Five Technologies That Can Improve Project Management

In today’s complex, technologically advanced business environment, project managers have a distinct advantage. In the past five years alone, there has been a solid increase in technologies that were built to enhance and reorganize project development with greater synchronicity, making a project manager’s job simpler, more intuitive and more easily adaptable to the ever-changing technological landscape. Here are just a few instances of how technology used in project management is changing the game for the better.

Collaboration Tools

When it comes to newer types of project management technology, there can be no underestimating the effect of collaboration tools and project-based chat platforms. Recent innovations in social platforms reflect the growing need for team members who may not be working in the same space to keep updated in real time. According to Forbes, one in five Americans works from home. As this trend grows and becomes the more practical standard for larger businesses, chat software and social tools are becoming one of the most crucial types of technology used in project management.

Project Tracking

As businesses gravitate more and more towards cloud-based sharing and project management technology, project managers are presented with a number of ways to increase productivity within teams, while also establishing better accountability for team members. Recent research has shown that using cloud-based project management technology can increase productivity and focus among team members.

For this reason, more and more businesses are finding it crucial to use cloud-based project management software like Clarizen for collaborative projects. The closer you can track the progress and targets of a project the quicker you will be able to identify potential roadblocks and issues.

Information-Gathering Tools

When it comes to technology used in project management, there’s no underestimating the effect of sensors and other information-gathering tools that can provide project managers with a specific type of informed, accurate market research within a short timeframe. Creating an accurate, wide-ranging census of data is crucial to many research-based projects.

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In addition to cloud-based software that tracks team progress and updates team members in real time, having technology that is constantly updating and adapting itself to the changing market is an essential component of team-based research and development. When data is always subject to change, it’s of the utmost importance that data-based projects don’t get caught with ineffective or outdated input.

Scheduling Software

Many teams today are working across cities, countries and time zones to accomplish a specific time-sensitive goal. Because of this, the importance of scheduling software to successful project management cannot be underestimated. With the growing number of decentralized workforces in the U.K. alone, making sure that each team member is on the same page with group meetings, calls and deadlines, can mean the difference between a successful project outcome and a disastrous mess.

Workflow Automation

 Developing your own workflow has its own set of challenges, which is why workflow automation tools are so important. With workflow and reporting automation, you can free members of your team from ongoing administrative tasks so they can focus on what’s really important. In doing so, they can respond faster to important queries and complete the task at hand. When seeking out workflow automation tools, be sure to adopt a flexible system that can quickly adapt to changing market conditions.

Just as the cloud allows businesses to update and communicate in real time, modern scheduling software benefits from being built into certain social and chat platforms. This kind of technology is vital when it comes to team accountability, project progress tracking and advanced, team-wide communication.

 There’s a lot at stake for 2021. The question is whether mainstream technologies such as data analytics and AI will remain a force. Or will we see newer forms of technology dominating the year? Even if time is the sole judge and determinant, here are possible technology trends we believe will dominate 2021:

1.      5G Will Go Mainstream

All that talk and hype around 5G will become real in 2021. The need for a reliable and fast connection became urgent only a few months earlier. When digital collaboration, remote work, and videoconferencing became a part of our lives, we had to get ways to boost speed.

It was already clear enough for telecommunication companies that 5G was the way to go. The deployment of this technology will be important for different tools like IoT. About 51% of companies using IoT have noted an improved insight into customer needs, behaviors, and preferences. The value of 5G, therefore, will skyrocket in 2021.

2.      Customer Data Platforms (CDP)

In the last few months, we have witnessed a rise in customer data platforms (CDP). It’s not easy to organize fragmented data from multiple sources. To operate efficiently, you’ll need well-curated and timely operations.

In a recent study, an estimated $3 trillion goes down the drain annually due to bad data. Therefore, it’s quite imperative that you address this problem early as a company. Luckily, CDPs help to solve this problem by collecting data from all sources. They then organize it, tag it, and make it usable.

