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An Ultimate Guide To Python For Data Science (2024)

Introduction to Python for Data Science

Are you looking forward to learning data science with Python? Want to know what is data science in Python? You have come to the right place if that's even a slight nod anywhere. 

Python for data science has now emerged as the preferred language to be used by many data scientists worldwide. It is considered a high-level language and a good choice for object-oriented programming. It offers massive functionality for dealing with mathematics, scientific functions, and even statistics. There are extraordinary libraries that deal with data science applications. The main reason for the growing popularity of Python in data science is that it is widely used in the scientific and research communities due to its ease of use and simplicity of syntax. Because of this, Python is being adopted by people who still need an engineering background.

Read: Data Science vs Machine Learning- Career That is Right for You

The people from academia and industry believe that the deep learning frameworks which are available with the Python APIs and other scientific packages have made Python very versatile and productive. Thus, there has been a huge rise in learning the Python frameworks in recent times. Even in application areas, Python is preferred by the ML scientists as well. In the case of applications like natural language processing NLP and other sentiment analysis etc. developers also opt for Python as the latter offers a great number of libraries that help to solve complex problems quickly.

In this blog we are going to discuss how to learn python for data science, what all python for data science handbooks are available  and python for  data science tutorials.

Why Should you Learn Python for Data Science?

Over the years, Python has developed a dedicated community of users and an even more faithful following with the professionals working with data science. Let's check the reasons in detail that shall compel you to learn  Python for data science:

  • Ease of Use: If you have to rate the quality of Python in terms of simplicity, it does well on that scale. Its simplicity is based on its accurate and methodical syntax. It has the beauty of finishing the same tasks as done by other languages but with much less code. This implements really fast solutions.

Java and python

  • Enough Material AvPythone: Python in data science developed a highly varied and colorful community of data scientists, which means that there is no shortage of Python data science handbooks and Python for data science tutorials, fixes for commonly occurring bugs, code snippets, etc.
  • 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 considerable savings in time and effort. Some of the most popular libraries are NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch, NLTK, etc.
  • Machine Learning: Python is also endowed with a state-of-the-art library for data analysis and machine learning, which considerably brings downtime needed to give relevant results.
  • Extendable: The code is easily extendable by adding new modules formed in other programming languages like C++ or C. Python with data science, is considered an expressive language, which means offering a programmable interface by embedding it into the applications is possible.
  • Lightweight: Python is lightweight and super portable. It lets developers efficiently use the code in cross-functional programming like SQL, Java, and Unix. Data science Python can run on any OS, including Windows, Unix, iOS, and Solaris
  • Raspberry Pi: One of the exciting parts of Python is Raspberry Pi. Users can create robots, cameras, remote-controlled toys, or arcade machines using this combination. 
  • Data Cleaning is a Breeze: A significant part of data science involves data cleaning, which can be labor-intensive. Python excels in this area with libraries like NumPy and Pandas, making it easier to handle data-cleaning tasks.
  • Effective Communication: After analyzing data, communicating findings is crucial. Python in data science aids in creating clear visualizations for data presentation. Tools like Matplotlib, Pandas, and Seaborn in Python simplify the creation of effective visualizations, enhancing the communication of data insights.
  • Quick Prototyping: Python for data science is highly efficient for building prototypes to test ideas and concepts. This is essential in data science, where projects can be resource-intensive. Python's ability to facilitate both dynamic and static analysis makes it an excellent choice for quickly developing and testing prototypes.

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Version Battle: Python 2.7 v/s 3.4

This has become the most discussed topic, and if you plan to learn Python for data science, this topic will cross your eyes especially if you are a beginner in the field.

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Python 2.7

  • It comes with the wonderful support of the community which is essential in the early days. Python 2 which had been released since late 2000, has been in use for over 15 years now.
  • There is a warehouse of third-party libraries. Although many libraries have given 3.x support, many modules still work only on version 2.x. Thus, if you plan to use Python for particular applications like web development with heavy dependence on other outside modules, 2.7 is a better option.
  • Many features of 3.x are endowed with backward compatibility and thus can easily work well with the 2.7 version.

Python 3.4

  • This is cleaner and quicker. Although there were some inherent glitches, they were also fixed by the developers. Many other small drawbacks were also fixed for setting a stronger foundation for the future.
  • 2.7 is the last of the 2.x family, so ultimately everything must move to the 3.x family. Many stable versions have also been released by Python 3 for the last five years and will continue to remain the same.

Read More: The Ultimate Guide To Python Web Scraping For Beginners

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How To Learn Python For Data Science?

python learning road map

To learn Python for data science, it is advisable to follow the following steps:

1). Learning Core Programming Skills:

Efficient programming means memorizing the syntax and learning a new thought or approach. You must invest your time and resources to build a strong base in core programming concepts. Such a foundation helps translate solutions in mind itself for a practical Python for data science syllabus coverage.

