scrapy, BeautifulSoup: The Scrapy library also allows you to do web crawling, web scraping, and data scraping.  
• SciKit-Learn or SkLearn: Sklearn is an end-to-end library for machine learning from model building to model evaluation.
• Keras, TensorFlow, PyTorch: These libraries are one-stop solutions for deep learning projects.

Also read: Pytorch vs. TensorFlow: Which Framework to Choose?

#6 Supportive and Helpful Community

When adopting a new habit, whether exercising or learning a programming language, having support or a partner is essential. Python is no exception, boasting a vast, supportive community that caters to all expertise levels, from beginners to experts.

With nearly three million GitHub repositories, this robust community provides ample resources, documentation, and forums for discussing issues, sharing ideas, and developing software and web projects. Additionally, local and global meetups enable coders and developers to connect and collaborate.

#7 Portability and Extensibility 

Python offers portability, allowing code written in Python to be compatible with various platforms, including Windows, Mac OS, and Linux, without needing any modifications.

Additionally, Python’s extensibility feature enables developers to perform cross-language tasks, integrating Python code with Java, .NET components, or C/C++ libraries. Python’s portability is made possible by its interpreted nature, converting source code into machine code during runtime.

With these features and versatile applications, Python is increasingly popular and a highly sought-after programming language to learn.

Use Cases of Python in Various Fields

Python is extensively used in many fields and is the backbone of various applications. A skilled Python professional can pursue immense career opportunities in the lucrative fields of data science, medicine, finance, or trading.

Let’s look at some of the common applications in detail below:

• Artificial Intelligence and Data Science 

Python’s popularity in data science and AI stems from its versatility, powerful open-source libraries, and simple, user-friendly syntax. It offers a comprehensive toolkit for data science, allowing you to import, clean, and manipulate data, create visualizations, build and fine-tune models, evaluate, and deploy them. Additionally, Python facilitates working with large, scalable datasets and big data solutions, such as PySpark.

• Automation and Scripting

A script is a sequence of codes designed to perform tasks autonomously. In addition to building models and complex software, Python is invaluable for writing automation scripts, which streamline manual, repetitive workflows without human intervention.

These scripts can handle data processing, such as counting records, verifying headers, detecting and eliminating null and duplicate values, trimming trailing spaces, performing KPI computations, converting data types, and cleaning up folders and files, among other tasks.

• Web & Application Development 

Python offers several frameworks for application development, including Flask, Django, and CherryPy, as well as built-in testing and debugging tools like PyUnit. PyUnit facilitates setup sharing, code test shutdown, test automation, test aggregation, test collections, and test reporting.

Web development frameworks enable the creation of both back-end and client-side functionalities, streamlining application creation by managing content, accessing databases, tracking errors, mapping function calls, and ensuring data authorization. Additionally, Python supports tools such as Pytest and Splinter for further functionality.

• CyberSecurity

Python is also applicable to cybersecurity issues. Its libraries, such as Yara, Cryptography, Nmap, Scapy, and Requests, enhance its ability to reciprocate security threats quickly.

Using Python, you can perform penetration tests on web applications to detect system breaches. It is employed for making payloads, malware analysis, scanning networks and ports, decoding packets, and accessing servers.

Also read: What is Cybersecurity? Importance, Types, Challenges, and Course

• Database Access and Seamless Integration

Python integrates with various databases, including MySQL, Oracle, Server, Durus, ZODB, and SQLite. It enables real-time viewing and editing of database content. Additionally, Python can be integrated with other programming languages like Java, C, and C++.

Getting Started with Learning Python

Although Python has simpler syntax and uses straightforward, easy-to-understand English, the programmer’s approach and mindset significantly impact the time needed to learn it; hence, there are some key points discussed below to consider:

1. Determine Your Purpose

Identify why you want to learn Python—whether for data science, web development, game development, etc.- and then choose the appropriate Python framework based on your goal.

2. Time to Learn Basics

It typically takes around 6 to 8 weeks to become comfortable with the basics of Python. You will learn to write and understand Python code and debug errors during this time.

3. Mastering Advanced Topics

Achieving proficiency in advanced Python topics and preparing for jobs may require additional time. This depends on your specific career goals, the complexity of projects you aim to undertake, and the depth of understanding you wish to achieve.

4. Learning Methods

Self-study requires consistent dedication and commitment. You can enroll in a structured training or boot camp for a guided learning environment and peer support.

