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How to Learn Python for Data Science in 2024 [In 5 Steps]

24th Apr, 2024
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    How to Learn Python for Data Science in 2024 [In 5 Steps]

    In today’s AI-driven world, Data Science has been imprinting its tremendous impact, especially with the help of the Python programming language. Owing to its simple syntax and ease of use, Python for Data Science is the go-to option for both freshers and working professionals. Python also finds its use in academic research and building statistical models adding to its versatility. So, before moving forward with learning Python for a successful Data Scientist career, one must understand its significance first. 

    Use of Python in Data Science  

    Data Science here is indeed an umbrella term, however, let’s try and understand, how Python is super helpful and integral part of end-to-end Data Science pipeline.

    Data Science Pipeline

    This image depicts a very gh-level pipeline for DS. Largely we are interested in each stage of this pipeline and how Python is associated with it.

    Exploratory Data Analysis (EDA)

    StepsWrangleCleanExplorePre-processModel TrainingModel ValidationModel Deployment









    The above table showcases, Python has a presence in each of the stages of DS pipeline.   

    • Pandas is one of the core packages in Python which allows you to perform data wrangling, cleaning, pre-processing with ease in Python.   
    • NumPy allows you to perform large and complex numerical operations within a few lines of expression.   
    • Seaborn is a very powerful data visualization library in Python. This can be used to create any kind of graph and plot.     
    • Python provides frameworks/libraries like Scikit-learn, TensorFlow, PyTorch, Keras among others for building and validating ML or DL models in just 5-10 lines of code.   
    • Web-development framework like Django, allows developer to build API around model for deployment.   

    Apart from this, Python has in-depth support for NLP (Natural Language Processing) and CV (Computer Vision) which are advanced domain of Machine Learning.   

    Python is adopted by organizations of every size and domain, as it provides end-to-end coverage for the DS pipeline, and it has quite rich use-cases. Overall, Python helps you achieve data science essentials as a one-stop shop.   

    How to Learn Python for Data Science in 2024 [Step-by-Step Guide]

    Data scientists are in great demand right now. The most significant time to start learning Python if you're thinking about a career in data science is right now. Python is a popular, easily understood programming language with a vibrant, expanding user base. Python is a wonderful place to start for anyone wishing to change jobs and enter data science. 

    As per the current data scientist job market, A data scientist will make an average income of $119,118 in 2023, according to Glassdoor. As demand for data scientists rises, that number is only anticipated to climb. Data scientists had three times as many available opportunities in 2020 as in 2019. Both Python and data science look to have a very promising future. Fortunately, it's now simpler than ever to learn Python. 

    Here are five steps to learning Python for data science 

    Step 1: Learn Python fundamentals

    Everyone has a beginning. Learning the fundamentals of Python programming is the first step. If you're not already familiar with data science, you'll also want to get acquainted with it. 

    This may be accomplished through online courses like Data Science complete course. The fundamentals of Python may be learned in any order. Please check out the syllabus, Data Science with Python syllabus. The secret is to pick a direction and stick with it. 

    Find a community online. 

    Join an online group for support in maintaining motivation. In most communities, you may learn by asking the group questions or asking questions yourself. 

    Additionally, you may establish ties with specialists in the field and interact with other community members. Further, because 30% of all hires come from employee referrals, this boosts your chances of finding work. 

    Additionally, many students find registering for a Kaggle account and joining a neighborhood Meetup group beneficial. 

    Step 2: Practice with hands-on learning

    Hands-on coding is one of the most acceptable methods to advance your knowledge of Python. 

    Here are a few tips for practicing Python with hands-on learning: 

    1. Learn The Basic Syntax: The syntax of a programming language must be understood at least at a fundamental level to be learned. Spend a few days learning the fundamentals of Python programming. You will learn more quickly if you start working on projects as soon as possible. When you subsequently get stuck, you can always go back and review the syntax. The ideal time for this period is a few weeks, but no longer than a month. 
    2. Practice with Python projects: Start working on independent projects once you've mastered the fundamentals of Python syntax. It will be challenging to recall anything you've learned until you put what you've learned to use. Projects will challenge you, teach you new Python ideas, and assist you in developing a portfolio to demonstrate your skills to future employers. 
    3. Work independently on Python projects: If you can start working on your projects on topics you find fascinating after completing a few structured assignments, you will learn Python more quickly. But bear in mind, to begin with, a smaller project. Starting and finishing a smaller project is preferable to a much larger job and never-ending one. 

