March 19, 2023 - ONLEI Technologies Reviews

Top 6 Data Science Projects To Get You Hired in 2023

Data Science helps solve real-world problems by properly using the relevant data. In this day and age, companies are using the information procured by data science professionals to understand customers’ behaviour, project sales, and estimate the future of the product in the market it is being launched. This is why a Data Science Certification from a reputed university with a curriculum designed to build industry-valued skills is preferred by companies that are looking for data science professionals.

In order to make sure that your resume stands out from the rest, it must contain some crisp and fresh data science projects. In this article, we have collected 6 data science projects that can help you build a strong profile.

1. Sentiment Analysis

Language: R
Dataset: janeaustenR
Libraries (guides included): Pandas, Scikit-learn

What is Sentiment Analysis?

Sentiment analysis is a method that is used to analyze the opinion of targeted customers on a specific product or service offered by the company. It is used in companies to perform testing of the likeability of their products or services. The main goal of this is to figure out the WHYs behind not achieving the target sales or a product/service not being liked by the customer base.

Additionally, it helps in figuring out the reforms to make the brand’s offering more acceptable to its customer base.

Details about the project

This data science project would require you to make use of NLP, computational linguistics, text analysis, and biometrics to derive rich insights from the data provided. The basic task in sentiment analysis python is to classify the polarity of the opinion of the service/product being offered. The ranges of responses can be positive, negative, or at times included with multiple options like happy, sad, neutral, excited, and so on. This is a popular data science project idea that you can customize as per your need to make the project simpler or more complex.

2. Detection of the Parkinson’s Disease

Language: Python
Dataset/Package: UCI ML Parkinsons dataset
1.5 Color Detection with Python

What is Parkinson’s disease?

Parkinson’s disease is an old-age-related problem wherein the person loses control of his/her body parts. Its symptoms begin from tremors in hands, the rigidity of the body, to even shuffling of steps. This disease has 5 stages with stage 1 being comparatively non-interfering with daily activities and stage 5 being severely limited in terms of day-to-day activities. Most people suffer more due to late detection of the disease.

Details about the project

This is where data science steps in. You can use Python as the coding language in detecting Parkinson’s disease with XGBoost. XGBoost is an open-source software library that supports multiple libraries, including C++, R, Python, Java, Julia, etc. Using this data science project, early predictions of Parkinson’s disease can be made. The patients who are prone to getting the disease or show signs of getting affected by Parkinson’s disease in the future can be notified and this way an improved health service can be given to a patient.

3. Detection of Fake News

Language: Python
Dataset/Packages: news.csv
Libraries (guides included): Scikit learn (TfidfVectorizer and PassiveAggressiveClassifier), Pandas and Numpy

What is Fake News?

Recognizing fake news is not easy. There are multiple platforms and channels where information is distributed – but is it correct or not? This is a grave concern as fake news can ignite miscommunication, which can cause huge damage worldwide. With the increasing amount of data being generated every day, the spread of fake news has also increased rapidly. How can we detect this fake news with the help of Data Science?

Details about the project

You can create a project using Python with this data science project idea. This model will have two classifiers – TfidfVectorizer and a PassiveAggressiveClassifier to segment the news as Real or Fake. You can make use of JupyterLab, a web-based user interface that enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in an integrated, and extensible manner. A data set with dimensions of 7796*4 will prove to be highly supportive in this case.

4. Prediction of the Next Word

What is the Prediction of the Next Word?

We have all used Google Docs, WhatsApp, or the Google search bar at least once in our life. Have you noticed that while you are typing, you are given a few suggestions for the next word? This is what we mean by prediction of the next word. There are various algorithms built to help in predicting and suggesting what our next word may be.

Details about the project

A distinctive aspect of working on data science projects is that you get the freedom to create predictive type models. You must have noticed this while using Google Docs, WhatsApp, or even the Google search bar: all of these use the technique of predicting the next word by suggesting a new word after each new word you type.

This project is a great choice for someone who wants to transition to advanced-level projects. It requires the knowledge of NLP or deep learning to uncover the next word. The LTSM (Long short-term memory) model is the ideal choice for this as it makes use of deep learning with a network of artificial cells that manage the entire memory. This makes them better suited for predicting the next word.

5. Movie Recommender

Language: R
Dataset: MovieLens
Packages: recommenderlab, ggplot2, data.table, reshape2

What is Movie Recommender?

In today’s extremely busy world, recommendation systems are becoming very popular. Have you ever used the streaming platform Netflix? The platform understands what kind of content you typically watch, and according to your liking, it recommends similar movies or TV Series that you may enjoy.

A movie recommender helps an individual find more content that they may enjoy. It curates a specific list that can is unique to each person based on their liking. These recommendations may be based on browser history, based on what other people with similar demographics/traits are watching, and more.

Details about the project

This is one of the data science projects that will surely grab a lot of eyeballs! After all, who doesn’t like to be recommended movies or series on YoutTube or Netflix, that too in the area of your interest! To execute this project you can collect inputs from viewers who saw a movie first and classify their responses.

