Edureka Fan

Edureka Fan

1615297697

Data Security and End Point Security Tutorial For Beginners

This Edureka “Data Security and End Point Security” video will give you an introduction to Data Security in Cybersecurity.

#security #developer

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Data Security and End Point Security Tutorial For Beginners
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Jeromy  Lowe

Jeromy Lowe

1599097440

Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials

Data Loss Prevention: What is it, and Do You Need it?

Data Loss Prevention is a set of tools and practices geared towards protecting your data from loss and leak. Even though the name has only the loss part, in actuality, it’s as much about the leak protection as it is about the loss protection. Basically, DLP, as a notion, encompasses all the security practices around protecting your company data.

Every company, even if never vocalized it, has or should have at least some DLP practices in place. You obviously use identity and access management that include authenticating users; you also for sure use some endpoint protection for users’ computers. Maybe (and hopefully) you do beyond that. And this all can be called data loss prevention.

#data-protection #cybersecurity #data-backup #data-security #data-breach #personal-data-security #data #cyber-security

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Sival Alethea

Sival Alethea

1624305600

Learn Data Science Tutorial - Full Course for Beginners. DO NOT MISS!!!

Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You’ll learn the important elements of data science. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.
⭐️ Course Contents ⭐️
⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science

  • Welcome (1.1)
  • Demand for Data Science (2.1)
  • The Data Science Venn Diagram (2.2)
  • The Data Science Pathway (2.3)
  • Roles in Data Science (2.4)
  • Teams in Data Science (2.5)
  • Big Data (3.1)
  • Coding (3.2)
  • Statistics (3.3)
  • Business Intelligence (3.4)
  • Do No Harm (4.1)
  • Methods Overview (5.1)
  • Sourcing Overview (5.2)
  • Coding Overview (5.3)
  • Math Overview (5.4)
  • Statistics Overview (5.5)
  • Machine Learning Overview (5.6)
  • Interpretability (6.1)
  • Actionable Insights (6.2)
  • Presentation Graphics (6.3)
  • Reproducible Research (6.4)
  • Next Steps (7.1)

⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)

  • Welcome (1.1)
  • Metrics (2.1)
  • Accuracy (2.2)
  • Social Context of Measurement (2.3)
  • Existing Data (3.1)
  • APIs (3.2)
  • Scraping (3.3)
  • New Data (4.1)
  • Interviews (4.2)
  • Surveys (4.3)
  • Card Sorting (4.4)
  • Lab Experiments (4.5)
  • A/B Testing (4.6)
  • Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)

  • Welcome (1.1)
  • Spreadsheets (2.1)
  • Tableau Public (2.2)
  • SPSS (2.3)
  • JASP (2.4)
  • Other Software (2.5)
  • HTML (3.1)
  • XML (3.2)
  • JSON (3.3)
  • R (4.1)
  • Python (4.2)
  • SQL (4.3)
  • C, C++, & Java (4.4)
  • Bash (4.5)
  • Regex (5.1)
  • Next Steps (6.1)

⌨️ Part 4: Mathematics (4:01:09)

  • Welcome (1.1)
  • Elementary Algebra (2.1)
  • Linear Algebra (2.2)
  • Systems of Linear Equations (2.3)
  • Calculus (2.4)
  • Calculus & Optimization (2.5)
  • Big O (3.1)
  • Probability (3.2)

⌨️ Part 5: Statistics (4:44:03)

  • Welcome (1.1)
  • Exploration Overview (2.1)
  • Exploratory Graphics (2.2)
  • Exploratory Statistics (2.3)
  • Descriptive Statistics (2.4)
  • Inferential Statistics (3.1)
  • Hypothesis Testing (3.2)
  • Estimation (3.3)
  • Estimators (4.1)
  • Measures of Fit (4.2)
  • Feature Selection (4.3)
  • Problems in Modeling (4.4)
  • Model Validation (4.5)
  • DIY (4.6)
  • Next Step (5.1)

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=ua-CiDNNj30&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=7
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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#data science #learn data science #learn data science tutorial #beginners #learn data science tutorial - full course for beginners