5. Previous Programming Experience

Prior programming experience can expedite the learning process. A logical programming mindset helps you grasp Python faster than those without a programming background.

• Is Python hard to learn?

Python is one of the easiest programming languages to learn, featuring concise syntax written in simple English. For example, consider the following code:

movies_group = ["Avatar", "Batman", "Casino Royale"]

if "Dark Knight" not in movies_group:

print("We will not go for a movie tonight!")

The code clearly demonstrates its purpose. While Python is not inherently difficult to learn, mastering it does require a deep understanding and consistent practice of its nuances.

• Step-by-Step Approach: Following a detailed, step-by-step method can simplify the learning journey.
• Requirements and Ambitions: Your learning curve will depend on what you want to achieve with Python. If aiming to become a Python developer, daily practice is essential.

Additionally, Python is very accessible. It’s an open-source technology with numerous tutorials available for free. You can also find resources on its official website. Being open-source, Python allows for continuous updates and community contributions.

After understanding the approach to begin your learning, here’s our next section to guide you in setting up a framework for your Python learning journey.

Beginner’s Guide to Learn Python

Learning Python independently is possible, but it can be time-consuming without consistent dedication to a daily schedule. Following these steps can help streamline the process:

• Pick an Interest Area 

As you have seen, Python is highly versatile. Hence, you must know which area you want to specialize in. Knowing the niche areas will allow you to focus the resources, time, and energy in a particular direction.

The common applications in Python are data science, data extraction, artificial intelligence,  machine learning, deep learning, web development, game development, app development, robotics, and automation scripts. 

• Master the Basics

Mastering the fundamentals of Python is essential for reaching your end goal, whether building an application or model. It won’t take long to learn these basics. The fundamentals of Python include:

1. Variables 
2. Data Types
3. Flow Controls
4. Conditions
5. Loops: For, While
6. Functions
7. Operators
8. Data Types 
9. Objects and Classes 
10. Object and Data Structures
11. Lists, Tuples, Dictionaries, and Sets
12. Arrays
13. Lambda
14. Iterators, Decorators
15. Inheritance

Once you grasp these above concepts, you can advance to be proficient in advanced topics. This will also help you understand each line of the Python code. 

• Consistent Practice

If you want to become a software developer or data scientist, Python must become your best friend—so familiar that you can code while half asleep! Merely watching lectures or tutorials isn’t enough. Consistent daily practice, at least 30 minutes, is essential to build muscle memory and confidence. By practicing regularly, you’ll become proficient in Python and job-ready.

• Join Python Communities

Coding can be challenging, and guidance, assistance, and support are often essential. Python’s community offers free and worldwide support, so there’s no need to worry—help is always available. You can turn to Google and Python communities for answers if you’re not in a course or working with a team.

Joining Python developer forums on platforms like Stack Overflow, GitHub, and Reddit provides access to a diverse community of experts and beginners. These people can answer a wide range of questions and offer valuable assistance. Engaging with these communities not only allows you to learn but also allows you to help others, creating a rich and supportive learning environment.

• Take a Coding Bootcamp

Bootcamps answer the question, “Can I learn Python in a week?” They are intensive training programs that cover a range of programming languages and skills and are among the fastest and most effective ways to learn to code. Bootcamps delve into concepts from the fundamentals to data exploration, providing hands-on practice in Python coding.

• Enrolling in Degree Courses or Training 

While self-learning is possible, having a structured program can make learning Python more enjoyable and accessible. Traditional degrees and formal courses indicate a candidate’s commitment and offer a more focused environment to learn topics and specialize in areas of interest.

A computer science or advanced data science degree is ideal for those interested in mastering advanced Python programming and coding.

• Learn by Teaching

Among the fastest ways to learn Python is by teaching someone else. You can start by:

1. Taking Notes: Create notes or flashcards with key points. Writing a blog post explaining a concept is also effective. Teaching or explaining a topic enhances understanding and helps it stick with you.
2. Breaking Down Topics: First, understand the aim of the question or requirement and then break it down into granular levels.
3. Writing Blogs: This provides a new perspective on learning and helps you tackle challenges you encounter as a developer.

• Explore Resources: Books, Tutorials, & Videos

Other ways to support your Python learning include investing time in reading books, exploring tutorials, and watching videos. Some tutorials even include walkthroughs of working on projects, helping you apply your knowledge in practical scenarios.

