Data Science

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• What is AI?
• AI Governance / Algorithm Administration
  • Data Governance
    • Benchmarks
    • Data Preprocessing
      • Feature Exploration/Learning
      • Data Quality ...validity, accuracy, cleaning, completeness, consistency, encoding, padding, augmentation, labeling, auto-tagging, normalization, standardization, and imbalanced data
    • Bias and Variances
    • Master Data Management (MDM)
      • Managed Vocabularies
      • Datasets
    • Privacy in Data Science
    • Data Interoperability
    • Excel - Data Analysis
• Visualization
• Hyperparameters
• Evaluation
  • Evaluation - Measures
• Train, Validate, and Test
• Data Science | Wikipedia
• Data science concepts you need to know! Part 1 | Michael Barber - Towards Data Science
• Data Fallacies to Avoid - An Illustrated Collection of Mistakes People Often Make When Analyzing Data - Tom Bransby

Learn Data Science Today - Data Science Tutorial for Beginners 2020! This Data Science Course will give you a Step by Step idea about the Data Science Career, Data science Hands-On Projects, roles & salary offered to a Data Scientist!

What is Data Science? | Introduction to Data Science | Data Science for Beginners | Simplilearn This Data Science tutorial will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles.

Intro to Data Science - Crash Course for Beginners Learn the basic components of Data Science in this crash course for beginners. In this course for beginners, you will learn about: 1. Statistics: we talk about the types of data you'll encounter, types of averages, variance, standard deviation, correlation, and more. 2. Data visualization: we talk about why we need to visualize our data, and the different ways of doing it (1 variable graphs, 2 variable graphs and 3 variable graphs.) 3. Programming: we talk about why programming helps us with data science including the ease of automation and recommended Python libraries for you to get started with data science.

Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn) Learn Data Analysis with Python in this comprehensive tutorial for beginners, with exercises included! Data Analysis has been around for a long time, but up until a few years ago, it was practiced using closed, expensive and limited tools like Excel or Tableau. Python, SQL and other open libraries have changed Data Analysis forever. In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports. Additionally, we've included a thorough Jupyter Notebook tutorial, and a quick Python reference to refresh your programming skills. Check out all Data Science courses from RMOTR: https://rmotr.com

Data Science in 60 Minutes | What Is Data Science | Neural Networks | Great Learning Data science is the most popular domain. All the companies are using the Data Science technique as it helps them to use their data and get insights from them. It has also become the most demanding job of the 21st century. Every organization is looking for candidates with knowledge of data science. Understanding all of this, we have come up with this 'Data Science in 60 Minutes' Tutorial. Data Science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data. It is an interdisciplinary field that allows you to extract knowledge from structured or unstructured data. Data science enables you to translate a business problem into a research project and then translate it back into a practical solution. It also uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data-related problems. It is the future of artificial intelligence. In this tutorial, we have covered all the important topics such as What is Data Science, What are its features, When to use this technique, and much more. Visit Great Learning Academy, to get access to 80+ free courses with 1000+ hours of content on Data Science, Data Analytics, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. https://glacad.me/3duVMLE Get the free Great Learning App for a seamless experience, enrol for free courses and watch them offline by downloading them. https://glacad.me/3cSKlNl

Data Science in 30 Minutes: Predicting Content Demand with Machine Learning Netflix is well-known for its data-driven recommendations that seek to customize the user experience for every subscriber. But data science at Netflix extends far beyond that - from optimizing streaming and content caching to informing decisions about the TV shows and films available on the service. The talk covered work done by Becky and the Content Data Science team at Netflix, which seeks to evaluate where Netflix should spend their next content dollar using machine learning and predictive models. The Data Incubator is a data science education company based in NYC, DC, and SF with both corporate training as well as recruiting services. For data science corporate training, we offer customized, in-house corporate training solutions in data and analytics. For data science hiring, we run a free 8 week fellowship training PhDs to become data scientists. The fellowship selects 2% of its 2000+ quarterly applicants and is free for Fellows. Hiring companies (including EBay, Capital One, Pfizer) pay a recruiting fee only if they successfully hire. You can read about us on Harvard Business Review, VentureBeat, or The Next Web, or read about our alumni at LinkedIn, Palantir or the NYTimes. About the speakers: Dr. Becky Tucker is a Senior Data Scientist at Netflix, a streaming media and entertainment company based in Los Gatos, CA. She holds a PhD in Physics from Caltech. At Netflix, Becky works on models that predict the demand for TV shows and movies. Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners.