    KnowledgeHut interactive Python courses may take you from absolute beginner to employable using actual code in a matter of months. You can explore KnowledgeHut’s Data Science with Python syllabus. 

    Step 3: Learn Python data science libraries

    NumPy, Pandas, Matplotlib, and Scikit-learn are the four most important Python libraries to solve data science problems. 

    Pandas are mainly used for tabular data manipulation and analysis in Dataframes. Data may be imported into Pandas from various file types, including Microsoft Excel, JSON, Parquet, SQL database tables, and comma-separated values. 

    • NumPy: It is used to manipulate arrays. Additionally, it contains matrices, Fourier transform, and functions for working in the area of linear algebra. 
    • Matplotlib: It is a Python module used for data visualization. This library's plots, which include line charts, bar charts, histograms, and more, are constructed on top of NumPy arrays. 
    • Scikit-learn: Python's most widely used machine learning library. 

    For examining and experimenting with data, NumPy and Pandas are fantastic tools. A data visualization package called Matplotlib creates graphs similar to those in Google Sheets or Excel. 

    Step 4: Build a Data Science Portfolio as you Learn Python

    A portfolio is a must for aspirant data scientists because it's one of the critical qualities hiring managers look for in a prospect. 

    These projects should include working with various datasets, and each one should present intriguing insights you found. Consider the following project categories: 

    Projects using unclean or "unstructured" data that you clean up and analyze can impress potential employers because most real-world data has to be cleaned before use. 

    A) Project for Data Visualization  

    The ability to create appealing, simple-to-read visualizations is a programming and design challenge, but your analysis will be much more beneficial if you succeed. Your portfolio will stand out if a project includes attractive charts. 

    B) Machine Learning Project 

    If you want to become a data scientist, you must have a project demonstrating your ML proficiency. Several machine learning initiatives, each centered on a different algorithm, can be what you need. 

    C) Effectively present your portfolio 

    To make your analysis understandable to a technical audience, it should be written in a format similar to a Jupyter Notebook. (Your charts and textual explanations allow non-technical readers to follow along.) 

    D) Do you need a theme for your portfolio? 

    A specific theme is not necessary for your portfolio. Find interesting datasets, then figure out how to link them. Showcasing projects related to a particular sector is a terrific option if you want to work for a specific business or in a particular field. 

    These projects show potential employers that you've invested the time to master Python and other crucial programming abilities. 

    Step 5: Apply advanced data science techniques

    Finally, develop your abilities. Although learning new things will be continuous in your data science path, there are advanced Python courses you can take to be sure you've covered everything. 

    Gain confidence with the k-means clustering, classification, and regression models. You may also get started with machine learning by learning about bootstrapping models and building neural networks with Scikit-learn. 

    As Python is an open-source language, there are indeed free books available on the internet which you can refer to as and when needed. Following are some resources:  

    1. Automate the Boring Stuff with Python  
    2. Python for Everybody  
    3. Think Python: How to Think Like a Computer Scientist  

    You can also read daily updates and events at: -   

    1. Pythonware Daily: http://www.Pythonware.com/daily/  
    2. Planet Python: http://planet.Python.org/  

    You can also be part of the ever-growing Python community: -   

    1. Irc Node: http://www.Python.org/community/irc/  
    2. Stack Overflow: http://stackoverflow.com/questions/tagged/Python?sort=newest  

    And finally, you can also look at the official web page of Python,  

    1. Official Tutorial: http://docs.Python.org/tutorial/  
    2. Language Reference: http://docs.Python.org/reference/  
    3. Knowledgehut Python tutorial. 

