Do Women Have A Bright And Promising Future In Data Science?

Do Women Have A Bright And Promising Future In Data Science?

Data scientist, the most desiring and sexiest job of the century, has witnessed immense demand over the recent years. Today, women from every corner of the world are blazing a trail in this domain.

Did you even know that it’s been found that the need for data scientists is going to rise further by 28% by coming 2 to 3 years?

So, pretty convincing to assume that this career choice is not going to decrease anytime soon.

Women surpass every possible field and have a long way to go ahead besides gender parity in technological and job sectors.

Remember, 

“There is no limit to what we, as women, can accomplish.” 

 Michelle Obama

Today, there are a number of women data scientists and leaders who are inspiring and proving the fact wrong that women are any less than men.

 

Wondering If Data Is Really A Career Choice For Women, Too, Just Like Men?

 

Women-career-in-Data-Science

 

Of course, yes! No questions to be asked further. After all, women are amazing contrarian thinkers. They surpass at logical analyzing, nurturing a positive atmosphere everywhere, great problem-solving abilities, and be role-modellers.

Women are inquisitive and possess exceptional leadership skills, and that’s what makes them an ideal fit for a data scientist job. To all those women who are performing their jobs, with all their dedication and effort, kudos and a big shout out to each one of you.

Demand For Data Science In 2021

 

Demand-for-Data-Science

 

In 2020 itself, it’s been found that there were 2.9 million job openings for data scientists, and considering these numbers, by the end of 2021, it is certainly going to show an up movement. As per the report published by IBM, last year, the data scientist job boomed by lakhs and more. Hence, it would not be wrong to mention that the data scientist career is soon going to be the hottest job roles for both men and women in 2021 and beyond.

Job Roles In Data Science Domain

 

Job-roles-in-Data-Science

 

The steep increase in data science as a career choice in 2021 has led to a rise in various job roles and responsibilities. Some of them are listed below –

  • Data administrator
  • Data and analytics manager
  • Data engineer
  • Data scientist engineer
  • Data statistician

In order to kick-start in this domain, one must pursue data scientist training and acquire the skills.

 

How Data Scientist Professional Be An Equally Fascinating Career Option For Women?

 

how-data-science-good-career-for-women

 

See, the purpose of this job is to dig facts and truths from a variety of data sources, and women are considered to be the best in it. Nevertheless, there are many reasons that make women ideal for this job. Let’s get to know them –

  • Women possess great communication skills – In the data science domain, competence plays a key role. It gets pre-eminent to make decisions based on final data outcomes. Nevertheless, all professionals of this field have mutual knowledge, or we can say background of business acumen, statistics, math, computer science, and business analytics, but the primary thing that stands apart, in this case, is analytical thinking and constrains the approach. Not to forget, women often surpass it, which further leads them to the point where they tend to make informed and better business choices.
  • Women possess great team management skills – Working as a data professional is an extremely demanding job, requiring seamless communication among one another and exceptionally agile performing skills to work. What’s more, working in some big-scale enterprise often has IT, business leaders to talk to and manage their business operations.

True data scientist professionals don’t just possess exceptional technical skills; instead, they are great at everything, probably the ones who can get the best insights out of the data that can further add value to the firm. Most essentially, they have got strong yet effective communication skills to convince another member to implement some great changes in order to come with some great outcomes in the forthcoming.

However, one other great reasons for people showing more inclination towards this industry is it’s increased salary and upscaled market revenue.

 

Data Science Salary for 2021

 

Data-Science-Salary-2021

Many people these days are getting attracted to this domain – the most significant reason behind it could be its challenging nature and the growth opportunities it has to offer.

Do you even know, as per Glassdoor, the average salary for a data scientist job is very much more than you can think?

Talking in numbers, it’s somewhere approx 1052k per year; that is pretty attractive on its own. However, too often than not, it varies from one enterprise and employee to that of another.

 

Women Data Scientists Who Ruled And Made A Difference

 

Women-Data-Scientist-ruling-company

Despite the gender gap a big problem in the data science industry, many women have still made up a big difference and revolution in this domain.

Let’s now get to know about some brilliant and remarkable women ruling this data science arena.

