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Work on Real-life Data Science Projects and Get a Guaranteed Job In Data Science

If you ask me How can I secure my first job as a data analyst with no experience at all. My answer would be Data Science Projects.

It has become very important to showcase your skills to the world, to the employers to stand out from the crowd to get placed in top companies.

Even if you are skilled, How do companies get to know? Your employer needs to know that you have worked on real-life problems. Because that is what your job description is in a nutshell.

So we have classified some useful and interesting data science projects into two parts :

Data Science Projects For Beginners :

Fake News Detection Using Python :

I do not need to tell what fake news is. In the era of Advance Connectivity, False news and rumors are spreading everywhere.

There are many fake and misleading news stories related to medical treatments and major diseases like cancer or diabetes. Trusting these false stories could lead you to make decisions that may be harmful to your health.

“FAKE NEWS CAN LEAD TO CHAOS”

Pandas, NumPy, and sci-kit-learn are some Python packages suitable for this project.

Project On Sentimental Analysis :

According To Wikipedia,

SENTIMENT ANALYSIS (ALSO KNOWN AS OPINION MINING OR EMOTION AI)
IS THE USE OF NATURAL LANGUAGE PROCESSING, TEXT ANALYSIS, COMPUTATIONAL LINGUISTICS,
AND BIOMETRICS TO SYSTEMATICALLY IDENTIFY, EXTRACT, QUANTIFY, AND STUDY AFFECTIVE STATES AND SUBJECTIVE INFORMATION.”

Sentiment analysis is actually a tool that recognizes the sentiments behind a text.

Intel, Twitter, IBM, Facebook, IMDB are among the companies using sentiment-analysis software and similar technologies to determine employee concerns and, in some cases, develop programs to know trends.

Detecting Of Credit Card Frauds :

Credit Card Frauds is a growing problem, According to one survey :

In 2018, credit card fraud made up 50.5 percent of the top 5 types of identity theft.

You can go to Credit Card Fraud Statistics and be amazed. You’ll learn how to apply supervised learning algorithms to detect fraudulent behavior similar to past ones, as well as unsupervised learning methods to discover new types of fraud activities.

Companies like Amazon, Google, MasterCard, Apple, Paypal use Fraud detection systems.

Data Science Projects For Intermediates :

ChatBots :

Chatbots are really important for companies. A single human being is not capable enough to answer all the queries of thousands of customers without getting frustrated. Now We all have used chatbots, you must have seen the difference.

Some chatbots are nothing but a series of if and else, but some of them are really intelligent. Now companies are working on their chatbots.

There are a number of unique challenges to building these kinds of programs, like how do I turn human language into instructions for machines?

I do not need to tell what companies are using chatbots right?

Detection Of Drowsiness In Drivers :

Drowsiness is one of the big reasons for accidents to happen. Imagine there is a detection system in your car, that detects sleepy drivers, We can save many lives every year.

” 21 percent of all fatal accidents

are due to drowsy driving.

A webcam is required for this project for the system to monitor the driver’s eyes regularly. This Python project will require a deep learning model as well as packages such as OpenCV, TensorFlow to do this.

Conclusion

By now you must have realized, why these projects are important. Employers are looking for people who understand and solve real-life problems.

If your portfolio has these projects, They are badges. They tell a lot about your skills. But you must understand, you need to make these projects from scratch and with a guiding mentor.

Doing projects is the last step, you must learn data science. We are offering our course, where you will learn with industry expert instructors with guiding mentors, and live sessions with projects like these.

You can always connect us through our Website and we will be happy to help you and guide you.

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