How to Learn Python for Data Science In 5 Steps
Understanding python is one of the basic and critical skills required to be a data scientist. As a data scientist has vast opportunities with a huge job market, learning python becomes a crucial aspect of a successful data scientist’s successful career.
Data science is related to data mining, machine learning, big data, and a pretty huge field. But python can be quite useful for mastering the crucial codes of data science problems and solving the most complex computations.
Following are the simple steps to learn python for data science:
Step 1: Strengthen the basic python concepts
Python is an easy language with a simple syntax. These programs are easy to read, write and understand. Start learning python from basics that include expressions, variables, and string operations.
Python is versatile, and even people with no prior experience in programming can learn this language. Remember that you have just to learn the basics, and there is no need to do a complete detailed course in python. Just utilize more time to build a foundation of core programming theories.
Step 2: Learn about python data structure
After going through the basics, now you require to understand various data structures like lists and tuples and sets and dictionaries. All these are necessary for writing codes in python. Moreover, you will have a better understanding that how things work in python.
Start a tool using early known as Jupyter Notebook. This comes as a package with Python libraries. Moreover, join a Python community to get help and ideas on all the related topics. Go for the Command Line Interface (CLI), which lets you run scripts at more speed so as to test programs faster and with more data.
Step 3: Master some language fundamentals
The next step is to understand some language fundamentals. You must learn about conditions like if..else and if..elif..else, for-and-while loops, functions, and recursion. Try to learn more about classes and objects and about packages in python.
Learn regular expressions as normal articulations will prove to be more useful with data purifying. The process helps you understand inadequate, mistaken, erroneous, or unessential pieces of data and later replace, alters, or erase the grimy or coarse data.
Step 4: Learn to use python to work with data
Now you are able enough to learn to use python to work with data. It includes reading and writing files with the help of python. This also includes learning how to use Pandas to read, work with them, and save data.
Step 5: Analyze data and gain Insights:
Learn to analyze data using various Python libraries. Python has many libraries for data science. These libraries are the simple bundles of pre-existing function that can be imported into a script to preseve time.
Some essential libraries required are:
- NumPy – It allows easy and effective numeric computation
- Pandas – It is good for data structure and exploratory analysis
- Matplotlib – This is a flexible plotting and visualization library
- Scikit-Learn – This is a general premium learning library with many popular algorithms
- Bonus: Seaborn – Used to plot common data visualizations in an easy manner
Step 6: Grasp the data visualization concept:
Python also has a wide option of libraries to perform visualization. Some of these can be named Matplotlib, Seaborn, ggplot, Plotly, and Bokeh. Data visualization reveals the hidden patterns in data and is necessary if you want to be a successful data scientist.
Step 7: Learn to use python libraries
Python has a bundle of libraries to help learn data science and for machine learning. Understanding these libraries and learning to use them is another step to learn python for data science. These libraries include SciPy, Numpy, Pandas, scikit-learn, Theano, TensorFlow, Keras, and XGBoost.
Step9: Work on real-world python projects:
Finally, choose some personal projects and start working on them. Try to get involved with open-source, public projects to help improve your Python Data Science Skills. Implementing your theory in some practical aspects is absolutely fantastic.
It is a common phenomenon in data science when one gets stuck on a problem. The best part of learning python is that it has a brilliant community with lots of people and resources to help you come out from any situation.
Consider this a concise guide to master python for data science. But you would not gain anything from this. It is important to learn python from scratch and understand its basics with conceptual interpretations and stay familiar with this incredible language.