  1. Radhika Kulkarni – Indeed, a lady that really made India really proud. She’s the most influential woman in history, who served more than 35+ years in AI, machine learning, and data research. Her work and enthusiasm towards her work really made her an inspiration for many.
  1. Corinna Cortes – Corinna was born in Denmark and possibly be the most remarkable data scientist in existence. She’s known for her highly effective working skills and knowledge, proving many prescriptions wrong that women can’t do this or that. Presently, she’s working at Google on theoretical as well as machine learning problems.

If you’re someone who’s willing to make a rewarding and challenging career ahead, along with so many perks to experience, explore this male-dominated data science field and start your journey today, and get enrolled in data science certification training.

Just never forget –

“Feminism isn’t about making women stronger. Women are already strong; it’s about changing the way the world perceives that strength.” 

 G.D. Anderson

How to become a Successful Data Scientist?

How to become a Successful Data Scientist?

Do you wish to become a data scientist and accelerate your career? Great! Data science certification training from a reputed place will help you. You’ve reached the right place!

Data science is one of the most buzzed-about domains today, and data scientists are in the farthest demand, with enterprises in almost every business industry seeking to get the maximum value from their burgeoning information resources.

You need to understand –

“Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.”

Josh Wills, Director of Data Engineering at Slack

I think it won’t be wrong to state “DATA SCIENTIST IS KING” at today’s time & with data science certification training you acheive this title. Extracting actual business value from data with the combination of technical skills, mathematical know-how, storytelling, and intuition is what it is best at.

Given all the exciting applications and good reasons, it makes complete sense to say that data science is a very sought-after career.

 

How Do I Become A Data Scientist?

 

Data Science Certification Training

Here’s my personal favorite saying that will make things and necessities for it a bit more clearer to you –

“The No. 1 thing is you’ve got to have passion. This rich passion for going ruthlessly after the problem and being deeply intellectually honest with yourself about whether this is a reasonable answer….

“The second part can be extremely clever with the data. And what I mean by that is: You’re working with ambiguity. And very often, you can’t approach the problem with the rigor you would a homework assignment. The only way to survive through that is by being clever—to think of a different question that gets at the answer.”

DJ Patil, Former US Chief Data Scientist

 

What Do You Need to Be a Data Scientist?

 

What-Do-You-Need-to-Be-a-Data-Scientist

 

When decided to become a professional data scientist by profession, there are top four most vital things that one need to keep in his core remembrance and that are

If you can put three of these elements in place, odds are, you are onto the road to becoming a reliable and professional data scientist by profession. Let’s now know what the other attributes that are needed to be there in a person’s.

 

The Right Approach To Be Data Science whiz

 

The-right-approach-to-be-a-data-scientist

 

So what does it take to be a data science whiz? According to IT leaders, business analysts, successful data scientists, and others, here are some vital attributes and skills to earning the stripes and calling yourself a thriving data scientist. The journey won’t be simple; however, it will be substantially more stirring than following the conformist wisdom.

 

7 Steps To Become A Data Scientist

  • Develop the much-needed data skills
  • Acquire knowledge on data science fundamentals
  • Learn vital programming languages
  • Do hands-on practice on live projects and acquire practical skills
  • Make visualizations and practice using them
  • Make a portfolio and show your data science skills off
  • Apply for suitable and relevant jobs
  • Develop the much-needed data skills

If you’re someone who has expertise in data, then well and good, but if not, if you’ll need to acquire its concepts and knowledge. It is the need of the hour to work towards a data scientist career. It is indeed a high-position job, so before you become a pro in it, you will need to have a broad base of Python, Excel, Tableau, and SQL. The more you will be transparent with these things, the simpler it would be to reach another step.

 

Acquire knowledge of data science fundamentals

 

Data-Scientist-training-Fundamentals

 

Pursuing an executive program in data science can be one of the most ways to acquire data science skills. While pursuing this one, one will learn critical skills such as storing the data, collecting it, visualizing the present data using different tools, and other specialized applications. What’s more, one will be taught to use programming languages such as R and Python, to predict and analyze anonymous data’s behavior.

 

Learn vital programming languages

 

data-science-certification-training-helps-learning-languages

 

There are many tools and programs that data scientists need to be dependent upon for jobs such as data modeling, cleaning, and analysis. To do their jobs effectively, they need to be familiar with programming languages like Hive, R, Python, SQL, general-purpose Excel, and other tools such as RStudio and TensorFlow, Accord.Net, and Jupyter thoroughly.