Books and blog posts can be incredibly helpful, as they often come with diagrams and explanations. Some popular books on Python include:

1. Introducing Python by O’Reilly
2. Python Crash Course, 2nd Edition, by Eric Matthes
3. Python Basics: A Practical Introduction to Python 3 by Dan Bader

• Python Projects and Repositories 

The most impactful factor in your Python learning journey is actively working on projects and sharing your work with others. While learning a language and interpreting its syntax is essential, it’s not enough.

For a data scientist or software developer, offering solutions to business users is paramount. Problem-solving skills are honed through hands-on project work, where you encounter and overcome obstacles. Here are some project ideas to get started:

• Calculators
  • Simple interest or EMI calculator
  • BMI calculator 
  • A GPA calculator given a sheet of grades
• Weather Application
• Games
  • Sudoku solver
  • Tic Tac Toe
  • Hangman
  • Guessing Number 
  • Quiz Game
• Websites
  • Fully functional E-commerce site
  • Web-based crawler
• Text Generator
  • CV or resume generator: generating a CV or resume in from raw text format to PDF format
  • Text-to-speech model
  • Chatbots

Sharing your work is just as crucial as creating it. Contributing your codes and projects to open-source repositories like GitHub allows you to practice, learn, refer to others’ work, and continuously improve.

Additionally, showcasing your portfolio on public open-source platforms reflects your interest and passion in the field to potential employers, demonstrating the extra effort you’ve put into achieving milestones.

Learning Stages of Python 

Python learning stages can be categorized based on difficulty levels, such as basic, intermediate, advanced, expert, or functional. Going forward, we’ll explore the Python learning levels based on functionality:

• Stage 1: Basics of Python

The first step in learning Python is to install it on a Jupyter Notebook system and get comfortable with its environment. Jupyter Notebook is a popular Integrated Development Environment (IDE), but alternatives like Spyder, PyCharm, and Visual Studio Code exist.

Jupyter Notebook is especially favored by data scientists. It lets you write code alongside text, images, HTML, and LaTeX in a single document. You can create visualizations, equations, and more and see the output immediately after entering the code. Additionally, the Jupyter Notebook is easy to debug.

Google has also adopted Jupyter Notebook, offering Google Colaboratory as an alternative.

Now, the list of topics and basic concepts that a beginner must know are:

• Variables
• Data types
• Data Structures: lists, dictionaries, tuples, sets
• Indexing, Slicing
• File input-output operations (read, write to text files)
• Conditional statements
• Control flows (if-else-elif statements)
• Loops (For, While, and nested)
• Functions
• Methods
• Modules & Imports
• *args and **kwargs
• Iterables
• Python shell, basic arithmetic
• Exception handling
• Dictionary and lists comprehensions
• zip(), enumerate() functions
• Anonymous function: Lambda
• Regular Expressions 
• Object-Oriented Programming
• Inheritance
• Generators, Decorators

• Stage 2: Python for Data Analysis

The next phase in Python learning levels is Exploratory Data Analysis (EDA), which delves into the core of data science, focusing on data cleaning, munging, and visualization. To accomplish these tasks effectively, a solid understanding of the following libraries for data analysis and visualization is essential:

• Pandas
• NumPy
• Matplotlib, and 
• Seaborn

Here, you will learn to do the following tasks: 

• Import and clean data 
• Conduct exploratory data analysis 
• Use visualization techniques and 
• Understand the best feature engineering practices

Also read: Why Use Python For Data Analysis?

• Stage 3: Python for Statistics & Math

After mastering the basics of Python, data cleaning, EDA, and visualization, it’s essential to explore the fundamental statistics of data science, especially using Python.

Statistics is the foundation for various tasks behind a model, including training and evaluation. You must be comfortable with:

1. Inferential Statistics: Understanding sampling methods, confidence intervals, and hypothesis testing.
2. Hypothesis Formulation and Testing: Formulating testable hypotheses and using statistical tests to validate or refute them.
3. Handling Imbalanced Data: Techniques for skewed class distributions in machine learning models.
4. Train-Test Split: Properly segmenting data into training and testing sets for model evaluation.

Invest time learning the mathematics and statistics behind these algorithms to understand how machine and deep learning algorithms work. You’ll also need to code them using Python libraries like Scipy and statsmodels.

