How to Learn Python for Data Science (7 Ways)

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Are you thinking about a career in data science? Python is a great choice! In this article, we’ll tell you what you need to know when looking to learn data science with Python, including how you can get started and what a Coding Dojo course looks like.

What Is Python for Data Science?

Data science is a field that combines statistics, computer science, and business analysis to make sense of large data sets. Data scientists use their skills to build models that can make predictions or recommendations and find trends in data.

Python is an open-source, simple syntax programming language. It’s incredibly versatile for data science, and many data scientists use it to perform various tasks, including data wrangling, analysis, visualization, and machine learning.

Why Learn Data Science With Python?

Python is the most popular programming language across the board, and it can also be considered the language of choice for data scientists – over 66% reported using it daily in 2018. Choosing to learn data science with Python sets you up for success by familiarizing you with the code you’re most likely to use on the job.

Why Is Python Used for Data Science?

Python programming is helpful in data science for countless reasons. Here are just a few:

  • Simple – Python’s syntax is relatively simple and easy to read. It’s beginner friendly, and you can learn it quickly.

  • Valuable – Python has a range of powerful libraries for data science, such as NumPy, pandas, and matplotlib.

  • Versatile – Python programming language is versatile so that data scientists can use it for various tasks, from web development to scientific computing.

  • Open Source – Python is free and open source, meaning anyone can use it, and a large community of developers contributes to its development.

How to Learn Python for Data Science

Learning Python is a great way to initiate yourself into the world of data science, and it’s relatively simple to get started. Here are some suggestions you can use to learn Python basics:

1. Read Python Data Science Books

Although books can be considered a bit old-school, they’re still fantastic resources to familiarize yourself with technical domains such as programming. A well-written, concise book can introduce you to the basics of a topic in an easy-to-understand way and without any distractions.

2. Watch Python Data Science Tutorials

If you prefer learning by watching and doing, there are plenty of excellent Python tutorials, such as those on YouTube and DataCamp. These can be a great way to learn key concepts and practice Python fundamentals by following along with the code.

3. Enroll in a Data Science Bootcamp

If you want to commit to learning data science and are willing to invest both time and money, enroll in a course that teaches Python like Coding Dojo’s Data Science Bootcamp.

Industry experts design these programs to give you the skills and knowledge you need to pursue a career in data science. They’re ideal if you prefer having the support of an instructor or classmates while you’re learning.

4. Work on Python Data Science Projects

One of the best ways to learn anything is by doing. Once you’ve got the basics down, it can be helpful to work on some real-world Python projects to solidify your understanding and gain practical experience. Many resources list exciting data science projects you can work on, such as this one from Kaggle.

5. Get a Python Data Science Certification

While not strictly necessary, some people find that getting a Python certification can be helpful in terms of getting hired or advancing their careers. To get certified, you’ll need to pass an exam by a professional body such as the Institute for Certified Computer Professionals.

6. Apply for a Python Internship

Once you’ve familiarized yourself enough with Python and are ready to further your skills and knowledge through real-world applications, consider applying for an internship. This is an excellent way to get your foot in the door of the data science industry and gain some valuable experience.

7. Join a Python Community

Last but not least, another great way to learn Python for data science is by joining a community of like-minded individuals. You can join plenty of online and offline communities, such as the Python Software Foundation, PyLadies, and Django Girls. These are great places to network, learn from others, and get support while you’re learning.

Learn Python for Data Science at Coding Dojo

Coding Dojo’s immersive Data Science Bootcamp will walk you through becoming a job-ready data scientist. Read more below to find out what you can look forward to upon enrolling.

Data Science Bootcamp Overview

This data science boot camp is a deep dive into the fundamentals of data science and machine learning with Python. Throughout the course, you will understand the entire data science process from end to end, including data prep, data analysis and visualization, and how to properly apply machine learning algorithms to various situations or tasks. You’ll also walk away with a portfolio of projects showcasing your data science certification to prospective employers.

Week 1: Introduction to Python For Data Science

Learn how to use Python and the fundamentals you need for data science.

Week 2: Pandas for Data Manipulation

Learn how to load, clean, and manipulate data using the Python library Pandas. Additionally, learn the strengths and weaknesses of using Python to manipulate data.

Week 3: Exploratory Visualizations

Build visualizations to support exploratory data analysis (EDA).

Week 4: Explanatory Visualizations (Continued)

Use Python to create high-quality graphs to share with stakeholders and communicate key findings.

Week 5: Introduction to Machine Learning

What is machine learning and why use Scikit-Learn for Machine Learning? Topics include types of machine learning and data preprocessing for machine learning.

Week 6: Regression Models

Learn about machine learning algorithms, how to tune them to maximize their performance, and the strengths and weaknesses of each algorithm.

Week 7: Classification Models

Learn about classification metrics, confusion matrices, and how to hypertune classification models.

Week 8: Gradient Boosting Machines

Learn what gradient boosting algorithms are, why they are so performant, and how to get started with Kaggle competitions.

Week 9: Clustering Algorithms

Learn about unsupervised learning and its applications. Learn about clustering algorithms, how to tune them, and the strengths and weaknesses of each.

Week 10: Uses of Dimensionality Reduction

What is dimensionality reduction? Learn how to use it for data visualization, to speed up machine learning algorithms, and to understand data better. Explore Principal Component Analysis (PCA) and feature engineering techniques.

Week 11: Deep Learning Frameworks

Learn about why deep learning has transformed industries, various deep learning frameworks, and when to use deep learning techniques. Topics include sequential artificial networks, and deep learning regularization.

Week 12: Introduction to SQL For Data Science

Learn how to perform SQL queries and use SQLalchemy and SQLite.

Week 13: Introduction to Databases

Learn the advantages of using a relational database. Learn intermediate SQL queries to access and aggregate information.

Week 14: Intro to ETL (Extract Transform Load)

Develop an understanding of the process of extracting, transforming, and loading data.

Week 15: Statistical Analysis

Learn tools for statistical analysis including measures of central tendency, variance and standard deviation and comparing means.

Week 16: Model Assumptions

Explore model assumptions and how to test for them. Apply this knowledge to choose the appropriate model for a data set.

Week 17: Data Visualization – Model Interpretations & Insights

Learn to extract, visualize, and interpret model importance.

Week 18: Data Visualization – Time Series Analysis

Identify, pre-process, and plot time series data with Python. Explore statistics, aggregation, and seasonal trends.

Week 19: Data Visualization – Introduction to Tableau

Transform, explore, and analyze data while creating high-quality visualizations within Tableau.

Week 20: Data Visualization – Create Dashboards in Tableau

Create an interactive data dashboard in Tableau for data storytelling.

Learn Python for Data Science FAQ

Do you still have questions about learning Python for data science? Check out this FAQ for some answers.

Is Python Hard to Learn?

No, Python coding is not hard to learn. However, it is important to keep in mind that there is a difference between being able to use Python and being a Python expert. While Python is relatively easy to pick up, it can be difficult to master.

How Long Does it Take to Learn Python for Data Science?

The amount of time it will take you to learn to code Python will vary based on your specific circumstances, learning style and schedule. It can take an average of four months or more when doing it on your own, and as little as 20 weeks when taking an immersive boot camp like Coding Dojo’s.

Where Can I Learn Python for Data Science?

Python is a relatively easy language to learn, and there are many resources available online for learning Python for data science. Bootcamps like Coding Dojo can provide an immersive learning experience that will help you learn Python programming quickly and effectively.

If you want to learn Python with data science, the best thing you can do for yourself is find the right resources to get started. Coding Dojo is a great place to start your journey into the world of data science. Check out our course offerings today!

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