Learn Python for Data Science – 9 Essential Steps to become a Python developer
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How to Learn Python for Data Science
If you’re reading this, you’re probably aware of the fact that Python is a general-purpose and very powerful programming language. It is used everywhere from scripting and web development to data science and machine learning. But what makes it a good choice for the same, and how can we master Python programming to get started with data science? Let’s find out.
Why learn Python for Data Science?
Python has a great set of libraries geared towards Data Science, like Pandas, NumPy, SciPy, Matplotlib, scikit-learn, TensorFlow, and Seaborn. These make work easy and let you focus on more important things. It is also very readable and has a simple syntax. Python is also open-source and has a very large community constantly involved in improving it. If you want to make a career in Data Science, Python is a good start; many data scientists begin with Python.
Steps to Learn Python for Data Science
Below, we list down a number of steps you can follow to learn Python for Data Science.
Step 1 – Strengthen the Python Basics
Python is a very easy language; it is a good choice for introducing university students to programming. It has a simple syntax. Python programs are easy to read, write, and understand. To get started with Python, first, learn the basics. These include types, expressions, variables, and string operations.
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Step 2 – Understand Python Data Structures
After the basics, you need to understand various data structures like lists and tuples, and sets and dictionaries. You will use these when writing code in Python. This will also help you understand how things work in Python. Try a few exercises on these.
Step 3 – Master some Language Fundamentals
You now understand the basics of and data structures in Python. Now, let’s move on to some language fundamentals. Learn about conditions like if..else and if..elif..else, for- and while- loops, functions, and recursion. You should also learn about classes and objects, and about packages in Python.
Step 4 – Learn to Use Python to Work with Data
Now, let’s learn to use Python to work with data. This includes reading and writing files with Python. This also includes learning to use Pandas to read, work with, and save data using Pandas. You will also need to preprocess data.
Step 5 – Study to Analyze Data & Gain Insights
Learn to analyze data and gain insight from it using various Python libraries. This includes ndarray from NumPy, dataframe from Pandas, multiple functions and methods from SciPy, and various machine learning methods from scikit-learn. You will also often need to prepare and train models.
Step 6 – Enroll for a Certified Online Python Course
You can boost your learning with an online course on Python. DataFlair provides the best Certified Python Course for you to learn from. Here, you will find everything in one place so you can focus on learning. Enroll in this course and solve the practicals/assignments and projects. This will give you confidence and also some hands-on experience.
Step 7 – Grasp the Data Visualization Concept
Python has multiple options for choosing a library to perform visualization. Some of these are Matplotlib, Seaborn, ggplot, plotly, and Bokeh. You will need to learn to visualize data if you want to become a Data Scientist. This reveals patterns in data that are otherwise hidden.
Step 8 – Learn to Use Python Libraries
Like stated above, Python has many libraries geared toward Data Science and Machine Learning. These include SciPy, NumPy, Pandas, scikit-learn, Matplotlib, Seaborn, Theano, TensorFlow, Keras, and XGBoost. Learn about them and learn how to use them.
Step 9 – Work on Real-world Python Projects
Choose some personal projects and work on them. You can also get involved with some open-source public projects to improve your Python and Data Science skills.
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Now that you have your plan ready, are you excited to learn Python for Data Science with DataFlair?
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Would you like to add a point to this list of steps? Let us know in the comments below.
Happy learning Python!