Why is Data Science Important in 2021?

 

When we talk about data science, it is not about making complicated models, exquisite visualizations,   or writing code. Data science is about using data to create as much impact as possible for a company. 

Now, the impact can be in the form of multiple things like insights, data products or product recommendations for a company. Data science is used across various industries already. With the advancements in predictive modeling, data scientists can help predict the outcomes of a particular disease given the historical data of the patients. 

With data science, financial organizations can manage their resources and make smarter decisions through fraud detection.

Stages involved in Data Science?

1.Defining the Problem

2.Obtaining the Data      

3.Scrubbing/Cleaning the Data

4.Exploratory Data Analytics

5.Data Modeling

6.Data Visualisation

This section discussed with following aspects 

 

A)  ? Why is Data Science important for businesses

       ? What makes a data science job so desirable

       ? What is the future scope for Data Science

       ? Why is Data Science Important in 2021

B) examples of Data Science- centric Industries

Finally, How do I Become A Data Scientist?

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Fascinating Data Analytics Real Life Applications in 2021

To gain such important insight into data as a whole, it is important to analyze data and draw specific information that can be used to improve certain aspects of a market or the business as a whole. There are several applications of data analytics and business are actively using such data analytics applications to keep themselves in the competition. Not only businesses but even civic bodies are using data analysis for several reasons, like monitoring crime.   

Top Data Analytics Applications

1. Security

2. Transportation

3. Risk detection

4. Risk Management

5. Delivery

6. Fast internet allocation

7. Reasonable Expenditure

8. Interaction with customers

9. Planning of cities

10. Healthcare

11. For Travelling

12. Managing Energy

13. Internet searching

14. Digital advertisement

Wrapping Up

It is clear that data analytics applications are taking great strides in almost all avenues across the globe. If we are able to understand data and analyze it, it can help in increasing our overall job efficiency a lot. However, misuse or inefficient use of data can cause several problems and lead to the lowering of overall productivity.

So, it is important that data scientists know how to make use of data efficiently and engage in the right applications of data analytics. If used in the right way, data analytics can bring about a major positive impact on our society and world at large and increase the overall productivity of specific sectors.

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smart-farming-powered-by-analytic

 Fascinating Data Analytics Real Life                     Applications in 2021.

In today’s world, data rules the most modern companies. Numerous packets of data are circulating all around the world due to increasing access to the internet. Businesses are aware that this data translates to information which they can use to improve their customer service, understand trends, or even find market loopholes.

To gain such important insight into data as a whole, it is important to analyze data and draw specific information that can be used to improve certain aspects of a market or the business as a whole. There are several applications of data analytics,

1. smart-farming-powered-by-analytic

Topics covered :

1. Indian Agricultural Sector

1.1 Key problems faced by the Indian Agricultural Sector

2. Smart Farming

2.1. Role of Analytics in Smart Farming

2.2 Use cases of Analytics in Smart Farming

2.3 Analytics in every step of the farming cycle

3. Putting it all together – ‘Smart Farm Operating Model’

4. Global implementations of Smart Farming solutions

5. Key challenges in Smart Farming adoption

6. Addressing key challenges

A key area to be worked upon is the strategy to ensure economic feasibility and ease of adoption. Taking cues from implementations across the world, a prudent approach would be to start small– with pilots in small farming districts. Even though every market is unique, there are learnings from every implementation that can be taken forward. Once, a robust framework is developed, the solution can then be scaled across regions.

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The Top Email Spam Filtering Solutions (Discover the top email filtering solutions to filter spam, phishing and malicious email)

 Discover the top email filtering solutions to filter spam, phishing and malicious email

spam emails have evolved from being a nuisance to being a security threat, that can put individuals and businesses at risk of malware. Dealing with spam is frustrating, expensive, and time-consuming. For businesses, spam can be potentially harmful, with cyber attackers using spam email to spread malware to business users. For these reasons, it’s hugely important that anyone relying on emails has a strong anti-spam filter in place.

 

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7 Tips for Detecting Online Fraud

 Running an eCommerce business comes with its own unique set of challenges. One of the biggest is trying to figure out which of your customers are who they say they are and which are trying to commit fraud using stolen payment information.

Table of Content

1.     Fight fraud with Address Verification Service (AVS)
2.    Check location information for fraud indicators
3.    Google your customers
4.    Check for suspicious email addresses
5.     Detect fraud by noticing unusual account activity
6.     What are the best ways to stop fraud?
7.    How do I spot the fraud?
8.     How do I spot friendly fraud?
9. .  How is fraud most commonly detected?

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IT Careers: How AI is Driving the Next-Gen of IT Professions

 The growth of the Information Technology sector in the past two decades has been nothing short of phenomenal. IT, as it is commonly known, has become so commonplace among all sorts of organizations, irrespective of the type or size, that not using it is considered unwise and rather foolish. IT has established itself as indispensable to the modern economy and IT professionals are the backbone of it

.

AI-driven Occupations:

 A)Data Scientists:

With the amount of data that is being generated every second, it becomes imperative to sort this data and make something meaningful out of the data gathered.

B)Big Data Engineer:

A Big Data Engineer is concerned with one organization’s data ecosystem. They build an environment/ecosystem for interaction between business systems.

C)Machine Learning Engineer:

Machine Learning Engineers are responsible for building and maintaining self-learning software to facilitate machine learning projects.

D)Business Intelligence Developer:

A professional is someone who can offer the best of both worlds. One world being the world of business and the other being the world of data and IT.

E)Robotics Scientist:

AI and Robotics are the two things that are synonymous with the layman. However, robotics is a field that is being supported and improved with the emergence and improvement in AI.

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The Practical Value of Game AI

 The Practical Value of Game AI

What is it about board games and video games that attract artificial intelligence research so much? It started with the checker-playing algorithms in the 1950s, where researchers were amazed at the “thinking” the checker-playing algorithms exhibited. That was followed by chess which became a focal point of AI research all the way to the 2000s. Fast-forwarding to 2015.

A Viral video of a neural network playing super Mario increased mainstream interest in video game AI, and made its way beyond the video game developers’ niche and into mainstream data science banter.

For video : 

Quote: It is a bad idea to intuit how broadly intelligent a machine must be or have the capacity to be, based solely on a single task.

The checkers-playing machines of the 1950s amazed researchers and many considered these a huge leap towards human-level reasoning,
yet we now appreciate that achieving human or superhuman performance in this game is far
easier than achieving human-level general intelligence. In fact, even the best humans can easily be defeated by a search algorithm with simple heuristics. Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks.

— Luke Hewitt

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