Python for Data Science
Data Science Immersive. A comprehensive course with an emphasis on the practical application of Python to data analysis.
Data is growing in importance across every industry, and Python has become far-and-away the most popular tool for doing advanced data analysis.
In this hands-on course, students will quickly go from learning the fundamentals of Python to analyzing real-world datasets.
What will students learn?
- How to retrieve data from outside sources and organize data using Python
- Organize data into at least three different tables or equivalent grouping
- algorithms to analyze the data
- Build machine learning API that outputs results of an analysis
- Application and usage of Big Data
- Reuse and simplify code with object-oriented programming
The course begins with an overview of Python and quickly has students building simple applications. Python syntax, including function and module design is considered. File consumption and exception handling are also included early in the class.
This course will cover many unique features that make Python such a popular language, and will go beyond just "the basics." Upon completion of the course, students will gain experience in the full development life cycle using the Python programming language.
Python for Data Science
This course introduces the fundamentals of Python for Data Science. Students will learn basic Python programming and how to use Jupyter Notebooks. Students will be familiarized with popular Python libraries that are used in Data Science, such as Pandas and NumPy
Data Engineering for Data Science
In this course, students will learn about Advanced Excel, data structures, relational databases and ways to retrieve data. Students will learn about the fundamentals of SQL for data querying for structured databases, as well as NoSQL (and MongoDB) for non-relational databases. Furthermore, they’ll learn the basics of HTML, XML and JSON to be able to access data from various sources using APls, and perform Web Scraping.
Probability, Samping & AB Testing
A basic course that introduces the fundamentals of probability theory, where students will learn about probability principles such as combinations and permutations. Students will go on and learn about statistical distributions and how to create samples when distributions are known.
In this course, students will learn how and when regression models can be used to transform data into insights. Students will learn about both linear and logistic regression and the algorithm behind regression models. Students will be able to evaluate the result of regression models and extend them to for interaction effects, and polynomial features.
Machine Learning & Big Data Project
In this course, students will learn how to build and implement the most important machine learning techniques. Students will take their first steps into classification algorithms through supervised learning techniques such as Support Vector Machines and Decision Trees.
Deep Learning & Natural Language Processing
In the final module, students learn how to use regular expressions in Python, and how to manage string values, analyze text and perform sentiment analysis. Additionally, students will get an in-depth overview of deep learning techniques, learning about densely connected neural networks, for high-performing classification performance, convolutional neural networks for image recognition, and recurrent neural networks, for sequence modeling