How to Learn Python for Data Science in 2023?

In a world where Data Science is advancing rapidly, strategic decisions are most likely dependent on the use of efficient data. Business organizations understand the importance of Data Science in the current world. By 2026, it is predicted that the Indian Analytics industry would grow to a value of $119 billion creating more than 11 million jobs in Data Science. In the field of Data Science, where programming is an essential task, Python has become the most favored programming language for Data Scientists.  Most Data Science aspirants have a common question before pursuing this field- “How to learn Python?” This blog post would provide clear demarcations for learning Python and the steps to become an expert in the programming language. 

Reasons to learn Python for Data Science 

Python as the most popular and highly used programming language carved its way through by overtaking various other programming languages. It is preferred by most Data Scientists in the industry today. You need to learn Python for Data Science because of the following reasons: 

  • The highly efficient platform for competitions in Data Science, R on Kaggle was overtaken by Python in 2016 
  • In 2017, it became the most used tools in the annual poll of R on KDNuggets 
  • In 2018, Python became the number one programming language used by over 66% of Data Scientists. 
  • Python became the most widely used and highly popular programming language in 2021, overrunning Java in the TIOBE Index. 

As Data Scientists find Python to be the most efficient programming language in the world, they want the trend of efficiency posed by Python to continue. 

How to learn Python- 6 Steps to become an Expert 

How to learn Python

Step 1: Learn the Basics of Python 

The basics of Python includes numerous topics and concepts which would help you in building a foundation before you move to learn critical topics. The fundamentals of Python can be learnt by opting for basic level online programming courses that would provide you with clear knowledge on the topics. The various topics included in the first step to learn Python for Data Science are: 

  1. Installing Python & Python Environment
  2. Numbers
  3. Strings
  4. List
  5. Variable Scope 
  6. Dictionary
  7. Sets
  8. Classes
  9. Methods & Attributes
  10. Modules & Packages
  11. List Comprehension
  12. Map, Filter, and Lambda
  13. Decorators 
  14. File Handling
  15. Standard Input, Output
  16. Basic commands in Python
  17. If-then-else statement
  18. Loops
  19. Functions

Pickl.AI provides through its online Data Science course a comprehensive curriculum where you would be able to learn about the basics of Python. Additionally, one of the short-term courses by Pickl.AI- Python for Data Science- is a self-paced program that would allow you to learn all about Python. 

Step 2: Know about Python Libraries and their uses 

To learn Python for Data Science, the next step involves learning about the various Python libraries which are used to perform different tasks related to Data Science. Importing Python libraries to your script would consume less time as it indulges in collection of pre-existing functions. The various popular libraries in Python includes: 

  • NumPy: Using this Python library, you would be able to convert different types of non-numerical data into numerical data. All kinds of numerical operations in data are conducted using NumPy. 
  • Pandas: this is mainly a tool for data manipulation and data analysis using which it is possible to work with data frames, 
  • Matplotlib: Python library can also be used for data visualization thereby interpreting raw data into simpler terms using graphs or charts. Matplotlib ensures that you are able to draw a chart or a graph to evaluate results. 
  • Scikit-Learn: this is the most popular Python library for machine learning which has various ML algorithms for cross-validation and pre-processing, etc.

Step 3: Learn about the Basics of Statistics with Python 

Basics of Statistics with the help of statistics it is possible to make use of the accurate algorithm which would help in solving problems. accordingly, statistics becomes an important part of Data Science while its requirement is also identified for Python. Statistics for Data Science includes topics like statistical tests, distribution, and maximum likelihood estimators. However, to build a statistical model, there is a Python Library that can be utilized. StarsModel. This library finds its place above Numpy SciPy and Matplotib. It contains advanced level functions which can be used for the purpose of statistical testing as well as modeling. 

Also Read: Data Visualizations in Python and R: Python V/S R

Step 4: Learn about Accessing Database 

The data that you collect needs to be stored and managed within a database for which using SQL would be necessary. In addition, you can also use Python to connect with your databases.  

