How to get started with Python programming for learning Data science

Nowadays Data science is a trending topic. But you may require to learn Python for learning data science. Firstly before knowing about the importance of python programming , you may need to learn basics of data science and usage of python basics in Data science .“Data science” is about as specific a concept as it comes by listing its more specific components.

Data discovery & analysis:

It may be easier to explain what it is.

Included here:

Pandas, NumPy, SciPy, a basic library supporting hand from Python programming.
Data Visualisation
A rather self-explaining name. Take data and making it colourful.

**These include: **

Matplotlib; Seaborn; Datashader; others.
Classical machine learning
You may describe this conceptually as any supervised or unsupervised learning activity that is not deep learning.
These include:
Scikit-Learn, StatsModels.
Deep Learning

This is a subset of machine learning that is undergoing a revival, and is widely implemented among other libraries with Keras. Over the last ~5 years it has seen significant changes, including AlexNet in 2012, which was the first architecture to integrate consecutive convolutional layers.

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**Here included: **

Keras, TensorFlow and a number of others.
Big data systems and data management.
Big data is best described as data which, in the absence of a distributed environment, is either simply too large to exist on a single computer or cannot be processed. Here, Python’s links to Apache technologies play heavily.
Apache Spark; HDFS; Apache Hadoop; Dask; h5py / pytables
Chances and ends.

It features subtopics such as manipulating the natural language, and image manipulation with libraries such as OpenCV.
Included here: nltk; spacy; OpenCV / cv2;.
Step 1:
Learn Fundamentals of Python Programming.
Everyone starts somewhere. This first step is where the fundamentals of Python programming will be learned. You are probably going to want an introduction to the data science.
Jupyter Notebook, which comes prepackaged with Python libraries to help you learn these two things, is one of the crucial resources you can start using early on your journey.

Start your learning by:

joining a community By joining a group, you’ll be putting yourself around like-minded people and growing your work opportunities. Employee referrals account for 30 per cent of all hires, according to the Society for Human Resource Management.

Build a Kaggle account, enter a local Meetup group and engage in discussions with current students and alumni on Dataquest’s members-only Slack.

Related skills:

Use the Command Line Interface
The Command Line Interface (CLI) helps you to run scripts faster, allowing you to check programs more easily and to work with more data.
Step 2:
Mini Python Projects Exercise
You genuinely believe in hands-on learning. How soon you’ll be able to create small Python projects can surprise you.

Try Python programming things like online game calculators, or a program that gets Google weather in your area. Creating these mini-projects can help you learn the Python. Projects like these are common for all languages, and a perfect way to solidify your simple understanding.

You will start building up your API knowledge and start web scraping. In addition to helping you learn how to program Python, web scraping can be useful to you later when gathering data.

Start your learning with:

Reading Improve your coursework and find answers to the problems you encounter in Python programming. Use guidebooks, blog articles, and even the open source code for other people to know best practices in Python and data science – and get new ideas.

Automate The Boring Stuff With Python is an excellent and enjoyable tool by Al Sweigart.

**Similar skills: **

Working with SQL
SQL databases is used to converse with databases to change, modify, and reorganize information. SQL is a standard in the data science community, as it is regularly used by 40 percent of data scientists.
Step 3:
Learn Python Data Science Libraries
Unlike some other Python programming languages, Python typically has the best way to do something. NumPy, Pandas, and Matplotlib are the three best and most popular Python Libraries for data science.
NumPy and Pandas are perfect for data exploration and gambling. Matplotlib is a tool for data analysis, creating diagrams as you would find in Excel or Google Sheets.

Start your learning with: Ask questions You do not know what you do not know!
Python has a wealthy group of experts keen to help you learn Python. Tools such as Quora, Stack Overflow, and Dataquest’s Slack are full of people passionate about sharing their expertise and helping you learn Python. You also have a FAQ for each task to help with problems you find with Dataquest during all of your Python programming courses.

**Similar skills: **

Using Git for version control Git is a common tool that helps you keep track of changes that have been made to your code, making it much easier to correct errors, experiment, and collaborate.

**Step 4: **

Create a Data Science Portfolio as you Learn Python
A portfolio is a must for aspiring data scientists.
Such ventures should include many different datasets and give interesting insights to readers that you have gleaned. Your portfolio doesn’t need a specific theme; finding datasets that concern you, and then finding a way to bring them together.

Displaying projects like these offers some teamwork to fellow data scientists, and shows future employers that you have really taken the time to learn Python and other essential Python programming skills.
One of the nice things about data science is that, while showcasing the skills you’ve learned, your portfolio serves as a resume, like Python programming.
Start your learning by:

talking, sharing and concentrating on technical skills During this period, you will want to ensure that you
develop the soft skills you need to interact with others, making sure you really understand the inner workings of the resources you use.

**Similar skills: **

Learn beginner and intermediate statistics While learning Python for data science, you’ll want to have a good statistical background too. Knowing statistics will give you the attitude you need to concentrate on the right stuff and you can find useful insights (and actual solutions) instead of only executing code.
Step 5:
Apply Advanced Techniques in Data Science
Finally, strive to sharpen your skills. Your data science journey will be full of endless learning, but you can complete advanced courses to ensure you’ve covered all of the bases.

You’ll want to be confident in clustering models with regression, grouping, and k-means. You can also move into machine learning-using scikit-learn to bootstrap models and build neural networks.
Around this stage, Python programming projects may involve building models using live data feeds. This type of machine learning model changes its predictions over time.
Data science is an ever-growing field, spanning many industries.

There are exponential learning opportunities at the pace at which demand is growing. Continue reading, communicating and conversing with others, and over time you are certain to maintain interest and a competitive edge.

How Long it would take to Learn Python?

The most common question you have people ask us after reading those steps is: "How long does all this take? Really, it all depends on your ideal schedule, free time you can devote to learning Python programming and the speed you are learning.

Conclusion:

I hope you reach to a conclusion about Python programming for data science. You can learn more through Python online training.

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