Data Science for Engineers: Part 2

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Page 2 of 4

Introduces the basics of Numpy, a numerical computing library in Python, covering advantages, memory layout, operations, and linear algebra functions.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Big Data Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Data Science Essentials

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

NumPy Arrays and Graphical Representations: Introduction

Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Air Pollution Data Analysis

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

Data Wrangling with Hadoop: Storage Formats and Hive

Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.

Scopes and Lambdas: Data Science with Python

Covers scopes, lambdas, and pandas in data science with Python, including nested declarations, scoping, assignments, and pandas manipulation.

Introduction to NumPy and Matplotlib for Scientific Computing

Introduces NumPy and Matplotlib, essential tools for scientific computing and data visualization in Python.