3.      Internet of Behaviors(IoB)

IoB is an upcoming trend that you’ll hear more of in 2021. Companies and businesses are taking advantage of technology to monitor consumer and customer behaviors. Some of the effective technological tools here include location tracking, big data, and facial recognition.

According to a prediction by Gartner, more than half of the global population will be under an IoB tool by 2025.

Interesting Read: Top 20 Mobile App Development Trends to Look for in 2021

4.      Cybersecurity Mesh

With the cybersecurity mesh, you can access any digital security asset – no matter its location. The benefit of this technology is that it allows people to place the security wall around individuals rather than the entire organization.

The sudden rise in remote workforces and cloud technology has affected the security of company assets outside the company’s perimeter. Thanks to the help of the cybersecurity mesh, the security perimeter goes beyond and covers individuals working remotely.

5.      Total Experience

Often abbreviated as TX, the total experience goes side by side with the customer experience, user experience, and employee experience. As most of the interactions today are virtual, distributed, and mobile, it’s important to use the TX strategy even more.

Already, the COVID-19 pandemic has forced a new norm in collaborations. And that’s why you need to bring together the customers and employees.

6.      Intelligent Composable Business

This trend focuses on taking advantage of packaged business capabilities. You can develop it through vendors or do it in-house. ICB helps to bring different things together, including better access to changing data, better decision making, and application delivery.

7.      Hybrid Cloud

Most businesses are moving towards the hybrid cloud model. The reason is simple – the hybrid cloud is more convenient. It helps businesses to strike a balance for their distinct cloud infrastructural needs.

Most of the giant public cloud providers have started focusing on the hybrid cloud. Some of them include Amazon Web Services(AWS), Google Cloud, Azure, Oracle, and IBM. All of this aims at dealing with different customer challenges including:

• Privacy
• Security and compliance.
• Exponential data growth.

8.      Privacy and Confidential Computing

It involves the encryption of the entire computing process and not only the data. This creates extra layers of security that help to protect sensitive information. We should expect more of privacy and confidential computing in action come 2021.

9.      Smart Work from Home Technologies

The coronavirus pandemic has shifted the way we do things and carry out business. Initially, it would be radical to allow employees to work from home on a large scale. Even when employees were asking for enhanced work flexibility, it was never to be.

The current lockdowns have led to the common acceptance of the work from home formulas. And many pundits argue that this work-from-home policy will outlast the pandemic. We have seen many tech giants extending the work from home flexibility to their staff. Some like Twitter have even given their employees the option of working from home entirely for the rest of their working careers.

Part of what made all this a reality is the deployment of smart work from home tools such as WebEx, Zoom, and Microsoft Teams. Such apps have witnessed massive usage over the past few months, and this is expected to continue even in 2021.

10. Artificial Intelligence

AI has continued to improve operations for many companies and businesses. The pandemic even made its usage more common. AI, data, and machine learning have played an unmatched role during this coronavirus period.

We have seen how AI has helped in the suggestions customers get when shopping on Amazon, or even when they are watching movies on Netflix. More companies will benefit from AI since the cloud continues to enable access to the rising computing power, software, and frameworks. It’s estimated that 45% of companies using artificial intelligence have increased their total spend per customer, and their ASPs.

11. Hyperautomation

Robotic process automation(RPA) was a major technology tool that companies have had their focus on. Now, it has moved from task-based automation to process-based automation. The magnitude of automation is set to go even higher as Hyperautomation increases in the coming year.

12. Distributed Cloud

This involves the spreading of public cloud services to different physical locations. However, the evolution, governance, and operations of the services remain at the public cloud provider. By 2025, Gartner predicts that most cloud service providers will provide a number of distributed cloud services.

13. Device Form Factors

The current customer needs include small, light, and effective devices. Therefore, manufacturers are heading to the call by providing hybrid devices that double up as phones and tablets. For instance, the Samsung Galaxy Fold 2 does this quite well. 2021 will see a return of stronger and more efficient folding and unfolding devices.