Irrespective of the fact that you are entirely new to the field of programming or already know any other language and have just got to memorize the Python syntax, after this level, you should have proper answers to all of the following:

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  • Difference between an integer, float, and string
  • Using Python in place of a calculator
  • Structure and use of a for loop
  • Use of conditional statements
  • The functioning of Import statements
  • Primary structure of a function

Additionally, you can also check the following resources to practice these concepts further.

  • Code Fights: It is a platform that offers you short coding challenges that can be completed in 5 minutes.
  • The Python Challenge: It is one of the most interesting challenges on the internet. It has 33 levels which can be accomplished by the Python scripts.
  • PracticePython.org: This forms a collection of short practice problems for Python for data science tutorials which are updated weekly.

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2). Data Science Libraries:

Data Science Libraries are basically collections of pre-existing functions and objects which can be imported into the script for saving on time. Python has line-up libraries for data science. Here are a few steps which need to be followed when you want to pick a new library.

  • You should open a fresh Jupyter Notebook
  • Further down, you should be thoroughly going through the documentation for getting a proper introduction about the modules.
  • Then you have to import the library in your Jupiter Notebook.
  • Here you have to go by the stepwise quickstart tutorial to see how the library works
  • Finally, you may just review the documentation to learn about other capabilities.

A Jupyter Notebook is a favorite among data scientists. It is basically a lightweight IDE and is recommended for many projects.

Read: 100+ Data Science Interview Questions and Answers {Interview Guide 2023}

3). Data Science Portfolio:

You must build a data science portfolio for all beginners in data science. Later, it can comprise projects that have several datasets and should be able to leave the readers with unique insights into your data science Python journey. Such efforts reflect your interest and the time you have given to learn the language and other vital skills for programming. You don't have to build your portfolio around any specific theme. It is also a good idea to develop your soft skills and knowledge about statistics to accelerate faster in the path of data science using Python..

Read: Data Scientist Skills Required For Your Dream Job In Organization

4). End-to-End Projects:

A basic understanding of the core programming concepts and the salient features of libraries is enough to get started with Python. However, to consolidate your knowledge, you may want to go through various data science projects for practice.

  • Kaggle Competitions: It is a website that hosts many competitions for data science. The most crucial feature of Kaggle is that each project is self-contained, and you are presented with a dataset, a goal, and a few guidelines to start. However, they do not replicate real-time data science Python 
  • DIY Projects: The primary benefit of these projects is that they represent real-world data science using Python more closely. You have to set goals, collect data, engineer the features, etc. But, for this to be successful you must know the data science workflow.

Read: An Easy To Understand Approach For K-Nearest Neighbor Algorithm

5). Apply Advanced Data Science Techniques:

As your data science knowledge improves, you ought to take up more complex techniques as you keep building your skills. Python for data science is a fast-developing field that calls for lifelong learning. You may want advanced Python certifications and courses on essential topics to strengthen your foundation.

  • Get acquainted with different data science Python models like regression, classification, and k-means clustering in depth. These underlying models serve as the spine of most data analysis activities and underpin the comprehension of intricate data patterns.
  • Explore the aspect of machine learning, which is a critical component of data science with Python. Begin with bootstrapping sampling methods that assist in approximating the distribution of sample statistics and its correlation with a model prediction. Such knowledge is key in arriving at evidence-based decisions.
  • Also, integrate yourself into building neural networks with the Scikit-learn library. Currently, many modern data-driven applications, such as neural networks, are at the vanguard, providing excellent means of pattern recognition and predictive modeling. These complicated models are made accessible through Scikit-learn, which makes them your essential data science arsenal.

Essential Tips to Learn Python for Data Science in a Faster Way

If you are dedicated to continuously learning and practicing, you will quickly become excellent at coding Python for data science by following our quick tips below. Remember that it is just as much about the journey as the end outcome. So, relish the learning experience while you follow the following tips to learn Python for data science:

(Note: The below tips may seem repetitive in some instances, but as it's in the form of harnessing the goal faster, if you follow them religiously, you will soon be a pro in using Python with data science)