You can make use of the R programming language to create this movie recommender system. The right choice of dataset for this project will be MovieLens. It covers 58k movies and you can also avail packages like reshape2, ggplot2, and data.table.

6. Customer Segmentation

Language: R

What is Customer Segmentation?

The process of dividing a company’s customers into different groups (each group reflects similarity), is known as customer segmentation. The goal is to decide how we can relate the customers in each segment to maximize the value of the customers to the business.

Typically, customers are divided into segments based on the following factors:

Psychographic, demographic, geographic segmentation, and behavioral. However, there are other ways to divide the customer group as well. Companies perform customer segmentation because they realize that each customer group may have different needs. To satisfy the various requirements of different groups, a company must cater to their needs differently.

Details about the project

Businesses are always in the process of devising methods to segment their customers. The segmentation process ensures that the business can create consumer-specific strategies and create a product or service that suits their needs. This is a MUST-DO activity before running any online marketing campaign.

Customer Segmentation is a popular application of unsupervised learning. In this, clusters are used by the company to define and place its customers in different groups which are categorized on the basis of region, gender, age, preferences, and so on. This project is also useful to identify the inputs of annual incomes and spending trends of the customers to create a strategy for that segment.

Closing Thoughts

No data science project is difficult if you have adequate knowledge about the right tools and techniques. In fact, the practical application of any technology is best tested by working on a number of projects. It gives you the right amount of exposure and increases your problem-solving skills.

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March 19, 2023

Does Data Science require Coding or Not?

Data Science is a field that is a combination of mathematics, business and technology. In a constantly evolving field, the mathematical understanding of Data Science remains consistent. Now, it is a question of the rest. Let’s understand more:

Business: Data Science is a business-agnostic field. Whatever domain you come from, you can leverage your business knowledge to do better data science.
For instance, if you are from a CA background, you can help Fintech companies. In addition, given your strong understanding of financial data, you can understand more than most. However, based on your interest, it is possible to work in any domain of Data Science.
Technology : Technology is a field that keeps evolving every day. A lifelong learning mindset must be applied to keep up with the pace of technology.
Once we understand the foundational elements of technology, it becomes vital that we keep upgrading ourselves based on the latest technology, like by doing the insight data science Bootcamp.

Why Is Coding Required in Data Science?

Data Science is a field where experiments are carried out on data to help improve the quality or bottom line of the enterprise. We just use project specific tools to analyze data. Large volumes of data are generally present on a cloud platform, and a Data Scientist must perform analytics.

To do this, a Data Scientist needs to have a robust toolkit where they are free to experiment. Any experimentation, data manipulation and visualization should be possible to strive to achieve the end result. It’s not engineering; it’s actual science that consists of performing experiments, where some succeed, and most fail.

Coding is required in Data Science because:

Sourcing Data: Regardless of the cloud platform or source, code can help get the data from wherever it is stored. Code enables us to manipulate data while pulling it right from the start.
Data Transformation : Knowing how to code can help to manipulate, fix and transform the data as required — this can be done via multiple platforms. For instance, Python code can be applied on almost any cloud platform or tool.
Exploratory Data Analysis : The patterns in data can be deciphered with the help of code; it is vital to explore large datasets to understand the visible and hidden patterns.
Experimenting with Data : Working on different hypotheses to see if there is backing for a data-driven decision, can be done with the help of code.
Machine Learning & Modelling : Having the freedom to make models and perform machine learning on data, can be done with the help of code.
Visualization: Giving a Data Scientist the ability to visualize data in multiple ways is a powerful tool. It can transform how we go about solving a problem, as visualizing data can help business stakeholders make data-driven decisions better.

In this section, let’s go over some of the roles and the amount of coding that is required:

Data Engineer

A data engineer would need to be an expert in SQL or a data query language and understand the fundamentals of Python/R to manipulate data as required. A knack for attention to detail can help you become a better Data Engineer.

Machine Learning Engineer

A Machine Learning Engineer needs expertise in a coding language such as Python/R and understands the fundamentals of a querying language such as SQL. Value addition for this role is the fundamentals of Software Engineering, like basic Data Structures.

Business Analyst

Depending on the company you are applying for, this is a role that requires less coding. Understanding the fundamentals of SQL and a visualization tool such as Power BI and Tableau can help you become a better Business Analyst.

Data Scientist

A Data Scientist needs to know everything mentioned above. There must be a keen interest to learn, irrespective of the technology stack or problem. Data scientists must keep learning throughout their career, irrespective of platform, coding language, tools and technologies.

Python

Data scientists worldwide primarily use Python as their language of choice. It is a highly diverse language and fits nicely into multiple technology stacks companies use. Python also has excellent support from the developer community.

Without fail, it is asked in all Data Science technical interviews. The focus should be on mastering concepts and general logic rather than trying to become an expert in the syntax of Python. Language(s) simply enable you to implement logic.

SQL

Companies test SQL as a fundamental querying language skill. SQL enables us to query databases in a simple language. SQL is a reasonably intuitive language to learn and can be one of the first languages to pick up to give you the initial boost of confidence.