14. Quantum Computing

Quantum computing has helped in the management of COVID-19 in terms of managing the spread, looking for possible vaccines, and the development of therapeutics. As people continue to realize the power of quantum computing, we are more likely to witness its application in many industries in 2021 and beyond.

15. Anywhere Operations

This IT operating model supports customers everywhere. It also enables employees anywhere in addition to enabling the deployment of business services across distributed infrastructure.

 

Difference between Artificial intelligence and Machine learning

Artificial intelligence and machine learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.

Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two different terms in various cases.

On a broad level, we can differentiate both AI and ML as:

AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.

Below are some main differences between AI and machine learning along with the overview of Artificial intelligence and machine learning.

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Artificial Intelligence

Artificial intelligence is a field of computer science which makes a computer system that can mimic human intelligence. It is comprised of two words "Artificial" and "intelligence", which means "a human-made thinking power." Hence we can define it as,

Artificial intelligence is a technology using which we can create intelligent systems that can simulate human intelligence.

The Artificial intelligence system does not require to be pre-programmed, instead of that, they use such algorithms which can work with their own intelligence. It involves machine learning algorithms such as Reinforcement learning algorithm and deep learning neural networks. AI is being used in multiple places such as Siri, Google?s AlphaGo, AI in Chess playing, etc.

Based on capabilities, AI can be classified into three types:

• Weak AI
• General AI
• Strong AI

Currently, we are working with weak AI and general AI. The future of AI is Strong AI for which it is said that it will be intelligent than humans.

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Machine learning

Machine learning is about extracting knowledge from the data. It can be defined as,

Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.

Machine learning enables a computer system to make predictions or take some decisions using historical data without being explicitly programmed. Machine learning uses a massive amount of structured and semi-structured data so that a machine learning model can generate accurate result or give predictions based on that data.

Machine learning works on algorithm which learn by it?s own using historical data. It works only for specific domains such as if we are creating a machine learning model to detect pictures of dogs, it will only give result for dog images, but if we provide a new data like cat image then it will become unresponsive. Machine learning is being used in various places such as for online recommender system, for Google search algorithms, Email spam filter, Facebook Auto friend tagging suggestion, etc.

It can be divided into three types:

• Supervised learning
• Reinforcement learning
• Unsupervised learning

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Key differences between Artificial Intelligence (AI) and Machine learning (ML):

Artificial Intelligence	Machine learning
Artificial intelligence is a technology which enables a machine to simulate human behavior.	Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.
The goal of AI is to make a smart computer system like humans to solve complex problems.	The goal of ML is to allow machines to learn from data so that they can give accurate output.
In AI, we make intelligent systems to perform any task like a human.	In ML, we teach machines with data to perform a particular task and give an accurate result.
Machine learning and deep learning are the two main subsets of AI.	Deep learning is a main subset of machine learning.
AI has a very wide range of scope.	Machine learning has a limited scope.
AI is working to create an intelligent system which can perform various complex tasks.	Machine learning is working to create machines that can perform only those specific tasks for which they are trained.
AI system is concerned about maximizing the chances of success.	Machine learning is mainly concerned about accuracy and patterns.
The main applications of AI are Siri, customer support using catboats, Expert System, Online game playing, intelligent humanoid robot, etc.	The main applications of machine learning are Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc.
On the basis of capabilities, AI can be divided into three types, which are, Weak AI, General AI, and Strong AI.	Machine learning can also be divided into mainly three types that are Supervised learning, Unsupervised learning, and Reinforcement learning.
It includes learning, reasoning, and self-correction.	It includes learning and self-correction when introduced with new data.
AI completely deals with Structured, semi-structured, and unstructured data.	Machine learning deals with Structured and semi-structured data.

PYTHON END TO END FUNCTIONS ON RANDOM NUMBERS

PYTHON END TO END FUNCTIONS ON RANDOM NUMBERS

Random Module:

The Python random module functions depend on a pseudo-random number generator function random(), which generates the float number between 0.0 and 1.0.

