What you will learn:
- Understand Python language basics and how they apply to data science.
- Practice iterative data science using Jupyter notebooks on IBM Cloud.
- Analyze data using Python libraries like pandas and numpy.
- Create stunning data visualizations with matplotlib, folium, and seaborn.
- Build machine learning models using scipy and scikitlearn.
- Demonstrate proficiency in solving real life data science problems.
In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn!
Data visualization is the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general. "A picture is worth a thousand words." We are all familiar with this expression. It especially applies when trying to explain the insights obtained from the [...]
This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you'll be ready to create your first Python scripts on your own! Kickstart your learning of Python for data science, as well as programming in general with this introduction to Python course. [...]
Create a project that you can use to showcase your Data Science skills to prospective employers. Apply various data science and machine learning techniques to analyze and visualize a data set involving a real life business scenario and build a predictive model. Now that you've taken several courses on [...]
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.