    Tips to Learn Python for Data Science

    To learn Python for data science, follow these gradual stages: 

    1. Learn just the basics of Python: Learn about basic syntax and popular data science libraries application to solve data science basic problems  
    2. Learn to visualize data using Matplotlib: The fundamental Python visualization library. The most prominent charts, including line charts, bar charts, scatter plots, histograms, and box plots, may be made using Matplotlib if you know how to do it. 
    3. Learn to use SQL and Python: Both Pandas and SQL are used to alter data by data scientists because some data manipulation tasks can be completed quickly and easily using SQL. In contrast, other activities may be completed quickly and effectively using Pandas.  
    4. Learn basic Statistics with Python: You should be aware of the kind of problems that statistics can help with. Recognizing the problems that statistics can help you overcome. 

    The following are some fundamental statistical ideas you ought to understand: Sampling, frequency distributions, means, medians, modes, variability measures, fundamentals of probability, significance testing, standard deviation, z-scores, confidence intervals, and hypothesis testing (including A/B testing). 

    1. Learn the application of  Machine Learning using Scikit-Learn: Your objective is to become proficient at using Scikit-Learn to implement some of the most popular machine learning methods. 

    Python Libraries for Data Science

    Python is now widely used as a general-purpose programming language, a high-level back-end programming language for building apps, web applications, machine learning models, and prototypes. Python is one of the most popular languages among developers due to its readability, versatility, and adaptability for data science operations. Python is simple and easy to read by design, making it simple to learn. Thanks to Python's extensive library availability, data scientists can access specialized packages for free download. Python has become extremely popular among data science and analytics experts due to its extensibility. 

    Python libraries greatly ease complex operations and speed up data integration with fewer lines of code. More than 137,000 libraries in Python, all of which are robust and widely utilized to meet consumer and corporate needs. These libraries have aided researchers and programmers in analyzing vast volumes of data, producing insights, making crucial decisions, and much more. 

    The following Python libraries are some of the most popular ones in data science: 

    1. NumPy

    A comprehensive Python library called NumPy is used to do calculations in science. It uses complex functions, N-dimensional array objects, C/C++, and Fortran code integration tools. You may treat generic data as a multidimensional container. 

    2. SciPy

    Another critical Python library for programmers, academics, and data scientists is SciPy. It provides packages for computations in optimizations, statistics, linear algebra, and integration. Any beginner data scientist might benefit significantly from assistance with numerical calculations.

    3. Matplotlib

    Data scientists frequently utilize Matplotlib, a well-known Python charting package, to create a wide range of figures in different formats based on compatibility with different platforms. For instance, you may design your scatter plots, histograms, bar charts, and other visuals with Matplotlib. It offers rudimentary 3D charting with restricted use and high-quality 2D plotting. 

    4. pandas

    The most powerful open-source Python data manipulation package is called Pandas. The Python Data Analysis Library is an upgraded version of the NumPy software. When managing and saving data from tables by performing operations over rows and columns, DataFrames are regarded as the most popular data structure in Python. When merging, reshaping, aggregating, and separating, pandas are quite helpful. 

    5. Scikit-Learn

    Scikit-Learn is a group of tools for data analysis and mining-related activities. SciPy, NumPy, and Matplotlib serve as the backbone of this program. Model selection and tuning, picture recognition, data reduction techniques, classification models, regression analysis, and many other topics are included. 

    6. statsmodels

    For statistical modeling, use statsmodels. Users may examine data, estimate statistical models, and run statistical tests using the Python package statsmodels. A comprehensive array of descriptive statistics, statistical tests, charting tools, and outcome statistics are accessible for various data types and estimators. 

    7. Seaborn

    Seaborn for displaying statistical data. Python's Seaborn package allows you to create visually appealing and educational statistics visuals. Seaborn, a Matplotlib-based project, seeks to make visualization a key component of data exploration and comprehension. 

    8. Bokeh

    For building interactive charts, dashboards, and data apps on current web browsers, use Bokeh. Bokeh gives the user the ability to create D3.js inspired visuals that are beautiful and concise. Additionally, it can do high-performance interaction across extremely big or streaming information. 