There are different types of functions used in a random module . Lets get into them.

1)random.random(): Generates a random float number between 0.0 to 1.0. The function doesn't need any arguments.

In [1]:

import random

print ("A random number between 0 and 1 is : ", end="")

print (random.random())

A random number between 0 and 1 is : 0.7019698935234014

This function generates a new random number each time you run it. If you want the same random number to get generated each time you the code ,then you can seed them.

2)random.seed():The seed() method is used to initialize the random number generator. The random number generator needs a number to start with (a seed value), to be able to generate a random number.If you use the same seed value twice you will get the same random number twice.

In [2]:

print("Random number intializing a seed value : ")

random.seed(10)

print(random.random())

 

random.seed(10)

print(random.random())

 

print("Random number without intializing a seed value : ")

print(random.random()946899135

Random number without intializing a seed value :

0.4288890546751146

3)random.randint():The randint() method returns an integer number selected element from the specified range.This method is an alias for randrange(start, stop+1).

In [3]:

rn=random.randint(1,10)

print("The random integer between the specified range is : ",rn)

The random integer between the specified range is :  10

4)random.randrange(): The randrange() method returns a randomly selected element from the specified range.

In [4]:

print ("A random number from range is : ",end="")

print (random.randrange(1,10))

A random number from range is : 1

5)random.choice():This method returns a randomly selected element from the specified sequence.The sequence can be a string, a range, a list, a tuple or any other kind of sequence.

In [5]:

print ("A random number from list is : ",end="")

print (random.choice([1, 4, 8, 10, 3]))

# String manipulation:

x = "WELCOME"

print("Random character from the given string : ",random.choice(x))

A random number from list is : 4

Random character from the given string :  C

6) random.sample(): This method returns a list with a randomly selection of a specified number of items from a sequnce.

In [6]:

mylist = ["apple", "banana", "cherry"]

print("The randomly selected items are:")

print(random.sample(mylist, k=2))

 

string = "PythonProgramming"

print("With string:", random.sample(string, 4))

 

# Prints list of random items of length 4 from the given tuple.

tuple1 = ("ankit", "geeks", "computer", "science",

                   "portal", "scientist", "btech")

print("With tuple:", random.sample(tuple1, 4))

 

#Prints list of random items of length 3 from the given set.

set1 = {"a", "b", "c", "d", "e"}

print("With set:", random.sample(set1, 3))

The randomly selected items are:

7) random.shuffle(): This method takes a sequence (list, string, or tuple) and reorganize the order of the items.

In [7]:

li = [1, 4, 5, 10, 2]

print("The list after shuffling :")

random.shuffle(li)

print(li)

 

mylist = ["apple", "banana", "cherry"]

random.shuffle(mylist)

print("mylist after shuffling : ")

print(mylist)

 

from random import shuffle

x = [i for i in range(10)]

random.shuffle(x)

print("x :",x)

, 7]

8) random.uniform(): This method returns a random floating number between the two specified numbers (both included).

In [8]:

print(random.uniform(20, 60))

44.53307282186836

Random numbers using numpy

1D Array :

In [9]:

import numpy as np

  

# 1D Array

array = np.random.randn(5)

print("1D Array filled with random values : \n", array)

1D Array filled with random values :

 0.79780742 -1.49681385]

2D Array

np.random.randn(no. of rows,no. of columns)

In [10]:

array = np.random.randn(3, 4)

print("2D Array filled with random values : \n", array);

1.08453488 -1.21153574 -0.0549759 ]]

3D Array

np.random.randn(no.of matrix,no. of rows,no. of columns)

In [11]:

array = np.random.randn(2, 2 ,2)

print("3D Array filled with random values : \n", array);

3D Arra

]]]

Discrete random variables using Scipy:

In [12]:

from scipy.stats import rv_discrete

values = [10, 20, 30]

probabilities = [0.2, 0.5, 0.3]

distrib = rv_discrete(values=(values, probabilities))

distrib.rvs(size=10)

Out[12]: