A Beginner's Guide to Python for Data Science

Python is the high-level programming language that has become a popular choice for many Data Scientists and Data Engineers who appreciate its versatility. It has a growing number of dedicated libraries and resources for advanced data science applications.

Python programming is one of the most valuable skills required for a data science career. And since Python is versatile, your skills would be more transferrable to other disciplines, like application development, machine learning, and security.

Data Scientists find Python faster than R and good for data science. With all the powerful libraries and toolsets, it's known for building up productivity to carry out a wide range of analytical tasks, including data exploration, manipulation, preprocessing, analysis, cleaning, predictions, visualization, and preservation.

Python is neat, readable, well-structured, and easy to learn. That's part of why the learning curve is mild. With a vast amount of resources and growing data science communities across the globe, learning Python for data science or machine learning is as attainable as ever.

There are great opportunities to learn Python, and volumes of high-quality learning resources from educators according to the difficulty level and focus area. Some learners complete online courses at lightning speed, and others take time to learn. It all depends on your background and the desired timeline, but it usually takes three months to a year with steady practice.

The employment opportunities for Python professionals are also abundant, and your Python programming skills are valuable and easily applied in other disciplines. We use Python in everything from machine learning to build websites to software testing. So, the future is bright for Python professionals, and if you dedicate time to learning, you have the potential to become a Data Scientist.

Data Science is an ever-growing field, and Python is the top programming language of choice for Data Scientists, Data Engineers, and Machine Learning Engineers.

Python is used at virtually every step along the way in the data science process. It takes straining towards the goal and a lot of coding practice to gain a Pythonic approach and data fluency. We’ll show you how you can bring your career to a new level in simple steps with devotion.

Learn Python Basics

It’s appealing to get carried away enrolling for courses like Machine Learning, TensorFlow, and Computer Vision. The first step (and serious decision) is to learn basics in a course setting through learning platforms, official guides, or introductory books.

You’ll need an Introduction to Data Science in Python, and the basics should help you:

• Build a solid foundation in Python
• Work with different data types
• Learn to work with variables, process numerical data, and text data
• Creating and manipulate lists

Learn the basics of Python syntax, creating and using functions, loading modules, and importing packages to use the pre-written code from other programmers.

There are hundreds of learning resources online, but most of them are over promising without help. The most efficient way to learn the basics is to play and to practice with coding projects. Some educators offer a hands-on learning environment so that you'll learn to write actual code from your very first lesson.

These following recommendations (learning paths) cover important Python topics and provide extensive learner support in the context of data science to help you build hands-on data skills with interactive exercises and projects.

• Data Scientist in Python  ← Dataquest
• Data Scientist with Python ← DataCamp
• Become a Data Scientist ← Udacity

It’s also going to be easy to get frustrated. You will need a lot of practice to master a Pythonic approach to develop a simple style to write exceptionally robust and well-performing code, even as a beginner. So in the following steps, we provide practical tips to help you maintain interest, motivate your learning experience, and use this article as a guide to your data journey with a practical approach.

Learn Statistics with Python

Data scientists are in immense demand and, in most cases, are taking over the legacy statistician roles. Statistics keep us rooted and grounded to describe, summarize, and communicate the results. It involves hypothesizing information from vast stores of statistics, registers, and data. This data is mostly unsorted and difficult to correlate with any meaningful accuracy. So, Machine learning can help also us to make connections between dissimilar data sets but requires serious computational sophistry and power.

Statistics is an indispensable tool for data scientists and is critical for effective data science.

Competency in statistics will enable you to mine the large data sets for helpful information and lead you to a rewarding career in a wide range of industries. Without statistics, your Python skills are of no use in Data Science.

You need to build a firm foundation in statistics to use the following Statistical libraries for the data work in Python:

• Python's statistics—A built-in Python library for descriptive statistics.
• NumPy—library that makes mathematical and statistical operations easier.
• SciPy—The most powerful library for scientific computing
• pandas—Python library for numerical computing to work with the data.
• Matplotlib–A data visualization library that exquisitely works with NumPy, SciPy, and Pandas.

If you are learning intermediate topics in Python and struggling through a course that’s teaching you to work on the Statistical Libraries, it’s going to be easy to get baffled and quit. This can feel really demotivating, but you should know that it is easier to learn statistics. You don't need advanced statistics, but enough to interpret the results.

The self-starter way to learning statistics is to learn by doing. Here are a few of the best resources we’ve found for learning statistics.They can help you build an excellent knowledge of the important concepts, including statistical formulas, interpretation, and communicating results.

• Statistics Fundamentals (Dataquest)
• Introduction to Statistics in Python (DataCamp)
• Introduction to Computational Statistics for Data Scientists Specialization (Databricks)

It's always good to remember—just because statistics is easier to learn doesn’t mean you won’t have to put in serious efforts. You need to develop cognitive reasoning for decision-making and learn Bayesian thinking as well. This might sound intimidating, but with daily practice and a solid routine, you'll be amazed at how swiftly you’ll build data literacy.

MySQL and Python for Data Analysis

MySQL is a rich database management system known for its performance, readability, and security. It is the market leader in SQL solutions because it is open-source like Python with an aging community support. Some familiar names that use MySQL include Google, Uber, Netflix, Uber, Twitter, and more.

Python needs a MySQL driver, "MySQL Connector," to access the MySQL database. It is a powerful library that allows the conversion between Python and MySQL data types. This allows you to extract the data in the form of an SQL database and load the database into Python before pre-processing and analyzing it. We can also compress the data stream between Python and MySQL database server using protocol compression.

The generic workflow of a Python program that interacts with a MySQL database looks like this:

• Connect to the MySQL server
• Create a new database
• Connect to the database (new or existing)
• Execute the SQL query and fetch the results in Python
• Update database changes programmatically
• Close the connection to the MySQL server

Learn how to use MySQL with Python from basics to advance, including all necessary functions and queries. And as you build programming skills, learn to load database tables with the help of MySQL Connector, convert the tables into Pandas data frames to perform data manipulation, create databases, and write to them. Most importantly, learn to automate the task of database interaction in Python.

This will allow you to use your skills in data science projects. You should be well-versed with MySQL and statistics to demonstrate data analysis skills using Python on your data science portfolio.

Build a Data Science Portfolio (while learning)

In this section, we'll briefly discuss how to build a winning data science portfolio, even with no prior job experience. Your portfolio is one of the most important aspects of job applications in a way that’s complicated to do with a resume alone.

For every data scientist, a portfolio is not an option. Your portfolio shows your skills and validates your knowledge to recruiters, hiring managers, and potential clients. The easier it is for people to find your work, the easier it will become for you to get an internship and a job.

While you show experience and list skills on your resume, it is your portfolio that validates your skills. The portfolio is a compilation of data science projects you’ve worked on. The first step in building a data analytics portfolio is choosing where to host it.

When you’re just getting started, consider commonly used portfolio options:

• GitHub–You should have a few projects up on GitHub or make a blog using GitHub Pages to showcase your projects. You should keep data projects to a public repository, where your code is visible, including the elements like Jupyter Notebook. Your projects should be well-documented, with a README file both explaining how to set it up, and defining the required process.
• Kaggle–It is a great place for data science learners. You can join the data science competitions and display results. You can also showcase any data sets you’ve built or code you’ve written.
• LinkedIn–You can connect a rich community of experts who are eager to help you learn Python and data science on LinkedIn. It is easy to add, update, and remove projects from your LinkedIn profile. You can share projects in groups and add links under the Featured, Experience, or Education sections.

As you learn new skills and engage with the community, your portfolio will grow as well, and you may want to build a standalone website to expand out. You can host always your site through Medium, WordPress or Ghost. But the contents of your portfolio are important than where you choose to host it.

A simple portfolio should include at least two sections, an “About me” section, and data science projects. We have written a comprehensive guide on how to build a strong data science portfolio to get hired.

Join a community

One of the best ways every new programmer grows is by learning from peers. It has never been more necessary to be part of the Python communities for learners to improve skills, or simply to stay motivated in these strange times with beguiling ideas all over.

Surround yourself with like-minded people to increase your interest in programming and in data science. Python has an enormous community that spans many fields, including Data Science, Machine Learning, Deep learning and Ai.

• Stack Overflow: You can ask questions and receive answers, guidance and help from professional and enthusiast Python programmers.
• Kaggle: It is one of the best data science communities for beginners with over 3 million active users, 50,000 public datasets and 400,000 public notebooks to help solve the complex challenges.
• IBM Data Science Community: It is one of the best source for expert-level insights and we can find well-known Data Scientists blogging, podcasting, and even answering questions.
• Reddit: There are great resources (and discussions) for Python programmers and data science learners on several key subreddits, such as r/python, r/learnpython, r/learndatascience, and r/datascience.
• GitHub: It is great for collaboration, 'carefully' making revisions to projects and being able to track and rollback changes.
• Meetups: You can connect with people at meetups, work with them and also find opportunities to contribute to open-source packages.

Whether you’re just getting started learning Python or building new skills (in statistics or SQL), making connections is essential for every aspiring data scientist. You only learn to run if you learn to walk, and you walk by connecting, observing, and making contributions.

Your Guide to Data Science with Python

Decisively, purpose to hone your skills. Data science journey is full of continuous learning, and there are skill tracks and specializations you can take to ensure you’ve covered all the bases.

You’ll want to be comfortable with mathematics and k-means clustering models. You can also step into deep learning—bootstrapping models and creating neural networks.

If you’re looking for a way to build data science skills, gain experience, and work on real-world projects for your portfolio, consider the following resources to supercharge your learning.

• Basic Statistics for Data Science – Beginner's Study Guide
• Data Science: Statistics with Python Courses
• Basic Math for Data Science
• Best Python for Data Science Courses
• Master SQL For Data Science (Python)
• Learn TensorFlow for Data Science
• How to Build a Data Science Portfolio
• 7 Data Analyst Skills
• 7 Data Scientist Skills
• MLOps – Machine Learning Engineer Certification Programs

TL;DR

Now that you have a better understanding of how to jumpstart your data science career with Python. Are you ready to start learning? Visit Brainstation's Data Science Boot Camp to discover how you can gain the fundamentals in a demanding industry.

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