    9. Blaze

    Blaze for enabling distributed and streaming datasets using Numpy and pandas. Data from many different sources, such as bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc., may be accessed using Blaze. Blaze may be a handy tool for building dashboards and visualizations on significant amounts of data when used in conjunction with Bokeh. 

    10. Scrapy

    Scrapy is used for web crawling. The Scrapy framework is excellent for locating specific data patterns. It can start at the home URL of a website and then sift through its web pages to gather data. 


    For symbolic computing, use SymPy. Basic symbolic arithmetic, calculus, algebra, discrete mathematics, and quantum physics are only a few of the diverse capabilities of SymPy. The ability of SymPy to format calculation results as LaTeX code is another helpful feature. 

    12. Requests

    Requests for web access are made. Although significantly simpler to implement, it functions similarly to the common Python module urllib2. Although there are some minor differences between Requests and urllib2, Requests could be more practical for beginners. 

    The following extra libraries could be required: 

    • OS for file operations and the operating system 
    • For graph-based data operations, use NetworkX and graph. 
    • Regular Expression for Pattern Recognition in Text Data soup for web scraping involves pulling data from just one online page at a time. 

    Finding a platform with a curriculum designed for data science education may be the best option if you want to get the most out of your learning. KnowledgeHut’s Data Science complete course will help you in this regard. 

    Why Learn Python for Data Science and Is It Necessary to Learn It 

    The journey of Data Science begins with the programming language; in other words, the programming language is a critical and most important component of DS. Now that programming language can be anything from Python, R, Scala, Java, Go, SQL, and a few others.   

    However, among all languages that you can select from, Python is the most popular language for Data Scientists. It’s not “the most popular” just for saying or by stats, but there are fundamental reasons which make Python as the most popular and first choice for the large crowd. 

    1. Ease of Use 

    Python is the easiest programming language to start your journey. Also, its simplicity does not limit your functional possibilities. We can certainly say beginners and experts can work with Python very easily to become productive quickly. Python language is free and open source, which contributes very heavily to the success of the language.  

    Ease of Use

    2. Extensive Support Libraries

    Python offers free access to hundreds and thousands of open-source third-party libraries or packages. These packages are built by the community and using these libraries results in effective results and huge savings in time and effort. Some of the most popular libraries are NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch, NLTK, etc.  

    3. Machine Learning

    Python along with libraries has demonstrated tremendous capabilities in implementing the most common and critical functionality of ML. Libraries like Scikit-Learn and TensorFlow are the backbone of it. Implementing ML and its processes have become super easy and effective with Python.  

    4. Extensible

    Python is lightweight and super portable. It allows developers to do cross-functional programming like SQL, Java, and Unix quite easily. Python can run on any OS including Windows, Unix, iOS, and Solaris.  

    5. Raspberry Pi

    One of the exciting parts of Python is Raspberry Pi. Using this combination, users can create robots, cameras, remote-controlled toys, or arcade machines. 

    6. Speedy

    Python is a single language in which you get speed at multiple dimensions, like: -   

    1. The learning curve for one is very short. Hence time for development is also very limited; eventually, it brings down the development cost.   
    2. Testing your code is super easy.   
    3. With the help of third-party modules, the development of larger systems like ML or Web is far quicker in comparison with other languages. 

    7. Web Development

    It’s a surprise to many newcomers that Python is a fully grown language that even supports web development. Yes, that’s right. Python has extensive support for Web-development frameworks like Django, Flask, Pyramid, and Web2py among many others. Companies like Twitter and Instagram are using Python heavily in their web applications.   

    Apart from these fundamental reasons, Python also offers the following: 

    8. Huge Community

    There are 8.2 million+ Python developers in the world, so you can imagine how big and strong the community is. During your learning curve, this community plays a critical role.   

    9. Jobs and Growth

    Python is a versatile language that has a large number of applications, starting from simple application development to automation to DS/ML applications to web-development. Once you learn the language, you can opt for many additional roles. 