 

Do hands-on practice on live projects and acquire practical skills

 

data-science-certification-training

 

By now, you must have acquired the necessary skills of programming languages and digital tools; it is the time to put all your knowledge and skills into practice. Put your hands on doing projects and using all your range of skills and statistical knowledge to make different models and predicts insights. The more you practice doing so, the better and smarter you will get at it.

 

Make visualizations and practice using them 

 

practical-data-science-certification-training

 

One must practice making his visualizations from scratch, practicing different digital programs such as Plotly, Bokeh, Tableau, PowerBi to make calculations and graphs. Creating these visualizations is merely the beginning of the thriving career path, all you need to do practice it as much as you can and later on presenting the most accurate findings.

 

Make a portfolio and show your data science skills off

 

Data-Scientist-training-&-portfolio

 

Once a person has successfully attained the preliminary skills, knowledge and done with the hands-on practice thing – it is the right time to show what you have got by making a polished and striking portfolio. Give it all you have because it will land you to a certain position, and I hope no one ever wishes to compromise it.

Remember – 

Continuous effort– not strength or intelligence – is the key to unlocking our potential.”   

Winston Churchill  

 

Apply for suitable and relevant jobs

data-science-certification training & placement

 

Today, there are different, and indeed many, jobs in the data science arena. Once a person has acquired all the skills needed to be a professional data scientist, there are various subfields, like machine learning engineers, data analysts, quantitative analyst data engineers, database administrators, to name a few. It is up to a person and his passion for it to make him land the most-suitable and desirable job position. Today, opportunities abound, almost everywhere – all a person needs is a scarcity of technical talent, skills training, a strong portfolio, and dedication towards the job!

You’ve Got It!

Becoming a successful data scientist isn’t simple, but the key is to stay motivated and learn with dedication. It is the blend of a range of technical and soft skills that make up a superior data scientist – and this is something one can do via an exclusive training course.

Learnow guarantees excellence with their Advanced Data Science Courses, where you can learn the foundational coding and statistics skills in data science.

Good luck to you. May you become a reliable and quality data scientist super soon!

How Data Science helps in Powering Business Values

How Data Science helps in Powering Business Values

Irrespective of the industry a business is dealing with, every enterprise has to deal with silos of data every moment where the next-gen tools backed by modern technology assist businesses to manage their data to a great extent. Every day, businesses are flooded with a huge volume of data of various forms. In fact, as per a recent report, it is depicted that the total amount of data created, captured, copied, and consumed in the world is forecast to increase rapidly, reaching 59 zettabytes by the end of 2020 and cross 149 zettabytes by the year 2024.

So, as the data keeps on multiplying within a business, Big Data assists crucial factors where advanced data science training & expertise can transform modern technology into actionable insights.

In today’s times, every business welcomes big data for improving their business efficiency, user experience, development process, and so on. Data science’s importance is growing day after day with its procedure and analysis, which is where data science gains its true potential.

 

Understanding Data Science

Understanding-Data-Science-training

Data science is a strategy to transform business data into assets that help businesses improve their revenue, minimize costs, offer broader business opportunities, and uplift the overall customer experience. It is a combination of machine learning, data interface, and other unique algorithms. Data science helps in identifying hidden patterns from the raw data and addressing analytically complex data issues.

In order to make well-versed decisions, data science acts as the pillar while analyzing the current and previous data for receiving improved future aspects. All in all, data science can help make predictions with which businesses can make better business decisions. Since most of the data we come across daily is unstructured, which can’t be used for future aspects. At this point, data science, which is the future of AI, helps business owners make better and smart business decisions as never before.

 

Proven ways how Data Science can help in uplifting Business Values

how-data-science-training-help-in-uplifting-business

Data science has countless hidden benefits that the majority of the business owners might be unaware of. And in case you expect more convincing reasons to count in data science, here is a dropdown that lists the notable aspects of how data science can improve business values.

 

Improved Decision Making

 

Improved-Decision-Making-through-data-science-training

For every decision-maker, data must be at their fingertips. Still, since most of the data is unstructured and requires predictive analytic tools for getting insights on that data, it becomes a daunting task.