How to use SQL and Python? 

It becomes important for you to learn about SQL so that you’re able to retrieve data along with using Jupyter Notebook in Python that can help in Data Analysis. SQL is the programming language which is used for data manipulation along with Pandas. However, some tasks need to be performed by Pandas individually. Hence, use of SQL can be made for retrieving data while manipulation of those data can be enabled using Pandas. 

Step 5: Use Scikit to build your Machine Learning Model 

Scikit is the library of machine learning in Python which is used for its machine learning algorithms. Using the Scikit Learn you are required to implement common ML algorithms and conduct experiments with them. When you identify a problem related to machine learning you need to apply different algorithms and find out which gives you the most accurate outcome required to solve the problem. 

Step 6: Practice 

With learning of all the aspects of Python from basic fundamentals to libraries and even learning the use of SQL and Machine Learning algorithms, you now need to undertake rigorous practice. Practicing all of these aspects in real-life and experimenting at all levels would help you gain a higher level of efficiency. For this, you might take part in competitions of coding or related Data Science competitions where you can make use of your knowledge, implement them and gain more confidence in using Python. 

Python for beginners and expert

Python Learning Tips for Beginners 

Python is the most efficient high level programming language in the industry today and you need to be efficient from the beginning. As Python is being increasingly used by Data Scientist and the Analytics industry, being a beginner in learning Python would need you to follow some tips and tricks that can help you along your career development: 

  • Identify your interest area: Python can be used in different fields including Artificial Intelligence, Machine Learning, robotics, Python Scripts, etc. You need to find your interest area before you start learning Python that would help you to progress in a definite direction. 
  • Learning Basic Syntax: Python Syntax can be identified as the basic level concept for any beginner to learn. It would take very little time to get acquainted with the Python Syntax before you learn deeper coding concepts. If you have any doubts regarding the Python concepts, you can always revise the basic syntax. 
  • Practicing everyday: While you are learning Python as a programming language, you should practice coding everyday. This would help in increasing your efficiency in the task while making you consistent in the language. You can start from the basic level coding and then proceed to increase your coding time as you progress. 
  • Making Notes of your learning: It helps a great deal when you make notes while learning different concepts in Python. It would help in increasing your concentration as well as help in your future to clear doubts. Making notes helps you in jotting down important points which can be useful in the long run. 
  • Apply for an online course: you can always opt for an online course on Python that would help you learn basic to core concepts. Online courses include relevant resources and study material that can help you with expanding your knowledge and make you proficient. Pickl.AI offers a self-paced online course on Python for Data Science which includes basic fundamentals to core concepts. 
  • Interact with Python Community: While learning Python you may come across an online community of Python. Here, you may be able to interact with different people in the field from beginners to experts who might help you with doubt clearance. You may be able to participate in group discussions as well as webinars which would help you expand your knowledge. 
  • Take part in Projects: Using your theoretical knowledge in practical experiments is essential. You may indulge in small and beginner level projects and make yourself competent. As you progress with your knowledge and learning, you can actively take part in bigger projects where you can use your Python concepts for boosting your efficiency. 


From the above post, it can be concluded that Python is the fastest growing programming language used by Data Scientists today. If you want to be skilled in Python, you should have the knowledge of basic Python fundamentals to use Machine Learning algorithms for solving problems in an organization. However, you can become proficient in the programming language when you proactively take part in competitions or live experiments of coding. As Python becomes the widely used programming language in the industry, every Data Science aspirant must learn and develop this skill. 

Asmita Kar

I am a Senior Content Writer working with Pickl.AI. I am a passionate writer, an ardent learner and a dedicated individual. With around 3years of experience in writing, I have developed the knack of using words with a creative flow. Writing motivates me to conduct research and inspires me to intertwine words that are able to lure my audience in reading my work. My biggest motivation in life is my mother who constantly pushes me to do better in life. Apart from writing, Indian Mythology is my area of passion about which I am constantly on the path of learning more.