Introduction to Data Analytics and Machine Learning with Python

Program Overview

This online course introduces data analytics and machine learning using Python libraries. It covers topics like data manipulation, visualization, statistical analysis, and machine learning algorithms. The course is suitable for individuals with prior Python experience and aims to provide a foundation for entry-level positions in related fields.

Program Outline

Introduction to Data Analytics and Machine Learning with Python Short Course Analysis

Degree Overview:

Overview:

This online course provides an introduction to machine learning and data analytics using Python libraries for individuals with prior Python experience. The course focuses on building a foundation in data analysis and machine learning, potentially leading to entry-level positions in related fields.

Objectives:

• Understand the key principles of data analysis and machine learning.
• Gain practical experience using Python libraries for data analysis and machine learning.
• Build a portfolio of projects demonstrating data analysis and machine learning skills.

Program Description:

This course covers the following topics:

• Jupyter Notebook: Introduction to the data engineer's preferred IDE.
• NumPy: Exploration of N-dimensional arrays, broadcasting functions, linear algebra abstractions, and random number generators.
• Exploratory data analysis with pandas: Manipulation of data including loading, storing, cleaning, transforming, merging, and reshaping.
• Visualization and plotting with matplotlib: Generating plots, histograms, power spectra, bar charts, error charts, and scatterplots.
• Introduction to SciPy with statistics: Introduction to the scipy.stats package for distributions, fitting distributions, and random numbers.
• Introduction to machine learning concepts with scikit-learn: Training and evaluating learning algorithms, including decision trees, perceptrons, support vector machines, and neural networks.
• Scikit-learn: Delving deeper into data validation, cross-validation, and improving learning algorithm accuracy.

Outline:

Course Materials:

The course includes video recordings explaining complex concepts and tools, encouraging active participation for deeper understanding.

Course Schedule:

The course is offered in three formats:

• Weekly evening classes (10 weeks)
• Saturday classes (5 weeks)
• Summer School (1 week)

Course Content:

• Introduction to data analysis and machine learning
• Python libraries for data analysis and machine learning
• Data manipulation and cleaning
• Data visualization
• Statistical analysis
• Machine learning algorithms
• Project development

Assessment:

• Informal assessment through optional weekly assignments
• Final project applying state-of-the-art techniques to solve a real-world problem using real-world data

Teaching:

• Industry professionals with expertise in data analysis and machine learning
• Small group size for personalized learning
• Certificate upon completion of 70% attendance

Careers:

• Entry-level positions in data analysis or machine learning
• Potential career paths such as data analyst, machine learning engineer, data scientist

Other:

• No prior data analysis or machine learning experience required.
• Basic Python knowledge required (comparable to Introduction to Programming with Python).
• Familiarity with mathematical concepts is essential.
• Strong programming skills in other languages may be transferable, but consultation with the syllabus is recommended.
• Course is not formally accredited.

Note:

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