Businesses of all shapes and sizes live and die on information gathering and processing. Largely because of its structure, Python is far and away the most popular tool for performing advanced data analysis.

In this beginner-level, thirty-hour course, instruction starts with functions, loops, objects, and other foundational programming concepts. The fast-paced course then moves to Excel tabular data, where participants learn to combine and clean data in order to generate useful visualizations and models. All this builds up to data extrapolation, linear regression, and event predictions.

Successful course completion, when coupled with Immersive Machine Learning, paves the way for a Data Science Certificate. The machine learning course covers areas like k-nearest neighbors, models with logistic regressions, and decision trees.

Python Data Science Programming Course Overview 

This course takes participants from Python fundamentals to the beginnings of Python-based machine learning. Some general topics include learning Python’s place in the data science universe, working with data in Python, creating programs and data visualizations, and using statistics to build machine learning models.

Python Fundamentals

These building blocks include basic statements and expressions, data variables and types, lists, indexes, and slicing lists, methods and functions and methods, object-oriented programming, and IDLE programming. Each function has a specific purpose. It is essential to know when and where to use each one.

Structuring Programs

Workflow control tools and conditional statements dominate the next segment of the course. Specific topics include Boolean Logic, If/Else Statements, and different iteration types. Such topics constitute a large portion of your code’s logic. Additional topics include functions, loops to iterate through data, dictionaries, and Python packages.

Dataframes & Arrays

Instruction then progresses to data science tools and operations. Participants learn to clean, sort, and import data using Pandas and NumPy.

Visualizing & Analyzing Data

This course segment focuses on NumPy, Pandas, Matplotlib, and other data science libraries. Once they clean, filter, group, and pivot data, participants learn to generate useful insights using exploratory data analysis. This skill leads to sharp visualizations including histograms, scatter plots, and interpretive visualizations. These platforms make it easier to share your data insights with other stakeholders.

Linear Regression

Participants use their newfound skills to examine fundamental data science workflows and statistics. Proper implementation of these concepts virtually eliminates data bias. Building statistics-based models is also the gateway to machine learning.