Implementing data science can pull out numbers and statistics that enable businesses to create predictive models and simulate wide opportunities. This allows decision-makers to decide which solutions would lead to the best outcomes. Moreover, by recording performance metrics and analyzing them with time, decision-makers can make more accurate and efficient decisions based on recurring trends.

 

Improved Recruitment Process

 

Improved-Recruitment-Process

Hiring new candidates can be a daunting task for the hiring staff. However, implementing data science can make your job easy by making the entire process faster and accurate than ever before.

Since all the data points related to hiring on social media, job sites, and corporate databases are made available to businesses, hiring managers can utilize analytical methods to identify the ideal applicants suitable for a particular position. This implies that you get a fair opportunity to hire candidates that look good on papers and fit your company culture and meet the standard you are looking for. This is another reason that more and more people are getting attracted to an executive program in data science in today’s time.

 

Better Product Relevancy

 

The modern data science practices hold the potential to explore historicals, analyze the market, compare the competition, and make recommendations related to where and when will some particular products or services sell best. So, this assists businesses to get clear insights on how their product can assist customers.

The consistent analysis backed by data science offers an in-depth understanding of the market’s response to its products and services. With these valuable insights, business owners can rethink and plan a better and improved business model based on customer needs and expectations.

 

Identifying Potential Customers

 

Identify-potencial-customer

While managing silos of data on a daily basis, gathering the relevant data for your business and customers is always a hectic task for business owners. Irrespective of website visitors, email surveys, or social media engagement, every data gathered can be an effective tool to understand and get an idea about your customers and learn their interest areas and preferences.

With the help of data science, businesses can integrate data points for generating insights for targeting their potential customers more effectively and smartly. This indicates businesses can now tailor products and services to particular segments depending on notable parameters like age, income, and so on. So, this ensures a better opportunity to grab the attention of customers, which was difficult otherwise.

 

The verdict

 

Data Science certification training & strategies help add business value while taking the other major operations, including recruiting, training, decision-making, marketing, and a whole new level. Making well-informed and smart decisions in real-time is what businesses can expect from data science in the long run while experiencing next-gen business operations.

If you have a clear understanding of data science, your business can make the best out of it and take your business to a whole new level. For businesses who wish to expand their data and gain valuable insights while filtering the relevant data, data science can keep you covered and help you make wiser and better decisions in the long run. With the growing adoption and popularity of data science all across the globe, more and more people have started looking for an expert executive program in Data Science and grow their career in this ever-evolving industry.

Wish to explore more about data science’s true potential or learn about Executive Program in Data Science? Our experts are just a call away to assist you!

Why are Great Communication Skills Slowly Becoming a Requirement for Data Scientists?

Why are Great Communication Skills Slowly Becoming a Requirement for Data Scientists?

According to Glassdoor, Data Science is the best job in America for 2020. Many of them have called Data Science “the sexiest job of the 21st century.” Another data collected from the Business Insider, in which we get to know according to LinkedIn, Data Science will be high in demand in the coming years, and there will be more job opportunities for people in the future. Over 93,500 data science jobs were unoccupied in India at the end of August 2020, a research performed by EdTech company Great Learning.

IBM, Jio, Microsoft, Ericsson are some multinational companies currently hire Data Scientists and evaluate good written and verbal communication skills in candidates because it helps deliver and understand information accurately and quickly. Data scientists are ones who give new directions to your business, automate the ineffable through AI and machine learning, and help reduce the risk by making significant on-fire decisions.

As in every sector, “Soft Skills” are expected from data professionals. If they would not communicate, how can you imagine that they help your company manage and interpret a vast amount of data with solving your complicated issues? Data scientists are useless if they don’t interact in business meetings. They must be capable of corporate culture and must have practical ideas to help the company stay ahead in the competitive world. Without communication skills, it would be challenging for a data scientist to be a team player.

A Data Scientist’s chief responsibility is to communicate deep business or consumer analytics to business owners that you need to learn in your advanced data science or executive program in a data science course if you make your mind to build a career in this inter-disciplinary field. Not everyone has a beautiful mind. Not everyone can be a successful data scientist.  Still, when it comes to data analytics, you can’t forget communication skills when you already know that the data scientist aims to analyze and organize large amounts of data using specially-designed software. The data scientist’s final objective is to understand all involved stakeholders, particularly those working outside of IT.

