How to Learn Python for Data Science in 5 Steps

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What is the average salary for a data scientist:

According to the survey, the average salary for a data scientist ranges from 3 to 4 lakhs per year. As demand for data scientists rises, that number is only anticipated to increase. In upcoming years, data science job vacancies are expected to increase by 5%.

Both Python and data science appear to have a promising future. Now, learning Python is easier than ever. Now, we shall discuss it in five simple steps.

How to Learn Python for Data Science

Step 1: Learn Python basic

If you want to becoem proficient in a python programming language, you must understand the basic concepts of Python. So, before diving into the in-depth learning acquiring knowledge of the basics concepts of Python will help you know further about how Python is used in data science techniques and tools used in data science.

As a first step, you should learn the Properties, Data types like Numbers, Strings, Lists, Bytes objects and Bytearrays, Tuples, Sets, Dictionaries and many more. In addition, you should learn Strings, Flow control statements, Functions, Classes, Exceptions, File I/O, Conditions and variables.

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If you want practical learning, you can join a python course, participate in Bootcamp, learn books, attend seminars, etc. So, if you want to learn, you should grab all possible ways for learning, and consistency will help you shine in your career.

Many python beginner books are heaped on the market, and more advanced python books have been introduced. So, you can learn books like:

• Python Crash Course
• Head-First Python: A Brain-Friendly Guide.
• Python Programming: An Introduction to Computer Science (3rd Edition)
• Automate The Boring Stuff With Python, 2nd Edition: Practical Programming For Total Beginners
• Learn Python the Hard Way:3rd Edition

You can start learning these books once you have joined the python course. Books provide you better understanding after getting practical learning. You can join Python Training In Chennai, which will help you understand the unique features of Python, Python-2 and Python-3 differences, Install Python and Environment Setup, Python Identifiers, Keywords and Indentation, Python Basic Data Types and What are variables?

Find an online community

To keep you updated and stay motivated, you should attend online communities. Joining like-minded people will help you learn lots and stay revamped on the current features of Python.

If you join the python community, you will get opportunities to learn with the question and answer sessions and aid in meeting different cultural people who have different perspectives or opinions. So, joining a community will help you have wider exposure.

Moreover, you will get a chance to meet industry professionals and can build a relationship. With this healthy connectivity, you can get job opportunities as referrals, accounting for 25 of all hires.

There are many top 10 Python communities like:

• PySlackers
• Full Stack Python
• NHS Python
• PythonistaCafe
• Python
• HackerEarth
• Toptal
• Real Python

Many students find registering for a Kaggle account and joining a neighbourhood Meetup group beneficial.

Step 2: Practice through practical instruction

Practical learning is one of the best ways to advance your education. Work on Python projects for practice. You might be astonished by how quickly you pick things up when you create simple Python programmes. So, taking up a course will help you learn more. Some of them are as follows:

So, to get hands-on training, you can join Python Training in Bangalore, which will help you have a profound understanding of the core concepts of Python like:

• List, Ranges & Tuples In Python
• Python Dictionaries And Sets
• Input And Output In Python
• Python Functions
• Python Object Oriented
• Python Exceptions Handling
• Python Regular Expressions
• Common Web Application Tools and many other concepts

Another way to practice and learn

You can read blogs and python tutorials to enhance your knowledge and skills. If you read blogs related to Python, you will acquire lots of theoretical ideas which will help you understand how to implement theoretical knowledge in real-world projects.

Step 3: Learn Python data science libraries

The four most crucial Python libraries are Scikit-learn, Pandas, Matplotlib, and NumPy.

• NumPy — A library that simplifies numerous mathematical and statistical procedures; it also serves as the foundation for many pandas' library features.
• Pandas — Pandas have methods for handling missing data, executing mathematics, and working with the data.
• Matplotlib — The Matplotlib toolkit is frequently used to visualize information and produce interactive data visualizations. A visualization library that accelerates and simplifies the creation of graphs from your data.
• Scikit-learn — The most famous library for machine learning work in Python. SciPy is a library that is used in scientific computing. Along with modules for integration, interpolation, special functions, and linear algebra, it also contains modules for signal and image processing.

