In this guide, we'll walk through the 5 phases of your data science journey with Python from the basics of Python to building machine learning algorithms.
Python is one of the most popular coding languages because it is easy to read and has a lot of great open-source libraries for data science. Python also has an active community of users who regularly update and revise documentation, making it an excellent choice for beginners who might need guidance along the way.
In fact, one of Python’s official documents, The Zen of Python, elegantly describes its guiding principles for user-friendly design. With The Zen of Python in mind, we’ll walk through the essential libraries and topics that beginners will need to know to succeed in data science and analytics.
1. Python Programming Basics
First, you’ll want to learn the basics of Python and concepts such as data types, variables, and object-oriented programming. Once a learning environment has been set up, we will work with different data types such as strings, lists, dictionaries, and tuples. Each data type has its own particular purpose and knowing when to use each one will be essential.
2. Control Flow & Loops
Then you’ll learn to use conditional statements and control flow tools. This includes the If/Else Statements, Boolean Operations, and different types of loops. These topics create a large portion of the logic in your code and this course will help you master these concepts.
3. Exploratory Data Analysis
Next, you’ll get into the core of data analysis and the building blocks of data science by learning to import and clean data, conduct exploratory data analysis (EDA) through visualizations, and discuss feature engineering best practices. You’ll want to master popular data manipulation and visualization libraries such as Pandas, NumPy, Matplotlib, and Seaborn to execute these tasks.
Once you know how to clean data and conduct EDA, learn the data science workflows and fundamental statistics behind data science. These topics are critical in ensuring that the data you are using to train your models are not biased. Some of the topics you’ll learn include best practices for segmenting train/test data, dealing with imbalanced data, and most importantly, framing your data science question and developing a hypothesis.
5. Machine Learning
Finally, the last step will be to create predictive models using machine learning tools like scikit-learn. Scikit-learn is an open-source library that has a vast array of supervised and unsupervised learning algorithms. It is a fantastic tool with great documentation that aspiring data scientists must know how to use for modeling data.
Some of Scikit-learn’s most important features include clustering algorithms, dimensionality reduction, ensemble methods, feature extraction and selection, and parameter tuning. Scikit-learn also has a wide assortment of supervised learning algorithms for generalized linear models, classification models, and decision trees.
Data is quickly becoming an inescapable and ubiquitous aspect of life. Learning how to manipulate, visualize, and draw predictions from data using Python will be an invaluable skill. Even though it looks like a daunting challenge, it is a worthwhile task, and to quote line 15 from The Zen of Python, “Now is better than never.” Contact us today to learn more.