Again, if you are ready to build the most-rewarding career with advanced data science, you need to give more importance to communication skills than yourself. Here we’ve curated a few tips that you can look forward to improving your communication abilities for the role of Data Science.

 

Must-Know Communication Skills You Need to Check for Advanced Data Science

 

Be a Diligent Storyteller:

 

Communication-Skill-of-Data-Scientist

 

When a story is combined with data, it helps your audience understand what’s happening in the data and why a particular insight is essential. Storytelling is just as relevant as presentations.

A good leader uses storytelling strategies, especially when working with data science insights, that can be quite complex for business users if you help them grasp with your poor communication or storytelling skills. Therefore, you need to start with a hook and have a point for the story, like why your audience should hear this narrative. Being a storyteller in Data Science, you should set the right time to describe your story.

Follow These Easy Tips:

Frame the story: Set an interesting background for your listeners and help them make your story valuable.

Express the Story: Make your story exciting whenever you convey it to your users.

Moral of the Story – Don’t forget to mention the story’s moral while summing up or at the end.

 

Be an Active Listener:

 

Active-listener-in-advanced-Data-Science

 

In business, the habit of active listening exhibits to customers that you care for their unique demands. If you ever get a complement of an active listener, you can create outstanding records with data science. For this, you can follow the videos of famous data scientists and can join any Bootcamp to listen to experts in live classroom training.

Active listening provides numerous benefits to data scientists. Some of them are as follow:

  • It helps you build trust with new customers during business meetings.
  • It helps you build more connections to start new projects.
  • It aids in finding and solving problems faster.
  • It gives you a thorough understanding to work on a variety of data niches and helps you avoid critical information.

Learn Data Visualization with Online Lessons & Courses:

 

Learn-Data-Visualisation-with-Online-Lessons-&-Courses-in-advanced-data-science

 

Having the skill of data visualization is a must to represent the data graphically. It is the same as the illustration that you find in any comic. Remember that complex idea are presented in graphs and charts to transmit actionable information easily and quickly.

Data Visualization is an important subject taught by experts in the advanced data science course or executive program in data science. It plays a lead role in data analytics and data science, especially when working with complicated and larger data sets. You can start learning about data visualization software and programming languages like Python and Microsoft Excel to analyze and visualize a wide variety of data.

 

Enhance Business Writing Skills:

 

Enhance-Business-Writing-Skills-in-advanced-data-science

 

Writing skills are essential parts of communication. With good writing skills, you can convey your message clearly to your larger audience. Try to make your email professional because that is the key to manage the business efficiently.

To enhance your business writing skills, you can do some few things:

  • Write a white paper or conventional report based on your findings.
  • Write content for web pages.
  • Frame a future piece of writing in proposals or make a more structured strategic plan for the c-suite.
  • Again, start making formal emails and send them to the management levels. Do not use slang when writing for business.

Conclusion

Communication skills are crucial for data scientists and a necessity for everyone who wants to be successful in an organization. Data scientists communicate and collaborate every day with many people from different industries. You have to put yourself in their shoes while explaining their background and their interests. If you can’t work on your communication skills, sorry to say, you can’t become the highest-paying data scientist. If you want to increase your confidence, it’s high time to learn the advanced data science course online at LearNow. We help you interact with real mentors who are certified industry experts and support you while improving your data science communication skills.

How Python Emerged as the Foundation of Tech Giants: Win a Kaggle Competition in Data Science

How Python Emerged as the Foundation of Tech Giants: Win a Kaggle Competition in Data Science

Python is the most powerful, open-source, interpreted, object-oriented, and a very high-level programming language for data scientists. They need dynamic semantics, in-built data structures, a large community, and excellent libraries to deal with various data science projects/applications. 

It is a general-purpose programming language that can be used for developing both web and desktop applications, including complex numeric and scientific applications. Due to its versatility, it has become the fastest-growing worldwide use coding language and the right choice to kick-start a career in Data Science. 

So, How Python emerged as the foundation of tech giants to win a Kaggle competition in Data Science? Let’s dig deeper into the topic: Python for Data Science.