NumPy and Pandas are effective tools for exploring and testing data. A data visualization library called Matplotlib creates graphs similar to those in Google Sheets or Excel.

Now, we shall discuss libraries or tools used in Data Science in detail:

Data Mining

• Scrapy
• BeautifulSoup

Data Processing and Modeling

• NumPy
• SciPy
• Pandas
• Keras
• SciKit-Learn
• PyTorch
• TensorFlow
• XGBoost

Data Visualization

• Matplotlib
• Seaborn
• Bokeh
• Plotly
• Pilot

Apply the Knowledge to Projects

After learning the fundamentals of Python and the libraries of Python, you will have a brief idea for creating a project. So, you can start work on your project and implement your knowledge. Try doing a project will help you find your potential and aid you in learning new things in practice. Creating a project of your own will add credentials to your resume.

You can create structured projects on data science, machine learning, webpages, mobile applications, games, automation scripts, etc.

Once you're done with the more difficult sections, start working on your unique ideas. Utilize the resources available to you while working toward your Python objectives.

You should have mastered the ability to identify and fix mistakes at this point. You can build upon existing projects, discover open source platforms for contributions, work as a volunteer for nonprofits, contribute on GitHub, and more.

Step 4: Build a data science portfolio

Aspiring data scientists must have a portfolio because it's one of the essential characteristics hiring managers to look for in a candidate.

Each of these projects should showcase intriguing insights you discovered while working with various datasets. Consider the following project categories:

• Data Cleaning Project — While working in an organization, you will probably work on a project; clearing unstructured data will engrave potential employers since most real-world data needs cleaning.
• Data Visualization Project —The ability to create appealing, simple-to-read visualizations is a programming and design challenge, but your analysis will be more helpful if you succeed. Your portfolio will stand out if a project includes attractive charts.
• Machine Learning Project —You will undoubtedly require a project demonstrating your machine learning (ML) abilities if you want to work as a data scientist. Several machine learning projects, each primarily focused on a different algorithm, might be what you need.

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Effectively present your portfolio:

It should be written in a format similar to a Jupyter Notebook to make your analysis understandable to a technical audience,

Do you need a theme for your portfolio?

A specific theme is not necessary for your portfolio. Find interesting datasets, then figure out how to link them. Showcasing projects related to a particular industry is a terrific option if you want to work for a specific business or field.

These projects show potential employers that you have invested the time to master Python and other crucial programming abilities.

If you want to become a python developer or you intend to learn python to become a data scientist, you can join Python Training in Chennai, which will help you have a profound understanding of python concepts like Data types, types, Strings, Functions, Classes, Exceptions, etc.

Step 5: Use cutting-edge data science methods:

Finally, develop your abilities. Although learning new things will be continuous in your data science path, there are advanced Python courses you can take to be sure you have covered everything.

Gain confidence with the k-means clustering, classification, and regression models. You can also get started with machine learning by learning about bootstrapping models and building neural networks with Scikit-learn.

If you intend to become a data scientist, you can join Data Science Course in Chennai and learn lists, ranges & tuples in Python, Python Dictionaries and Sets, and Input and Output in Python.

Helpful Python Learning Tips for Beginners

Ask questions

If you want to learn Python, it has a rich community to help the learners who intend to learn data science with Python. There are multiple resources heaped on the internet. If you want to take up the course, you will get theoretical, hands-on trainig with interview questions and answers, which will help you encounter the interview.

Use Git for version control:

Git is the most crucial tool to help you track the changes in your code's modifications. This makes making adjustments, trying new ideas, and collaborating with others easier.

Learn fundamental and advanced statistics.

Learn fundamental and advanced statistics:

It would be good if you gained a solid statistical basis while learning Python for data science. Knowing statistics will help you develop the attention necessary to uncover insightful information.

Now, you would have understood 'what is data science in Python, how Python is used in data science techniques and tools used in data science.' So, if you want to learn data science, you can join Data Science Course in Coimbatore, which would help you understand Python functions, Python Exceptions Handling, data analytics using Python and many more core concepts.