An introduction to data science and machine learning with Microsoft Excel
To most people, MS Excel is a spreadsheet application that stores data in
tabular format and performs very basic mathematical operations. But in
reality, Excel is a powerful computation tool that can solve complicated
problems. Excel also has many features that allow you to create machine
learning models directly into your workbooks. While I’ve been using Excel’s
mathematical tools for years, I didn’t come to appreciate its use for learning
and applying data science and machine learning until I picked up Learn Data
Mining Through Excel: A Step-by-Step Approach for Understanding Machine
Learning Methods by Hong Zhou. Learn Data Mining Through Excel takes you
through the basics of machine learning step by step and shows how you can
implement many algorithms using basic Excel functions and a few of the
application’s advanced tools. While Excel will in no way replace Python
machine learning, it is a great window to learn the basics of AI and solve
many basic problems without writing a line of code. Linear regression is a
simple machine learning algorithm that has many uses for analyzing data and
predicting outcomes. Linear regression is especially useful when your data is
neatly arranged in tabular format. Excel has several features that enable you
to create regression models from tabular data in your spreadsheets.
The Four Mistakes That Kill Artificial Intelligence Projects
Humans have a “complexity bias,” or a tendency to look at things we don’t
understand well as complex problems, even when it’s just our own naïveté.
Marketers take advantage of our preference for complexity. Most people would
pay more for an elaborate coffee ritual with specific timing, temperature,
bean grinding and water pH over a pack of instant coffee. Even Apple
advertises its new central processing unit (CPU) as a “16-core neural engine”
instead of a chip and a “retina display” instead of high-definition. It’s not
a keyboard; it’s a “magic keyboard.” It’s not gray; it’s “space gray.”
The same bias applies to artificial intelligence, which has the unfortunate
side effect of leading to overly complex projects. Even the term “artificial
intelligence” is a symptom of complexity bias because it really just means
“optimization” or “minimizing error with a composite function.” There’s
nothing intelligent about it. Many overcomplicate AI projects by thinking that
they need a big, expensive team skilled in data engineering, data modeling,
deployment and a host of tools, from Python to Kubernetes to PyTorch. In
reality, you don’t need any experience in AI or code.
Three reasons why context is key to narrowing your attack surface
Security has become too complex to manage without a contextual understanding
of the infrastructure, all assets and their vulnerabilities. Today’s typical
six-layer enterprise technology stack consists of networking, storage,
physical servers, as well as virtualization, management and application
layers. Tech stacks can involve more than 1.6 billion versions of tech
installations for 300+ products provided by 50+ vendors, per Aberdeen
Research. This sprawl is on top of the 75 security products that an enterprise
leverages on average to secure their network. Now, imagine carrying over this
identical legacy system architecture but with thousands of employees all
shifting to remote work and leveraging cloud-based services at the same time.
Due to security teams implementing new network configurations and security
controls essentially overnight, there is a high potential of new risks being
introduced through misconfiguration. Security teams have more ingress and
egress points to configure, more technologies to secure and more changes to
properly validate. The only way to meaningfully address increased risk while
balancing limited staff and increased business demands is to gain contextual
insight into the exposure of the enterprise environment that enables smarter,
targeted risk reduction.
How to Really Improve Your Business Processes with “Dashboards”
The managers’ success is measured mainly by this task. That is correct in most
cases as he is usually receiving a bonus based on how well the KPIs under his
responsibility perform over a given period. Our goal is to design an
information system to support him with this task. The best way to do that is
to help him answer the main questions related to each step based on the data
we have: Is there a problem? What caused the problem? Which actions
should we take? Were the actions successful? From looking at the
questions above, you can already tell that the dashboard described at the
beginning of this article only helps answer the first question. Most of the
value that an automated analytics solution could have is left out. Let’s have
a look at how a more sophisticated solution could answer these questions. I
took the screenshots from one of the actual dashboards we implemented at my
company. ... The main idea is that a bad result is, in most cases, not caused
by the average. Most of the time, outliers drag down the overall result.
Consequently, showing a top-level KPI without quickly allowing for root cause
analysis leads to ineffective actions as the vast majority of dimension
members is not a problem.
The top 5 open-source RPA frameworks—and how to choose
RPA has the potential to reduce costs by 30% to 50%. It is a smart investment
that can significantly improve the organization's bottom line. It is very
flexible and can handle a wide range of tasks, including process replication
and web scraping. RPA can help predict errors and reduce or eliminate entire
processes. It also helps you stay ahead of the competition by using
intelligent automation. And it can improve the digital customer experience by
creating personalized services. One way to get started with RPA is to use
open-source tools, which have no up-front licensing fees. Below are five
options to consider for your first RPA initiative, with pros and cons of each
one, along with advice on how to choose the right tool for your your company.
... When compared to commercial RPA tools, open source reduces your cost for
software licensing. On the other hand, it may require additional
implementation expense and preparation time, and you'll need to rely on the
open-source community for support and updates. Yes there are trade-offs
between commercial and open souce RPA tools—I'll get to those in a minute. But
when used as an operational component of your RPA implementations, open-source
tools can improve the overall ROI of your enterprise projects. Here's our list
of contenders.
