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If you are taking Data Science as a career then it is very essential to learn Python. But, it was not always a programming language. Did you know? It is noted that in 2018, most data scientists’ reports said that Python was the main language for analytics professionals. The employment opportunities are quite vast as a data scientist using Python.
There are simple steps to learning Python. But remember the steps are simple yet you need to put lots of effort to become an expert. The efforts and dedication will not only help with the new skill but also will boost the career to a high level.
The first step is to find the right course that can help you in learning programming in python well. Data scientists require other skills also like some soft skills and technical skills other than python programming. Below are the 5 essential steps to learn data science with python;
Firstly you have to learn Python programming basics which will also give an introduction to data science. Jupyter notebook will be very helpful in the journey of learning. Try to learn the Command Line Interface which will help you to run the scripts very quickly. It also helps to test the programs faster and work with more data.
Practice the Mini Python Projects.
You can be an expert when you believe in hands-on learning. This way you will be surprised in a very short time you will be able to build small python projects.
Some of the simple projects could be some fun projects that also gives you some real insight. Try to find some survey data projects to use your skills in real data and offer guidance and give you the challenge to apply your skills in different ways. Building some of the projects will help in learning Python programming projects and help you in understanding the basics.
You can also start with web scraping which will help in gathering the data at later stages.
Some of the books that you can read are
- The Data Science Handbook.
- Python Data Science Handbook.
- Elements of statistical learning.
The three important Python libraries for data science are Numpy, Pandas and Matplotlib. NumPy makes mathematical and statistical operations very easier. This is the basics for most of the features of Panda Library. Pandas are specially created to facilitate working with data.
Matplotlib is a visualization library that makes to generate charts quickly and faster. The most popular library main for machine learning work with Python is scikit learn. NumPy and Pandas are mainly for exploring and also for playing with data. Matplotlib is a data visualization library that makes graphs that is like excel and googles sheets.
Some of the projects that can be considered are
- Data Cleaning Project will impress the employers as real-world data requires cleaning.
- Data Visualization Project is easy to read visualizations and if it is done properly then your analysis will have a good impact.
The portfolio need not be the particular theme. Collect the databases and put them together. If you are working for a company then the project should be relevant to the industry. The projects are a reflection of the efforts that you have put to learn Python. One more important thing to remember is while you learn Python for data science it is better you have a good statistics background. This will help in focussing on the right things.
The last step is that you have to sharpen your skills as data science learning is never an end and you have to be in constant learning. Some of the programming projects can be included in creating models using live data. The demand for data science is increasing day by day and many potential opportunities can be learnt.
If you have a consistent practice then the duration would be between 3 months to a year.
Ultimately it depends on the time you are dedicated to learning Python. The courses are for you to learn each step giving much time. The steps are full of lessons and opportunities where you can master the data science fundamentals. It’s possible to work as a data scientist using Python or R. Each has its strengths and also the weakness and both are used in all the industries. The most popular is Python.
Python can be concluded as much better for all-around work in almost all industries. and is much easier compared to other languages.
- Python and SQL are used to pull the data from the database.
- Python and the Pandas are used to clean and sort the data.
- Python, Pandas and matplotlib libraries, explore and also visualize the data.
- Once you learn about the data then Python and the scikit-learn library is used to build a predictive model that gives the future outcomes for your company.
- Then the final paralysis is arranged in the proper format. Python is used almost in every step.