Data Science is the hottest topic in IT. With Data Scientists in high demand and salaries at an all-time high, more and more graduates from various disciplines are opting to upskill and do a master’s to further their career prospects.
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Why you should do a master’s in Data Science?
Wondering, why to do a master’s in Data Science? Then read on.
Earlier, technological advances were pushed by hardware. With the increase in processing power and computing, the focus shifted to harnessing software-driven applications for business insights to roll out innovative products and services and make lives easier.
With data being the oil of the digital economy, the immense potential of data cannot be ignored. From data science to artificial intelligence, technological advances are making it the most happening and exciting discipline today.
Data is critical for all organisations, and upskilling in data science offers immense job opportunities. You can apply for many job titles: Big Data Engineer, Business Analyst, Data Architect, Data Engineer, Data Analyst, Data Scientist, Machine Learning Engineer, Data Science Generalist, Data Visualisation Specialist, Business Intelligence (BI) Architect, Business Intelligence Engineer. All of these have various job responsibilities and varying career scopes. Isn’t this reason enough to go for a post-grad course in Data Science?
In a post-grad course you understand machine learning and apply with coding. You learn about data architecture and the application of various programming languages and tools to attain the Data Science objectives. These skills prepare you for any of the above job roles.
It has almost become a cliché that Data Scientists are in huge demand, and hirers don’t find enough graduates to fill their openings. In this scenario, a post-grad in Data Science stands a good chance of getting hired, with good salary prospects. That means that with a Master’s in Data Science, you double your job prospects. Moreover, it paves the way for a Ph.D. in the future.
Ten Data Science Projects for Beginners and Experts
Here are some projects for you to practice and build your portfolio:
1. Building Chatbots
Chatbots are an important customer-facing tool for engaging with customers in real-time and handling queries without any business slowdown. By automating the process of customer queries through the website, chatbots reduce a lot of the daily workload. At the same time, they convert many casual queries into sales.
Chatbots can be trained using Recurrent Neural Networks and the implementation is handled on Python.
2. Fraud detection
Language: R. Python
Credit card frauds are common, and banks are constantly trying to find ways to detect fraud and take action before the loss occurs. Credit card frauds are detected using various methods like AI and Machine Learning.
The credit card customer’s usual transaction and card behavior are mapped, including location and transaction amounts. This helps to identify any fraudulent transactions that are deviant from the normal. Using the customer transaction as the dataset, it can be input into decision trees, Artificial Neural Networks, and Logistic Regression for the project.
3. Fake news detection
The proliferation of fake news is turning into a nightmare for product and service owners, and the government. As fake news often overturns and overshadows genuine news, it becomes difficult to separate fake news from genuine news. Fake news from unconfirmed sources causes problems and has the potential to cause panic or violent incidents.
Fake news can be detected by building a model to separate the real news from the fake, using Python libraries like Pandas, scikit-learn, and NumPy.
4. Designing a Recommender system
YouTube, Netflix, Amazon Prime, and others use systems that make recommendations to the user based on his behavior, preferences, viewing habits, and so on. They also consider metrics such as previously watched shows or videos, most-watched genre, ‘liked’ videos, watching frequency, duration, etc., and feed them into a machine learning model to create recommendations.
You can build a content-based or collaborative filtering recommendation system.
5. Forest fire prediction
Forests fires have become very common. And wildfire prediction systems are much sought after by the local authorities for early warning mechanisms. Wildfires are sudden uncontrolled forest fires that cause damage to forest trees and wildlife as well as property. You can create a project using k-means clustering to identify fire hotspots, predict the speed of spread, and the areas of potential damage. This warning system can be used for local authorities to prepare and allocate disaster management resources as required.
6. Customer segmentation
Ecommerce, real estate, media houses, and other customer-facing businesses need to know their customers to serve them better. They can offer personalized services, make customized offers and discounts based on the customers. It creates a differentiator for a business edge in highly competitive industries. For this, they need to segment their customaries according to pre-defined categories.
For this project, you can use unsupervised learning to segment customers into clusters like age groups, gender, interest areas, spending habits, browsing behavior, and so on. You can also use k-means clustering.
7. Sentiment Analysis
You need to know natural language processing (NLP).
In social media or forums, you come across opinions and feedback, which is important for a product or service owner to understand the sentiments of the users to improve the product/service for a happier audience. Sentiment analysis helps you to identify, and analyze user opinions like happiness, excitement, anger, like, negative/positive, etc. These opinions may be sourced from online reviews, survey responses, YouTube, Twitter, or Facebook. Companies gain from a sentiment analysis tool as they get an insight into the people’s reaction to a new product launch or a shift in a user policy.
8. Covid-19 data analysis
COVID-19 data processing is necessary to keep track of daily cases by date and region and the number of confirmed deaths. Data processing is critical for public health policy and planning distribution and logistics of medicines, oxygen, and critical health care. Use Python to get cumulative data of the number of Confirmed Cases, Deaths, Recovered and Active, as well as date wise and region-wise New Cases, New Deaths and New Recovered.
This is an excellent guide for the project.
9. Uber’s pick-up analysis
In this project, you explore and analyze Uber pickups. First, answer questions like Uber pickup and distribution, the time when Uber pickup happens in normal times, days when pickup happens, zone-wise pickup distribution, maps of clusters and hotspots, etc.
10. Housing price prediction
Language: R, Python
House Price prediction is important for Real Estate efficiency. Earlier, house prices were calculated with the acquisition and selling price in a neighborhood. But now, you can use various algorithms to explore how much is the worth of the houses. Linear regression LASSO and gradient boosting algorithms are the favored algorithms to predict house prices.
This is an excellent resource to start with.
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