Overview

This specialization focuses on equipping data scientists and analysts with the skills to preprocess data, handle big data technologies, and implement deep learning models. Learners will gain hands-on experience in data preparation, analysis, and building neural networks for predictive analytics. Industry-relevant tools and techniques will be covered to ensure practical application in real-world scenarios.

Syllabus

Course 1: Data Preparation and Analysis
Course 2: Big Data Technologies
Course 3: Deep Learning

Courses

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3 days 7 hours 41 minutes
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This course introduces the necessary concepts and common techniques for analyzing data. The primary emphasis is on the process of data analysis, including data preparation, descriptive analytics, model training, and result interpretation. The process starts with removing distractions and anomalies, followed by discovering insights, formulating propositions, validating evidence, and finally building professional-grade solutions. Following the process properly, regularly, and transparently brings credibility and increases the impact of the results. This course will cover topics including Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. Besides, this course will review statistical theory, matrix algebra, and computational techniques as necessary. This course prepares students ready for and capable of the data preparation and analysis process. Besides developing Python codes for carrying out the process, students will learn to tune the software tools for the most efficient implementation and optimal performance. At the end of this course, students will have built their inventory of data analysis codes and their confidence in advocating their propositions to the business stakeholders. Required Textbook: This course does not mandate any textbooks because the lecture notes are self-contained. Optional Materials: A Practitioner's Guide to Machine Learning (abbreviated PGML for Reading) Software Requirements: Python version 3.11 or above with the latest compatible versions of NumPy, SciPy, Pandas, Scikit-learn, and Statsmodels libraries. To succeed in this course, learners should possess a basic knowledge of linear algebra and statistics, basic set theory and probability theory, and have basic Python and SQL skills. A few courses that can help equip you with the database knowledge needed for this course are: Introduction to Relational Databases, Relational Database Design, and Relational Database Implementation and Applications.
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2 days 9 hours 8 minutes
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An introduction to the field of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, transformers, generative models, neural network compression and transfer learning. This course will benefit students’ careers as a machine learning engineer or data scientist.
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4 days 1 hour 58 minutes
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Big data is the area of informatics focusing on datasets whose size is beyond the ability of typical database and other software tools to capture, store, analyze and manage. This course provides a rapid immersion into the area of big data and the technologies which have recently emerged to manage it. We start with an introduction to the characteristics of big data and an overview of the associated technology landscape and continue with an in depth exploration of Hadoop, the leading open source framework for big data processing. Here the focus is on the most important Hadoop components such as Hive, Pig, stream processing and Spark as well as architectural patterns for applying these components. We continue with an exploration of the range of specialized (NoSQL) database systems architected to address the challenges of managing large volumes of data. Overall the objective is to develop a sense of how to make sound decisions in the adoption and use of these technologies as well as economically deploy them on modern cloud computing infrastructure.