What you will learn
- Understand Python language basics and 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 sci-kit learn
- 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 sci-kit-learn!
About this course
Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
About this course
The first in our Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you'll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.
We'll cover R's functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You'll learn how to apply general programming features like "if-else," and "for loop" commands, and how to wrangle, analyze and visualize data.
Rather than covering every R skill, you might need, you'll build a strong foundation to prepare you for the more in-depth courses later in the series, where we cover concepts like probability, inference, regression, and machine learning. We help you develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.
The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.