Python for Data Science Bootcamp

In this hands-on course, students will quickly go from learning the fundamentals of Python to analyzing real-world datasets.

Our Python for Data Science Bootcamp is meant to go from the very basics of Python programming to the start of machine learning with Python. In this Bootcamp, you’ll learn how and why Python is used for data science, how to create programs, work with data in Python, create data visualizations, and use statistics to create machine learning models.

Python Fundamentals

The course will start with the fundamentals of Python, including writing basic statements and expressions, creating variables, understanding different data types, working with lists, indexing and slicing lists, using functions and methods, and more. Concepts such as object-oriented programming and IDLE programming are introduced.

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.

Structuring Programs

The second part of the course covers 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. Learn to work with dictionaries, create functions, write for loops to iterate through data, and work with packages in Python.

Arrays & Dataframes

The third part of the course introduces operations and tools for data science. We will learn how to import and clean data using NumPy and Pandas. You’ll learn to work with Pandas dataframes, wrangle data, and get descriptive statistics for your data.

Analyzing & Visualizing Data

You’ll learn to analyze and visualize data with key data science libraries including Pandas, NumPy, and Matplotlib. Learn to filter and clean data, group and pivot data, and start generating insights from your data with exploratory data analysis. Then create visualizations including bar charts, histograms, and advanced visualization for easy interpretation and sharing of your data insights.

Linear Regression

Once we know how to clean our data and conduct EDA, the course will cover data science workflows and fundamental statistics. These topics are critical in ensuring that the data you are using to train your models is not biased. You’ll learn how to use statistics to develop machine learning models. Start building models and evaluating them on your way to machine learning.

Next Steps

After learning all the foundational Python programming and data analysis skills in this Bootcamp, you will be ready to dive fully into machine learning.

Our Python Machine Learning Bootcamp builds off this foundational knowledge to turn you into a full machine learning data scientist. Pick up right where the Python for Data Science Bootcamp left off with advanced statistics and create machine learning models with logistic regressions, k-nearest neighbors, and decision trees.

Learn more about Python for Data Science Bootcamp at Noble Desktop.