 

History of Python Programming Language

 

History-of-Python-programming-language

 

  • Python was created by Guido Van Rossum and was first released in 1991 to help programmers write a logical and clear code for small to large-scale projects.
  • Python 1.0 was issued in 1994 with new features such as map, lambda, filter, and reduce.
  • The final version of Python 2.0 came into the market on October 16, 2000, with additional features like garbage collection systems and list comprehensions.
  • Python 3.0 was published on December 3, 2008, to improve the language’s fundamental flaw.
  • Python 3.9 was declared on October 5th, 2020, and its next version is expected to come between 2020 and 2025.

Features that Attract Techies to Use Python for Data Science

 

Features-of-Python

 

Python is faster to learn and easy to use in contrast to other programming languages. Its syntax and grammar are very close to the natural language that helps programmers increase their productivity while working on Data Science.

 

Faster to Learn & Easy to Use

 

Python-fast-to-learn

 

Python for Data Science is the recommended programming language for beginners because it has no use of curly-brackets or semicolons. If a single skip in C-like languages, it would be impossible for you to compile the program.

 

Cross-Platform Language

 

Python-Cross-platform-language

 

From Google, Instagram, Facebook to Spotify like world-class companies use Python because it allows them to write a program on Macintosh computers and run it on different platforms such as Linux, Unix, Windows, and vice versa.

It is a very expressive and portable language. Overall, we can say that one language is sufficient for everything. Whether you want to build web applications and machine learning tools, it has everything you need in one language and simplifies your project using less time and money.

 

Numerous Data Science/Machine Learning Libraries

 

Python & Machine Learning

 

Python for Data Science is the key to the future. The reason for the Python popularity is its numerous free data science, data analysis, and machine learning libraries like Scikit-Learn, Pandas, Numpy, etc. All these libraries of Python help in data mining, data analysis, and data manipulation. With Python in-built libraries, you can quickly solve real-world problems and implement popular algorithms on datasets.

Apart from that, there are some deep learning frameworks in Python for data science, likewise TensorFlow, Caffee, PyTorch that allows you to build deep learning architectures by just using a few lines of Python code.

 

Hadoop Compatibility

Hadoop, an open-source software framework, supports Python for big data. The Python package labelled as Pydoop enables you to access APIs for Hadoop, and you can write several Hadoop programs using Python.

With this package, you can quickly write quality codes and solve complex problems by putting minimal effort.

 

Largest Community Support

 

 

Python-largest-community-support

 

Python has the largest community support for beginners to experience coders. The popularity of data science using Python is increasing day-by-day, but we can’t forget the language is also widespread due to its broad Python developer community.

They help everyone from Python programmers to data scientists and whenever they find it difficult while writing a code for web scraping or any other project related to Python.

 

Conclusion

 

Want to build a profitable career through Python for Data Science Training? Check upcoming classes at LearNow and give us your precious four days to prepare you for the certified Data Science exam. We have world-class instructors to teach you essential Python skills used in Data Science, and it helps you get the very tempting job of the 21st century with the highest salary in companies that you wish to work.

The hype around AI and the need for Advanced Data Science Courses

The hype around AI and the need for Advanced Data Science Courses

Every day we hear news about projects that involve Advanced Data Science and new AI advancement made in various parts of the world. The business leaders in the AI industry have worked to turn the ideas of Sci-fi movies into reality. We can now see driverless cars on the road, smart homes powered by AI, or other NLP examples in everyday use like e-Mail filters and smart assistants like Amazon’s Alexa or Apple’s Siri. But is AI actually worth the hype all around the world? Why is it that ambitious youngsters want to grab their Certificate in Advanced Data Science Course?  

According to Gartner, the IT department’s number of AI jobs has tripled between 2015 and 2019. Also, the most substantial demand for AI–skilled Data Scientists comes from the non-IT departments. A recent report by Statista states that the AI job titles are the highest paid jobs in the US as of 2019, with an annual income of around 143 thousand US dollars.

The above stats are evident from our daily life as well. We all have become slaves to our AI devices like Alexa or Google Assistant, and are falling for the genie inside our smart speakers. Humans are social animals, and unlike other living species, we are blessed with the power of speech. That, too, is not restricted to human beings. Machines are programmed to give an intelligent response to your questions. But, their answers are not 100% accurate to answer all the questions asked by humans. A report published by Business Insider in 2018 stated that “Alexa answered informational questions accurately 91% of the time, up from 78% in July.” The information questions are related to general questions, like discovering a country’s population to identifying how long it takes water to freeze.

