Doing Data Science with Python
Course info



Course info



Description
Do you want to become a Data Scientist? If so, this course will equip you with concepts and tools that can bring you to speed and you can utilize the skills acquired in this course to work on any data science project in a standardized approach.
This course, Doing Data Science with Python, follows a pragmatic approach to tackle an end-to-end data science project cycle. You'll learn everything from extracting data from different types of sources, to exposing your machine learning model as API endpoints that can be consumed in a real-world data solution. This course will not only help you to understand various data science related concepts, but also help you to implement the concepts in an industry standard approach by utilizing Python and related libraries.
Course FAQ
Yes! Python's robust libraries are ideal for manipulating data and it is a relatively easy language to learn for data analyst beginners!
Python and R are both great programming languages geared towards data science. However, Python is often easier for beginners, and is a more general purpose language with easy to read syntax. Python is better for raw data scraping, while R is more useful in analyzing already scrubbed data.
Yes. We will go over various standard Python libraries such as NumPy, Scikit-Learn, Pandas, Pickle, Matplotlib, and Flask to help with extracting, cleaning, and processing data, and building machine learning models.
Simply put, it is a combination of statistical and machine learning techniques through the use of Python programming to help analyze and interpret data.
Some previous exposure to Python or its libraries may come in handy, but is not required. Just come with an interest in data science.
Data science is a super popular field these days. Through data science we can find meaningful and valuable insights, and provide data-driven evidence to help organizations be more efficient and successful.