    10. Salary

    We can list down many more reasons to select this powerful programming language, so it’s up to you to determine which reason resonates with you the most. We strongly suggest trying this language due to its endless possibilities which will help you to build amazing products and help businesses.

    Python Libraries for Data Science

    Python Libraries for Data Science

    Python is the easiest programming language to start your journey. Also, its simplicity does not limit your functional possibilities. We can certainly say beginners and experts can work with Python very easily to become productive quickly. Python language is free and open source, which contributes very heavily to the success of the language. 

    Is Python Better Than R for Data Science 

    We know that even though Python is powerful and very easy, it’s not the only programming language for ML/DS. We’ve few other choices but often, ‘R’ programming language is considered as another choice for doing DS/ML.   

    Let’s try and understand the similarities and differences between R and Python: -   

    ObjectiveR is primarily used for Data Analysis & Statistics.Python is used for end-to-end system development, deployment & running it in production.
    Primary UsersScholars and R&D.Programmers, Developers, ML engineers, Testers, Data Scientists.
    FlexibilityR has a different way of writing code than other languages, hence learning curve is difficult in the beginning.Python has a very simple and institutive way of writing the code. Hence learning curve is smooth.
    IntegrationRun in the local environment.Has strong support for external apps and programming languages.
    TasksIt’s good for running & obtaining primary data analysis results.Python is good for developing & deploying algorithms in PROD.
    IDERStudioSpyder, PyCharm, Visual Studio, Jupyter notebook, Eclipse.
    Important Packages/Libsggplot2, caret, and zoo is some of the most important libs in R.Pandas, NumPy, Scikit-Learn, and Seaborn are the most important libs in Python.
    1. R has a slow & very high learning curve.  
    2. # Of libraries are limited in comparison to Python.  
    3. No support for Web Development.  
    4. Limited or No support for integration with other components or languages.  

    1. Poor memory management.  
    2. Not pure multi-threading or multi-processing.  

    The above table gives you clear and practical differences between the two languages. We now know which language is more useful in which cases. However, overall Python comes out as a lucrative option, and that's why you hear terms like “Data Science using Python”. 

    Companies that Use Python for Data Science 

    Because of Python's adaptability and proficiency, businesses that employ it frequently operate on a worldwide scale. Look at these 16 multinational organizations using Python to solve major data science problems. 

    1. Google  
    2. Facebook 
    3. Quora 
    4. Amazon 
    5. Stripe 
    6. Instagram 
    7. Spotify 
    8. Netflix 
    9. Uber 
    10. Reddit 
    11. Dropbox 
    12. Pinterest 
    13. NASA 
    14. Instacart 
    15. Lyft 
    16. Industrial Light and Magic 

    How Long Will It Take To Learn Python?

    Well, I guess if you put in honest efforts and spend 3-4 hours a day learning and practicing Python, I can assure you that you can master the language within 30 days.  

    Just imagine if I ask you to learn Spanish or German. Do you think mastering it is possible in 30 days? I don’t think so. But mastering Python is possible if you’re doing it rigorously. 


    Python is “the” important skill for any data science individual and time can’t get more excited than we’re living in. You can choose to learn this language for any reason but trust me, once you master it, you will open the doors of endless opportunities for yourself.   

    Frequently Asked Questions (FAQs)

    1Do I need to have a programming background to learn Python?

     No. Python is such an easy language that anybody can adopt it easily.   

    2Is Python enough to get a job?

     Yes, if you master the language, then you can get roles as a Python developer, Django developer, automation developer, etc.   

    3Is Python best for data science?

     “Best” is a subjective term but looking at the support and diversity provided by Python language for every task of DS, we can conclude that Python does the job well.

    4Is data science with Python easy?

     Data Science is a practice that gives you a framework for extracting patterns & insights from data and Python is just an enabler for doing Data Science. Using Python, the tasks of Data Science can be easily & effectively done.

    5Is Python difficult like another programming language?

     No. Python is fairly simple.   

    6Do we need a high configuration machine to start learning Python?

    No, you don’t need it.  


    Ashish Gulati

    Data Science Expert

    Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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