Type of Training: Remote (Online) / In-Person; Time: Evenings & Weekends

COURSE DESCRIPTION:

Students will learn fundamentals of Data Analysis with Python programming. After completing this course, students will be able to perform related work that requires knowledge of data analysis with Python programming, data manipulation and data visualization. This training is available in-class and remotely (online).

COURSE CONTENT:

1. Statistics Basics – students will review basic concepts and terminology in Statistics.

2. Python Programming Fundamentals – the module includes following topics: Basic Data Types (Basic Data Types, Variables, Operators, Functions and Modules), Compound Data Types (Lists, Strings, Sets, Dictionaries), Flow control (Conditional expressions, Loops, Iterators), Working with files, Working with functions, OOP Concepts, Benefits of Standard Library.

3. Data Analysis and Visualization with Python – module provides solid fundamentals of Data Analysis and Data Visualization using functions and Python Libraries, such as Numpy, Pandas, Seaborn etc.

4. Machine Learning Fundamentals - this module will introduce Machine Learning and Data Mining with Python. Students will learn predictive analytics - supervised and unsupervised learning. The following topics will be covered: Linear regression, Logistic regression, Train/Validation, Data Visualization, Model Performance Evaluation, Classification Trees, Ensemble Learning, Random Forests, Gradient Boosting, Neural Networks, Clustering (K-means), Dimensionality Reduction (PCA), Text mining (NLTK), Association Rule Mining and Basket analysis. Student will be able to build their projects portfolio and post it on GitHub.


*100 instructor-led in-class academic hours

Prerequisites:
Basics of Statistics

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