What happens when you open source everything?
If you start giving away the product for free, it’s natural to assume sales
will slow. The opposite happened. (Because, as Ranganathan pointed out, the
product wasn’t the software, but rather the operationalizing of the software.)
“So on the commercial side, we didn’t lose anybody in our pipeline [and] it
increased our adoption like crazy,” he said. I asked Ranganathan to put some
numbers on “crazy.” Well, the company tracks two things closely: creation of
Yugabyte clusters (an indication of adoption) and activity on its community
Slack channel (engagement being an indication of production usage). At the
beginning of 2019, before the company opened up completely, Yugabyte had about
6,000 clusters (and no Slack channel). By the end of 2019, the company had
roughly 64,000 clusters (a 10x boom), with 650 people in the Slack channel.
The Yugabyte team was happy with the results. The company had hoped to see a
4x improvement in cluster growth in 2020. As of mid-December, clusters have
grown to nearly 600,000, and could well get Yugabyte to another 10x growth
year before 2020 closes. As for Slack activity, they’re now at 2,200, with
people asking about use cases, feature requests, and more.
Quote for the day:
"If something is important enough, even if the odds are against you, you should still do it." -- Elon Musk
Simplifying Cybersecurity: It’s All About The Data
The most effective way to secure data is to encrypt it and then only decrypt
it when an authorized entity (person or app) requests access and is
authorized to access it. Data moves between being at rest in storage, in
transit across a network and in use by applications. The first step is to
encrypt data at rest and in motion everywhere, which makes data security
pervasive within the organization. If you do not encrypt your network
traffic inside your “perimeter,” you aren’t fully protecting your data. If
you encrypt your primary storage and then leave secondary storage
unencrypted, you are not fully protecting data. While data is often
encrypted at rest and in transit, rarely is it encrypted while in use by
applications. Any application or cybercriminal with access to the server can
see Social Security numbers, credit card numbers and private healthcare data
by looking at the memory of the server when the application is using it. A
new technology called confidential computing makes it possible to encrypt
data and applications while they are in use. Confidential computing uses
hardware-based trusted execution environments (TEEs) called enclaves to
isolate and secure the CPU and memory used by the code and data from
potentially compromised software, operating systems or other VMs running on
the same server.
Why the US government hack is literally keeping security experts awake at night
One reason the attack is so concerning is because of who may have been
victimized by the spying campaign. At least two US agencies have publicly
confirmed they were compromised: The Department of Commerce and the
Agriculture Department. The Department of Homeland Security's cyber arm
was also compromised, CNN previously reported. But the range of potential
victims is much, much larger, raising the troubling prospect that the US
military, the White House or public health agencies responding to the
pandemic may have been targeted by the foreign spying, too. The Justice
Department, the National Security Agency and even the US Postal Service
have all been cited by security experts as potentially vulnerable. All
federal civilian agencies have been told to review their systems in an
emergency directive by DHS officials. It's only the fifth such directive
to be issued by the Cybersecurity and Infrastructure Security Agency since
it was created in 2015. It isn't just the US government in the crosshairs:
The elite cybersecurity firm FireEye, which itself was a victim of the
attack, said companies across the broader economy were vulnerable to the
spying, too.
Creating the Corporate Future
With the move to the later 20th century, post-industrial age began with
the systems thinking at its core. The initiating dilemma for this change
was that not all problems could be solved by the prevailing world view,
analysis. It is unfortunate to think about the number of MBAs that were
graduated with callus analysis at their core. As enterprise architects
know, when a system is taken apart, it loses its essential properties. A
system is a whole that cannot be understood through analysis. What is
needed instead is a synthesis or the putting together things together. In
sum, analysis focuses on structure whereas synthesis focuses on why things
operate as they do. At the beginning of the industrial age, the
corporation was viewed as a legal mechanism and as a machine. However, in
the post-industrial age, Ackoff suggests a new view of the corporation. He
suggests a view of the corporation as a purposefully system that is part
of more purposeful systems and parts of which, people, have purposes on
their own. Here leaders need to be aware of the interactions of
corporations at the societal, organizational, and individual level. At the
same time, they need to realize how an organizations parts affect the
system and how external systems affect the system.
Microservices vs. Monoliths: An Operational Comparison
There are a number of factors at play when considering complexity: The
complexity of development, and the complexity of running the software. For
the complexity of development the size of the codebase can quickly grow
when building microservice-based software. Multiple source codes are
involved, using different frameworks and even different languages. Since
microservices need to be independent of one another there will often be
code duplication. Also, different services may use different versions of
libraries, as release schedules are not in sync. For the running and
monitoring aspect, the number of affected services is highly relevant. A
monolith only talks to itself. That means it has one potential partner in
its processing flow. A single call in a microservice architecture can hit
multiple services. These can be on different servers or even in different
geographic locations. In a monolith, logging is as simple as viewing a
single log-file. However, for microservices tracking an issue may involve
checking multiple log files. Not only is it necessary to find all the
relevant logs outputs, but also put them together in the correct order.
Microservices use a unique id, or span, for each call.
Quote for the day:
"If something is important enough, even if the odds are against you, you should still do it." -- Elon Musk
