Foundations of Data Science

by Avrim Blum, John Hopcroft, and Ravindran Kannan


📄 479 pages


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Your Guide to Latent Dirichlet Allocation

Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. Its uses include Natural Language Processing (NLP) and topic modelling, among others.

In terms of topic modelling, the composites are documents and the parts are words and/or phrases (phrases n words in length are referred to as n-grams).

But you could apply LDA to DNA and nucleotides, pizzas and toppings, molecules and atoms, employees and skills, or keyboards and crumbs.

The probabilistic topic model estimated by LDA consists of two tables (matrices). The first table describes the probability or chance of selecting a particular part when sampling a particular topic (category).

Link

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MIT 6.S191: Introduction to Deep Learning 2021
Created by MIT
⏰ 29 hours worth of material
🎬 43 Video lessons
👨‍🏫 Teacher: Alexander Amini
🔗 Course link

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Data Science Ethics (Login Required)

Utilize the framework provided in the course to analyze concerns related to data science ethics.
Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency.
Examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems.
Learn best practices for responsible data management.
Gain an understanding of the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten."

🎬 video lessons
Rating⭐️: 4.1 out 5
🏃‍♂️ Self paced
Source: University of Michigan

🔗 Course Link

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Introduction to the Data Science Process

Accelerating Deep Learning with GPUs (Login Required)

Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.
You can use accelerated hardware such as Google’s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the Cloud. These chips are specifically designed to support the training of neural networks, as well as the use of trained networks (inference). Accelerated hardware has recently been proven to significantly reduce training time.

🆓 Free Online Course
Rating⭐️: 4.7 out 5
🎬 video lesson
🏃‍♂️ Self paced
Duration ⏰: More than 7 hours worth of material
Source: cognitiveclass
🔗 Course Link


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Data Analysis Masterclass Using Spreadsheet, MS excel and Tableau

This is a Condensed course that will teach you all you need to know about how to get started and get going with data analysis we will start with learning how to use Excel and spreadsheet then use Tableau for Data analysis.

This course is made for anyone who wants to learn the art of Data analysis and data visualization. We will begin by learning how to use Excel and Spreadsheet then we will learn the various steps for doing data analysis which are data cleaning, preparation and finally data visualization after which we will use all of these learned skills to create a real interaction dashboard for our projects in Tableau.

This course should prepare you to utilize Excel, Spreadsheet and Tableau to do your next analysis with confidence. You will be able to proudly showcase your skills to the world and add it to your resume.

🆓 Free Online Course
🎬 video lessons
🏃‍♂️ Self paced
Modules ⏰: 4
Source: Skill Share
🔗 Course Link


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Basics of Pandas for Data Analysis & Data Science

Learn Basics of Pandas for Data Analysis , Data Manipulation , Data Visualisation & Data Science

Rating ⭐️: 4.6 out 5
Students 👨‍🎓 : 2,376
Duration ⏰ : 1hr 55min on-demand video
Created by 👨‍🏫: Shan Singh

🔗 Course Link


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Data Science and Machine Learning

by Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman

📄 533 pages

🔗 Book Link

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INTRODUCTION TO DATA SCIENCE/Machine Learning

In this course, I include easy-to-understand materials covering topics from data mining and machine learning. I try to explain the algorithms and the mathematics behind those algorithms in such a way that the resources do not overwhelm the learners. At the same time, I make sure that each lecture has adequate core content to build a data science foundation. I am still creating content and adding them to this curriculum.

🆓 Free Online Course
🎬 video lessons
🏃‍♂️ Self paced
Modules ⏰: 6
Source: computing4all
🔗 Course Link


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15 Different Types of Data Scientists [with Responsibilities]

A Data Scientist is a person who uses his/her skillset to gather, manage and store data for analysis. This is not easy as it sounds; a single person cannot do handling and engineer data. Proficiency in programming, mathematics, statistics, databases, etc., are some of the basic skills required to pursue different data science types of activities.

Creating various types of data science models is the key responsibility of a data scientist. Financial models, Business models, and Machine learning models are some of the models which are developed by different types of data scientists.

Data science is a broad term; the number of activities involved in data science is classified among professionals called Data Scientists. Let's explore the different types of data scientists along with their responsibilities.

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#Data_Science #Data #Science #datascience_types
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