Also read: 

• Fundamentals of Statistics for Data Science
• Descriptive vs. Inferential Statistics

• Stage 4: Python for Machine Learning

The last phase in Python learning levels is building machine and deep learning models. There are various Python libraries based on your needs and area of interest. Key libraries include:

1. NLTK and spaCy: For text data and natural language processing (NLP). Learning these libraries helps you tackle text data challenges like creating chatbots and building recommendation systems.
2. TensorFlow and PyTorch: These deep learning libraries are computational frameworks for expressing complex algorithms.
3. Scikit-Learn or sklearn: This machine-learning library offers a range of tools, including supervised and unsupervised learning algorithms, regression, dimensionality reduction, clustering, ensemble methods, parameter tuning, and feature extraction and selection.

Also read:

• What are the best Python Libraries for Machine Learning to learn in 2024?
• Supervised vs. Unsupervised Learning in Machine Learning

How is Python Used for Data Science?

Data Science is an interdisciplinary domain that involves extracting useful insights from large amounts of structured and unstructured data. It uses various statistical tools, scientific approaches, machine and deep algorithms, and big data.

Python is immensely useful in every step of a data science project’s life cycle, from ingesting the data to building web applications. Following are the ways how Python is used in data science:

1) Integrate with SQL

Python can be connected with SQL to pull data from the respective database by writing a query.

Also read: Guide to master SQL for Data Science

2) Data Mining and Wrangling

Python Programming is incredibly useful for all data exploration processes. The Pandas library is essential for analyzing structured data, while NumPy supports scientific computing and data analysis. SciPy is invaluable for conducting statistical analysis and hypothesis testing.

For preprocessing and cleaning text data, Python offers tools like RegEx, NLTK, and spaCy for tokenization and text manipulation. Additionally, OpenCV and PIL are available for image preprocessing.

Also read: What is Data Mining: Types, Tools, Functionalities, and More

3) Data Visualization

Python offers various visualization libraries. These are extensively used to illustrate how the data looks for all univariate, bivariate, and multivariate analyses. Another handy library for EDA and visualization is Pandas Profiling, which allows you to generate reports in fewer codes instantaneously.

Also read: How To Visualize Data Using Python

4) Model Building

Keras is preloaded with built-in functions for machine learning, sci-kit-learn, and deep learning, including data preprocessing and transformation. Import, and your model is ready! However, you would need to tweak and tune the parameters for better accuracy while minimizing error.

5) Build Applications using Flask

The Flask framework in Python allows you to implement machine learning or deep learning models. Flask is a Python API that allows you to construct web applications. It is used the same way as importing other Python modules and packages. This web framework is easy to use and offers tools, libraries, and technologies.

6) Frameworks for Machine and Deep Learning 

Python has in-built specific modules, packages, and libraries for machine and deep learning algorithms. These packages come with pre-defined functions and codes; after importing them, your model is one step away from being executed!

7) Interactive and Shareable Format

Python Jupyter notebooks are very interactive. You can write notes and explain your codes; they are also very presentable. The notebooks can be saved and downloaded in HTML and readily shareable PDF formats. It is easy to share the model results and final analysis in respective formats with others.

Conclusion

Python continues to evolve, with its origins dating back to February 1991, when it was created by Guido van Rossum. Over the years, it has undergone numerous updates and improvements, making it a dynamic and versatile programming language. Learning and mastering any programming language is an ongoing process requiring consistent daily practice to progress.

When starting with Python, it’s not just about the time it takes to learn it; it’s about the dedication and effort you invest in pursuing your goals. You will deepen your understanding and expand your skills by actively engaging with Python and exploring its wide range of applications, from web development to data science. These factors, combined with perseverance, will significantly impact the time it takes to master Python.

FAQs

• Which Python version is best for data science?

Python 2.7 ended the series of Python 2.x and has been succeeded by Python 3. Python 3.12.1 is the latest version (December 6, 2023). It has been updated and is the most relevant to present data science work. It must be learned to excel in the data science world. The purpose of Python 3 was to fix the issues or bugs present in Python 2.

• Do I need to be good at math to learn Python?

No, you don’t need to be good at Mathematics to learn Python. It’s a common myth. However, you need to have good logical reasoning to understand and build logic to do programming. A basic understanding of arithmetic would be good. 

• Can I learn Python without any programming experience?

Yes, you can learn Python without any prior programming experience. Python is very intuitive and easy to use. It is less syntactical, as the syntax is in plain, simple English, making it more readable. Each line of the code can be easily understood. 

• Can I learn data science without Python?

Yes, you can learn data science without Python. Other programming languages like R and Julia and tools like SQL, Excel, and SAS are also widely used in data science. However, Python is highly recommended due to its versatility, extensive libraries, and strong community support.