Data Science and Machine Learning

Harvard University via edX Professional Certificate

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Overview

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Have you thought about a career in data science and machine learning but didn’t know where to start?

Demand for professionals in the machine learning (ML) and artificial intelligence (AI) space is growing exponentially, with no signs of slowing thanks to the ever changing data science landscape. In fact, as the availability of machine learning tools becomes more accessible, companies will begin adopting them at a higher rate – continuing to drive the demand of data science analysts and engineers, especially those with experience in programming languages like Python.

Industries such as finance, health care, e-commerce, and technology will increasingly be reliant on data to drive strategic value and product and service innovation – leveraging data-driven insights to gain competitive advantage – and seeking experts in data analysis and machine learning techniques to meet growth goals.

This comprehensive certificate program is designed to provide learners with the practical knowledge in machine learning and its applications to launch a successful career path or transition into data science and machine learning using Python. The program delves into various facets of data analysis, predictive modeling, and machine learning techniques, providing hands-on experience with industry-standard tools like sklearn, Pandas, matplotlib, and numPy; and in methodologies, including decision trees and ultimately more complex algorithms like gradient boosting.

By the end of this certificate, learners will gain hands-on experience building and analyzing complex data sets using Python and machine learning, developing the skills to enter a robust job market with diverse opportunities.

Learners should have experience in Python and statistics in order to be successful in the course. You may wish to explore CS50’s Introduction to Programming with Python and statistics prerequisites, which can be met via Fat Chance or Stat110 offered through HarvardX.

Syllabus

Courses under this program:
Course 1: Introduction to Data Science with Python

Learn the concepts and techniques that make up the foundation of data science and machine learning.

Course 2: Machine Learning and AI with Python

Learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.

Courses

2 reviews
8 weeks, 3-4 hours a week, 3-4 hours a week
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Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?

Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).

Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.

Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.
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6 weeks, 4-5 hours a week, 4-5 hours a week
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It’s time to make a decision: beach or mountains? When choosing where you want to go for vacation, it can be simple. The options may be a or b. From a decision-making standpoint, it’s easy for the brain to process this decision tree. But, what happens when you’re faced with more complex, multifaceted decisions? You might make a comprehensive pro/con list, rank ordering the most important considerations. But, that can take endless amounts of time that you might not have to spare. When parsing through thousands or millions of data points, you and your organization need to tap into a more sophisticated approach.

The solution? Harnessing the power of artificial intelligence (AI) through machine learning to enhance your decision-making processes. Machine learning with Python can not only help organize data, but machines can also be taught to analyze and learn from disparate data sets – forming hypotheses, creating predictions, and improving decisions.

In Machine Learning and AI with Python, you will explore the most basic algorithm as a basis for your learning and understanding of machine learning: decision trees. Developing your core skills in machine learning will create the foundation for expanding your knowledge into bagging and random forests, and from there into more complex algorithms like gradient boosting.

Using real-world cases and sample data sets, you will examine processes, chart your expectations, review the results, and measure the effectiveness of the machine’s techniques.

Throughout the course, you will witness the evolution of the machine learning models, incorporating additional data and criteria – testing your predictions and analyzing the results along the way to avoid overtraining your data, mitigating overfitting and preventing biased outcomes.

Put your data to work through machine learning with Python.