How Can You Start Learning Coding for Data Science

Sitting on the fence about if Data Science is for you? Have you been trying to understand how you can get a head start to get your foot into the door with Data Science? As your research might be pointing to gradually, learning to code is the best way to get into Data Science. There is an inherent fear of the unknown. Coding can seem daunting, like doing advanced algebra as a kid. However, it is simply a barrier you must overcome to be a good Data Scientist.

What Jobs in Data Science Require Coding?

All jobs in Data Science require some degree of coding and experience with technical tools and technologies. To summarize:

Data Engineer: Moderate amount of Python, more knowledge of SQL and optional but preferrable is knowledge on a Cloud Platform.
Machine Learning Engineer: More amount of Python, a moderate amount of SQL and a keen interest in experimenting with data.
Business Analyst: Strong understanding of business, knowledge of a visualization tool, minimal coding (depending on company profile for Business Analyst).
Data Scientist: End-to-end understanding of the data pipeline. Needs coding.

Conclusion

Now you understand whether coding is required for Data Science and the answer is a resounding yes! Many of these opinions have been formed, having spoken to over 2000+ people in Data Science. Depending on your nature and the role that you are going for, there are multiple ways you can and will pick up on coding!

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March 19, 2023

How Netflix Uses Machine Learning

Netflix Recommendation Engine

Netflix has become the largest TV streaming provider with over 220 million subscribers in 190 countries. Containing a consistent stream of over 4,000 programs, Netflix needed a way to customize the user experience to make it easier for their subscribers to find relevant programs.

They found their solution through an ML-based recommendation algorithm, allowing Netflix to curate the pages of every individual user with relevant content. Used to identify what a subscriber is watching, every piece of content on Netflix has a set of labels: genre, cast, rating, success and acclaim, etc. Netflix’s algorithm will look at a subscriber’s implicit and explicit data: watch, search, and rating history, which will be used to feed relevant content onto subscribers’ pages. Moreover, Netflix will look for subscribers with similar watch history and recommend a program to the entire group if enough like-minded subscribers have watched it.

Customizing user interface

Once Netflix gets the right content in front of subscribers, they are faced with another challenge–get the subscriber to click on the content that is being recommended. Their strategy to get subscribers to engage with recommended content is to adjust the movie posters to fit the interests of the user. The movie poster could feature a different set of actors/actresses to fit more in line with a subscriber’s interest, or an image to suggest the film is scarier for horror movie enthusiasts.

To achieve success in targeting movie posters to each subscriber, Netflix uses machine learning (ML). After trial and error with batch learning and A/B testing, Netflix landed on using contextual bandits to lead their ML framework. Contextual bandits test out a number of predicted actions and can learn which will have the most favorable outcome.

Netflix uses training data obtained from randomization in the model’s predictions. Netflix calls this their data operation stage–when the number of poster images and the number of subscribers that they use will be applied to dictate how broad the data exploration stage becomes. Posters are then randomly applied to different subscribers’ dashboards, and the contextual bandits learn which poster has the most favorable outcome for each kind of user.

Improve video quality

Netflix’s large subscriber base, both in number of subscribers and number of countries, access Netflix on different signals and devices. The challenge for Netflix has become to engineer a way to ensure the premium quality of their content.

Bandwidth and network quality are hard to predict, but huge indicators of the quality-level of the content streamed. Netflix’s goal was to be able to predict both in order to deliver their subscribers a better watching experience: time spent waiting for video to play, quality of content, and buffer time.

Netflix has made itself accessible on a number of different devices, which are constantly expanding. ML-based prediction algorithms are used to avoid any issues when deploying Netflix on a new device. Using subscriber’s history log of issues that have arisen by new devices, Netflix’s algorithm can determine if a given set of conditions is likely to cause a problem.

Closing

Netflix was able to increase user engagement using ML as a staple part of their software. Generating personal recommendations, displaying title cards fit for each subscriber, and deploying prediction models all lead to the high watch times across subscribers. Being the first streaming service to heavily use machine learning propelled Netflix to become the leader in the increasingly competitive streaming industry.

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March 19, 2023

Day: March 19, 2023

Top 6 Data Science Projects To Get You Hired in 2023

Top 6 Data Science Projects To Get You Hired in 2023

1. Sentiment Analysis

What is Sentiment Analysis?

Details about the project

2. Detection of the Parkinson’s Disease

What is Parkinson’s disease?

Details about the project

3. Detection of Fake News

What is Fake News?

Details about the project

4. Prediction of the Next Word

What is the Prediction of the Next Word?

Details about the project

5. Movie Recommender

What is Movie Recommender?

Details about the project

6. Customer Segmentation

What is Customer Segmentation?

Details about the project

Closing Thoughts

Does Data Science require Coding or Not?

Does Data Science require Coding or Not?

Why Is Coding Required in Data Science?

Data Engineer

Machine Learning Engineer

Business Analyst

Data Scientist

Python

SQL

How Can You Start Learning Coding for Data Science

What Jobs in Data Science Require Coding?

Conclusion

How Netflix Uses Machine Learning

How Netflix Uses Machine Learning

Netflix Recommendation Engine

Customizing user interface

Improve video quality

Closing