Structured, Semi-Structured, and Unstructured

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• What’s The Difference Between Structured, Semi-Structured And Unstructured Data? | Bernard Marr - Forbes
• Difference between Structured, Semi-structured and Unstructured data | Ashish Vishwakarma - GeeksForGeeks

What is Big Data | Big Data Types | Types of Data | Structured Data | Unstructured Data | Semi-Structured Data What is Big Data? https://www.knowledgehut.com/

Analyzing semi-structured data… Like a boss With the increasing adoption of Big Data systems as the de facto standard for data storage, and the proliferation of web and mobile applications, APIs and IoT devices (all of which adopt non-tabular data models), it becomes immensely important for Tableau to enable users to connect to, and visualize data, that are in formats like JSON. In other words, semi-structured data. Join us as we breakdown what semi-structured data are, where and how they're being used, what Tableau does today to connect and use them, and what the handling of semi-structured data looks like in the future for Tableau.

Types of Data Under Big data Lecture By: Mr. Arnab Chakraborty, Tutorials Point India Private Limited

Chandra Bhagavatula: Adding Structure to Unstructured and Semi-structured data Chandra Sekhar Bhagavatula Abstract: In this talk, I will describe two systems designed to extract structured knowledge from unstructured and semi-structured data. First, I'll present an entity linking system for Web tables. Next, I'll talk about a key phrase extraction system that extracts a set of key concepts from a research article. Towards the end of the talk, I will briefly introduce an underlying common problem which connects these two seemingly distinct tasks. I will also present an approach, based on topic modeling, to solve this common underlying problem.

Ground Truth

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• Ground Truth Gold — Intelligent data labeling and annotation | The Hive - Medium
• Ground Truth | SageMaker - Amazon

Ground truth is a term used in various fields to refer to information provided by direct observation (i.e. empirical evidence) as opposed to information provided by inference. "Ground truth" may be seen as a conceptual term relative to the knowledge of the truth concerning a specific question. It is the ideal expected result. Wikipedia

You might have heard the term “ground truth” rolling around the ML/AI space, but what does it mean? Newsflash: Ground truth isn’t true. It’s an ideal expected result (according to the people in charge). In other words, it’s a way to boil down the opinions of project owners by creating a set of examples with output labels that those owners found palatable. It might involve hand-labeling example datapoints or putting sensors “on the ground” (in a curated real-world location) to collect desirable answer data for training your system. What is “Ground Truth” in AI? (A warning.) | Cassie Kozyrkov - Towards Data Science

Accurately Automating Dataset Labelling Using Amazon SageMaker GroundTruth (Level 200) Learn more about AWS Innovate Online Conference at – https://amzn.to/2WEdJhy Successful machine learning models are built on high-quality training datasets. Labeling raw data to get accurate training dataset involves a lot of time and effort, because sophisticated models can require thousands of labeled examples to learn from, before they produce good results. Typically, the task of labeling is distributed across large number of humans, adding significant overheads and costs. In this session, learn how Amazon SageMaker Ground Truth can solve data labeling problems, build highly-accurate training datasets, and achieve automated labeling. Speaker: Will Badr, Senior Technical Account Manager, AWS Enterprise Support, ANZ

Week 6: Ground Truth Ryan Baker discusses ground truth for week 6 of DALMOOC

The What, Where and How of Data Science | Iliya Valchanov