What you will learn:
- Apply various Data Science and Machine Learning skills, techniques, and tools to complete a project and publish a report.
- Practice with various tools used by Data Scientists and become experienced in using some of them like Jupyter notebooks.
- Master the key steps involved in tackling a data science problem and learn to follow a methodology to think and work like a Data Scientist.
- Write SQL to query databases and explore relational database concepts.
- Understand Python and practice Python programming using Jupyter.
- Import and clean data sets, analyze data, build and evaluate data models and pipelines using Python.
- Utilize several data visualization tools, techniques and libraries in Python to present data visually.
- Understand and apply various supervised and unsupervised Machine Learning models and algorithms to address real world challenges using Python.
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 [...]
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!
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. [...]