So, if you ask, “Can AI perform all my tasks as efficiently as I can do?” The answer would be a straight, “No!” But, if the question is, “Can AI simplify my task related to Data handling and analysis?” Then the answer is clearly, “Yes!” AI has techniques to handle such situations effectively and effortlessly. 

 

What is AI or Artificial Intelligence?

What-is-Artificial-Intelligence

 

Artificial Intelligence refers to a case where a computer can do anything that would otherwise require human intervention. It includes Machine learning, Planning and scheduling, Game playing, Machine Vision, and of course, NLP. Today, AI is used for Sentiment Analysis, and machines try to imitate human capabilities. But, devices cannot match the ability of a human mind and heart. The response of an engine is quite alienated. 

For example, if you ask a person and a machine to express a specific tweet’s sentiment, the answers will indeed vary. A person will read and understand the whole tweet, whereas a machine would consider the length of the words, their distribution, and the punctuation used. The sentiment analysis can reliably predict the text’s sentiment, which can be used for Advanced Data Science models. Still, human research will give a deeper understanding of the particular text. It is because humans and machines have a different approach to handle things. With increasing data volumes and the growing need for quick and accurate data analytics for better decision-making, we can switch to AI for Big Data Analytics. Businesses must understand the facts, statistics, and data for an effective and result-oriented decision-making process.

 

Cases where AI plays a crucial role

 

AI-Plays-crutial-role

 

Look for the following guidelines in your project, and see if AI will play a critical role in it.

  • Can be performed by a human
  • The process is descriptive and formal
  • The solution can be categorized as positive/negative
  • Can be expensive if done by humans
  • Time-consuming
  • Machines can perform the task effectively in a short period

A few examples where AI is significantly used, based on the above guidelines, are online conferences, prediction analysis, summarization of text, etc.

 

Cases where AI does not help

 

AI-Cant-Help

 

There a few cases where AI will not be useful, and the social role will be more important than machines. AI is not the best tool in every task and cannot replace human intelligence. There are a few cases where the workflow cannot be defined, and the job cannot be expressed formally. AI needs strict rules for the completion of a task and decision making. Also, the human cost may be negligible for a task compared to the implementation of Advanced Science models.

 

Benefits of using AI

 

Benefits-of-using-AI

 

AI is used to solve problems quickly and helps in better decision making. The following benefits will help you realize the importance of AI in the coming years:

1) Reduced Human Error

2) Overcome human inhibitions and take risks

3) Available 24/7

4) Performing Repetitive Tasks

5) Digital Assistance for social interaction

6) Quicker Decision making

7) Used in Daily Applications

8) Emoweirng Innovative developments

 

Drawbacks of using AI

 

Draw-backs-of-using-AI

 

Like they say, “There are two sides of a coin”, people are sceptical about the increasing popularity of AI in the industry. The disadvantages of using AI are:

1) Creation needs huge investment

2) Human learning and working capabilities may stagnate

3) Threat to employment

4) No Emotional bond with humans

5) Machines can’t think Out of Box 

Like every new development in technology, AI has massive potential advantages but may threaten human development. But, we must realize that the “rising robots” can no-way enslave the human race.

One of the major concerns these days is that automation will replace human resources and threaten the workforce. But, organizations and business leaders are working in this direction and using the social intelligence of their resources for other creative tasks. It will also take a lot of time for humans to build complete trust in machines and AI. Of course, one can afford to buy and rely on a driverless car, but it will take at least a decade for people to board a driverless bus.

Over the years, the demand for a Certificate in Advanced Data Science Course has increased tremendously, and there are many job opportunities in various industries. Netflix, Intel, Google, and Microsoft have made the most investments in AI startups. Microsoft, Nvidia, ServiceNow, DocuSign are leading AI companies.  

 

The AI hype is real

 

AI-Hype

 

With many organizations adopting AI and Advanced data science models for their business operations, the AI hype is justifiable. Our fascination with scientific fiction movies is becoming a reality. AI is silently operating a large section of the businesses and astonishing the users with its unimaginable intelligence. AI brings along a lot of excitement, but we should be realistic and understand the real prospects.