  1. Start with the Basics: Ensure you have a good grip on python basics before wading into data science with Python specialized libraries. Know data types, control flow (if-else, loops), functions, and simple data structures like lists and dictionaries.
  2. Master Key Libraries: Learn Pandas (for data manipulation), NumPy (numerical operations), Matplotlib, Seaborn (data visualization), and Scikit-learn.
  3. Practical Projects: Start working on real projects as soon as you are equipped with the relevant Python for data science syllabus. The practical engagement in itself is priceless because it consolidates the comprehension of abstract notions.
  4. Understand Data Manipulation: It is imperative to understand data cleaning and manipulation. Take time to understand how to deal with the missing data, join datasets, and transform data using Pandas.
  5. Grasp Statistical Concepts: Data science is founded on basic statistics. Learn the mean, mode, median, variance, standard curves, and statistical tests.
  6. Learn Machine Learning Basics: It’s essential to begin with less complex models, such as linear regression, and then logistic regression, before proceeding to more complex models. 
  7. Practice Coding Regularly: Consistency is the key to data science with Python . Practicing at regular intervals facilitates retention and understanding. Utilize sites such as LeetCode and HackerRank for data science using Python code sharpness.
  8. Participate in Competitions: There are platforms like Kaggle where one can compete, gain practical experience, and learn from the community.
  9. Follow Online Resources: Use accessible sources such as YouTube tutorials, blogs, and online courses.  
  10. Join a Community: Interact with social online communities like Reddit, Stack Overflow, LinkedIn groups, and many more. Such communities can offer support, resources, and networks for data science with Python.
  11. Cheat Sheets: Have cheat sheets always available for Python syntax, Pandas, NumPy, and Scikit-learn. They offer an easy way to make quick referencing and save time.
  12. Stay Updated: The field of data science is growing fast. Keep abreast of the latest trends, tools, and best practices through relevant blogs, podcasts, and newsletters. It's essential to adopt a self-learning approach through the latest updates 
  13. Focus on Data Storytelling: Data analysis is no less important than the ability to present it appropriately. Learn how to tell interesting stories and design beautiful visuals to explain your results.
  14. Time Management: Balance your learning process. You don’t have to absorb all that you hear in a short period. Establish manageable steps and milestones when learning various Python and data science parts.
  15. Seek Feedback: Ensure you are always getting some feedback from experienced programmers regarding your data science Python code or project. Criticism may help you navigate around the Python data science tutorial more effectively.

Read More: Check out the Python vs Java comparison blog to accelerate your career

How Long Does it Take To Learn Python For Data Science?

 

Time to learn python

Many aspiring data scientists prefer using Python, a highly versatile programming language. Different factors influence how long it takes to learn Python for data science. Here, we'll divide this section into three phases so that you can better assess how you can learn data science online:

Certification

  • Short-term Certificates (1-3 Months): Within a period of one to three months, you may gain an idea of Python for data science and earn a short-term certificate. Such certificates normally include a first course in Python programming, manipulation of data, and some basic data visualization.
  • Professional Certifications (6-12 Months): Professional certifications like Python with data science from reputable organizations for a more holistic appreciation and recognition take about 6 to 12 months. The programs involve Python, statistics, machine learning, and real-world projects.

Eligibility

  • No Prior Programming Experience (3-6 Months): Learning to program in Python and then proceeding to data science will take about 3 to 6 months if you have never been in programming. It is essential to understand the basics of Python before proceeding to data science concepts.
  • Basic Programming Background (2-4 Months): People who understand the basics of programming concepts find time shorter for learning data science using Python. With concentrated attention towards Python for 2 to 4 months, you can later progress into data science.
  • Experienced Programmers (1-3 Months): Python requires only one to three months for experienced programmers already familiar with concepts and other languages.

Scope

  • Junior Data Analyst/Scientist (6-12 Months): Within 6 to 12 months of commitment to projects, this can enable you to be a qualified professional for Junior Data Analyst or Junior Data Scientist positions.
  • Data Scientist (1-2 Years): To be an expert Data Scientist, you must learn and gain experience for around 1 to 2 years. This refers to proficiency in Python, advanced data handling, machine learning, and domain knowledge.
  • Specialized Roles (Varies): Learners might need extra time for specialized roles like NLP or Computer Vision within data science.

Read More: If you still want to gain a bit more understanding of how much time it may take for you to learn specifically Python, then click here!

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Conclusion

Python for data science is a handy language that has found its use in various applications. It is a favorite for engineers, data scientists, academia, etc. Learning Python needs commitment and a plan. It is not very difficult to learn if you have experience with other languages. Even if you don't, it's only a matter of time before you become a pro through JanBask Training’s data science online training.

FAQs

Q1: What is data science in Python?

Ans: Python is a high-level data science language commonly used because of its simplicity and adaptability. It has many libraries and tools built for data analysis, making it necessary for data scientists.

Q2. What are the major Python data science libraries?

Ans: Some of the strong libraries of Python are NumPy, Pandas, and Matplotlib, which are useful for data science. NumPy supports large, multi-dimensional arrays and matrices, whereas Pandas offers data manipulation and analysis. Matplotlib is a plotting library that can generate different types of visualizations.

