We all know that the boom in data science has changed the value of most of the programming languages. Python is also one of those languages that get affected by data science. Even it becomes one of the most important programming languages for data science. That is why most programmers are trying to learn python for data science.

If you are a python noob and trying to learn python for data science then this blog will help you a lot to start your journey of data science. Let’s have a look at some of the crucial steps. It would help every programming to get started with python for data science.

**Python Programming Basics**

The initial step to start any programming language is to understand the basics of the programming language. You should start by understanding the basics of the language, libraries, and data structure. The free course by Analytics Vidhya on Python is one of the best places to start your journey. This course focuses on how to get started with Python for data science and by the end. You should be comfortable with the basic concepts of the language

**Learn Regular Expressions in Python**

A regular expression is a sequence of characters that define a search pattern. Typically, these schemas are used by string search algorithms for the operations “find” or “find and replace” on strings or for input validation. It is a technique developed in theoretical computing.

Lean the regular expression in python it will help you a lot to do data cleansing and a lot more data science-related tasks. It is also useful in data sorting, collection, data mining, and a lot more techniques.

**Learn Scientific libraries in Python **

Learn the most crucial libraries in python for data science. It will help you a lot to get started with data science.

- NumPy is a python programming language library, adding support for large, multidimensional arrays and arrays. Along with a large collection of high-level mathematical functions to work with these arrays.
- SciPy is a free and open Python library used for scientific calculations and technical calculations. SciPy includes modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other common tasks in science and engineering.

- Matplotlib is a Python rendering library and its numerical mathematical extension of NumPy. Provides object-oriented APIs for embedding charts into applications using generic GUI tools such as Tkinter, wxPython, Qt, and GTK+.
- In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numeric tables and time series. It is free software issued under a three-cell BSD license.

**Data Visualization**

Now, this is the time to learn data visualization in python. Data visualization is a graphical representation of the data. It involves the production of images that communicate the relationships between the data represented with the viewers of the images. This communication is achieved through the use of a systematic mapping between graphical marks and data values in the creation of the visualization.

Python is one of the most powerful as well as free language for data visualization. And you can use different charts and graphs in python for data visualization. If you have a better command over math and statistics then you can learn data visualization easily in Python.

**Learn Scikit-learn and Machine Learning**

Scikit-learn is a free machine learning software library for python programming language. It contains various classification, regression, like clustering. and burst algorithms, including supporting vector machines. Apart from that supervised learning algorithms, decision trees, ensemble modeling, and non-supervised learning algorithms. Along with that also learn machine learning in python will help you a lot.

**Deep Learning**

Deep learning is part of a wider family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised, or unsupervised. Learn deep learning in Python help you to automate lots of tasks of machine learning. And help you to make a better prediction with the data.

**Practice on your project**

Now, this is the time to get started with your data science project. There are plenty of platforms where you can get data science projects to work along with the leaders. It will help you a lot to sharpen your data science skills with python programming. Do not forget to practice the key concepts of python for data science.

The more you practice the more you get confident with your skills. So don’t try to skip your practice. Do practice of every concept when you learn the concept for the first time. Keep in mind that start with the small data science project. And then go for the big and complex data science project to test your skills.

### Let’s Some Up

If you follow all these tips mentioned in this blog post. Then you can learn Python for data science from beginners level. As the experts say that basics are everything that is the reason if you work on your basics. Then you can get a strong command over python for data science. In this way, you can work on the enormous as well as complex data science projects. You can extend the features of python with its libraries. That is the reason it is the best programming language for data.

At last, I would like to suggest you that keep working on the basics. And also try to learn statistics for python. It would be helpful for you to cover the fundamentals of data science. As we know that the core of data science is statistics and statistics also useful in machine learning algorithms. So don’t underestimate the power of statistics.

If you are not good at statistics but good in programming. Then data science might not be the best option for you to enhance your career. Because if you want to learn python for data science then the statistics is a must-have thing for you. Best luck to start your journey of data science.