Why Learn Python for Data Science?
Before we dive into how to learn Python for data science, let us understand why you should Learn Python in the first place.
In detail, we have discussed why you should on our earlier article, which you can read here: reasons to learn Python. For now, here are a few answers to the question: Why learn Python?
- Python is an open-source, simple, and easy to learn
- Python is one of the most widely used programming languages
- Python is applicable in almost every domain
- Python has a vast pool of free libraries and frameworks
- Python has an excellent community of developers
Data science is all about the process of data collection, exploration, and extracting valuable insights from data. To so do, you need to organize data with scientific methods, develop learning models, visualize the findings, and more. And here is where Python is the best choice for doing all of the tasks mentioned above.
As it stands today, Python is the programming language of choice in the field of data science. According to the 2019 Kaggle ML & DS Survey, 87% of the data professional mentioned that they use Python as their primary tool. Additionally, 8 out of 10 data professionals recommend learning Python for aspiring data enthusiasts.
Why data professionals they love Python so much? Well, we got that explained on deatail on our blog DATA SCIENCE AND PYTHON: WHY DATA SCIENTISTS LOVE PYTHON.
Step 1: Configure your programming environment
First, you will need an environment set up, which will have both general-purpose Python as well as the special packages required for Data Science. Before getting into the fundamentals of Python, it is essential to configure your programming environment.
The easiest step to start configuration on your computer is first to install Anaconda. Anaconda is the most widely used Python distribution for data science. Next step is to download and install common Python data science packages. Finally, you can make sure that your programming environment is ready by installing and running Jupyter Notebook.
Step 2: Learn Fundamentals of Python
Everyone starts from somewhere. The very first step towards learning data science is to get familiar with fundamental ideas of Python programming language.
Fundamentals of Python for Data Science
Variables: Variables can be seen as containers to store value. As the name suggests, variables can be changed. Python, unlike other programming languages, it is not required to declare variables before use. You can create and use a variable whenever needed.
Data Types: Data type is an important concept in learning Python. It is the classification or categorization of data items. It represents the kind of value that tells what operations can be performed on a particular data. In Python, we have following six types of data:
- Number
- String
- List
- Tuple
- Set
- Dictionary
We have covered the detail on what is Data Types? What are the different Data types in Python on our blog: Data Types in Python
Operators: Operators are special symbols or constructs used to perform operations between operands. Operands can be a variable or any data type in Python. Consider the expression 1 + 2 = 3. Here, 1 and 2 are operands and “+” is an operator.
Python divides the operators in the following groups:
- Arithmetic operators
- Assignment operators
- Comparison operators
- Logical operators
- Identity operators
- Membership operators
- Bitwise operators
Loops: Loop is a code statement that repeats a sequence of instructions until a specific condition is met. Python has two types of loop, for and while.
Functions: Function is a block of organized, reusable code used to perform a single, related action. You can pass data, known as parameters, into a function. A function runs only when called.
Conditional Statements: Conditional statements in Python programming are used to make decisions based on certain conditions. Conditional statements are handled by IF statements in Python.
There are tons of resources available on the internet for you to start learning Python fundamentals. You can also start your Python journey with any of the books we reviewed on our blog Best Books to Learn Python for Beginners .
Kick-start a Python Journey: Join a Python community
Step 3: Learn Python Data Science Libraries
One of the significant reasons why Python is so popular amongst data scientists is the availability of open-source libraries that make data tasks much more comfortable.
Python libraries are data science useful for tasks of data collection, processing, modeling, and visualization. Some fo the best and most popular data science libraries in Python are:
NumPy: It is the most fundamental library to learn Python for Data Science. NumPy, which stands for “Numerical Python,” makes various mathematical and statistical operations easier. Many of the other data science libraries are built on the top of NumPy.
Pandas: Pandas is built on the top of NumPy and is useful for data structures and exploratory analysis. It is a library created specifically to facilitate working with data. It’s a must-have for data wrangling, manipulation, and visualization.
Matplotlib: Matplotlib is a compelling data science library for visualizations. It helps data scientists to make quick charts and graphs from the data.
Scikit-Learn: Scikit-Learn is a trendy high- performance Machine Learning library in Python. You can start to learn how to implement some of the most common machine learning algorithms using Scikit-Learn. It is used in data science during clustering, regression, model selection, dimensionality reduction, and classification.
Step 4: Practice Mini Projects
Learning by doing will get you a long way if you are looking to learn Python for Data Science. It would help if you tried your hands-on with some mini projects on Python as you progress with theoretical knowledge.
You can start by building some simple Python applications like a calculator or a to-do list. Practical implementation of what you have learned will help you solidify your skills and build confidence to move ahead.
You can start with basic web scraping and analyze the collected data to get some insights. It will polish your skills in data collection, modeling, and data visualization techniques.
If you are planning to start your career in data science, keep an eye on developing your portfolio. You can experiment on various projects on data science, showcasing your skills. Also, don’t forget to collaborate and communicate about your work with fellow data scientists.
Step 5: Apply Advanced Data Science Techniques
Learning Python for data science is a continuous journey. Once you have completed the fundamentals of data science and Python, it’s time to advance your skills.
Next step in learning data science, you will know what is beyond the Jupyter notebook or what data inputs are in hundreds of thousands? You will also learn new Python libraries, cloud computing, and new advanced tools used in data science.
You could start getting comfortable with collaboration techniques, testing, continuous integration and deployment, meta-programming, performance optimization using complex data.
Conclusion
Data science is an ever-growing field, and you must stay up to date with the latest trend across the area. So, never stop learning.
As a beginner, it can sometimes feel both tempting and confusing for you. So we have got you covered for preparing yourself for learning Python on our blog Beginner Tips for Learning Python Programming