Q3. What is the best way to clean and preprocess data using Python?

Ans: Several functions and libraries of Python can be used for cleaning and preprocessing data. For example, pandas have functions for dealing with missing values, removing duplicates, and transforming data. Furthermore, Python incorporates the re-module used for pattern matching and extraction purposes.

Q4. Is it possible to do statistical analysis using Python?

Ans: Absolutely! There is a dedicated library in the Python language called SciPy with many statistical tools and functions. SciPy handles descriptive statistics as well as advanced statistical models. Moreover, libraries such as StatsModels and scikit-learn provide an even higher level of statistical analysis functions.

Q5.  What are the methods that I can use to display data through Python?

Ans:The most popular one is Matplotlib, among many libraries for data visualization provided by Python. Among the plots Matplotlib allows you to create are line plots, scatter plots, bar plots, and histograms. Libraries such as Seaborn and Plotly are used for more interactive and appealing visualizations.

Q6. Can machine learning be done in Python?

Ans: Absolutely! Python dominates machine learning due to its libraries and frameworks. Scikit-learn is commonly used for various machine learning algorithms, and TensorFlow and pitch are used for deep learning.

Q7. Is it possible to use Python for natural language processing (NLP)?

Ans: Yes, Python is highly popular in natural language processing. For tasks such as tokenization, part of speech tagging, named entity recognition, and sentiment analysis, libraries like NLTK (Natural Language Toolkit) and spacy provide various tools and functionalities.

Q8. Can I learn Python for data science online?

Ans: Yes, there are a lot of websites where one can learn Python for data science. Various websites like DataCamp, Coursera, and Udemy provide relevant courses for learning data science. Furthermore, there are many free tutorials and documents on Python and its libraries’ official websites.

Q9. Is it possible to incorporate Python with other programming languages?

Ans: Yes, it is easy to integrate Python with other programming languages. For instance, Python’s types module can call C/C++ functions from your Python code. It also has libraries such as Py4J, which enables you to work with Java from Python.

Q10. Can Python be used for real-time data processing?

Ans: However, Python may not best fit for real-time data processing due to its interpreted nature and the Global Interpreter Lock (GIL). Nevertheless, workarounds such as using libraries like NumPy and Pandas that offer high-performance data structures and functions are available.

Q11. What are the use cases of Python in the data science world?

Ans: Python is widely adopted in numerous applications of data science. It serves fraud detection, recommendation systems, sentiment analysis, image recognition, and others. Therefore, Python is a widely used, flexible language with an extensive library ecosystem to solve complex data science problems in various industries.

Q12. What is Python online course certification, and why do aspiring data scientists need to have it?

Ans: Python certification marks your competencies to analyze and manipulate data with Python. It matters for prospective data scientists because it proves your proficiency in Python, the programming language used most in data science.

Q13. What will a data scientist course online do for my career?

Ans: Taking a data scientist course online allows you to learn and improve your data science skills in the comfort of your home. It is flexible, enabling you to work around your studies and other engagements. Furthermore, most online courses also give you practical hands-on experience and industry-relevant projects that can improve your marketability in your profession.

Q14. Will I be able to undertake a Python data science certification program and work full-time?

Ans: Yes, most Python data science certification programs are specifically meant for employed professionals. With online courses, you can choose your own pace of study while having a full-time job.

Q15. How will a Python data science certification influence data scientists’ careers?

Ans: Python data science certificates are in high demand as this area is booming fast. This certification will enable you to assume many roles, such as data analyst, data scientist, machine learning engineer, and AI specialist in various industries like finance, healthcare, e-commerce, etc.

Q16. Will a Python online course certification boost my earnings?

Ans: Acquiring a Python data science certification can significantly increase your salary. Data science is a specialized area; trained and certified professionals receive higher salaries compared to others who do not possess these qualifications. Besides, a high demand for data scientists also increases their earnings.

Q17. Why do you need to learn Python for data science?

Ans: Python is one of the most influential and convenient data science languages. With an extensive and busy community, support and resources are always available. Python also has a well-developed ecosystem of libraries and frameworks for data analysis and machine learning – NumPy, Pandas, and Scikit-learn, among many others. It is a simple language that makes it easy for beginners in data science to comprehend.

Q18. Is switching to the data science career path with the Python Data Science certification possible?

Ans: Sure, a Python data science certification can be a gateway to a career in data science. It equips you with the necessary skills and knowledge to analyze and interpret data correctly, making you a key person with the required data-driven insights for any company. Nevertheless, it is crucial to keep updating your skills and be aware of the latest developments in the field to keep up with the competitors.


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