Data is critical for every organization as it empowers the business with insights for solving problems, making decisions, and making strategic plans. In a digital world where massive data from multiple sources is generated in real-time, data science is needed to process and interpret the data and make business decisions.
Organizations are recruiting data scientists to find relevance in their data and help them have a competitive edge. They are recruiting data scientists in various data scientist job roles and numbers are rising every year. A LinkedIn report says hiring in data science job roles has grown nearly 46% since 2019.
If you want to carve a career in a data scientist job role, begin with the PGP Data Science course in Pune. Add to your job experience with a PG certification and watch your career grow in one of the hottest disciplines today.
What is Data Science?
Data Science is the technique of applying analytical methods and sophisticated technologies to extract insights for data-driven decisions. It crunches vast amounts of structured and unstructured data to analyze critical information for actionable plans and solving business problems. Data Science uses the latest in technical tools and software, together with social science, to understand the context of the data for mission-critical tasks, operational efficiencies, and a business edge over the competition. It uses scientific techniques, data frameworks, algorithms, analytical tools, and procedures to dig out insights from the data, regardless of volume, velocity, and variety of data sources.
Why learn Data Science?
Data science jobs have multiple roles – data engineer, machine learning engineer, data analyst, data science generalist, data architect, and more. The pursuit of data science as a career allows the aspirant to apply for any data scientist job role suited to his profile, education, and interests. The salary structure is high, and growth prospects are quick for candidates with many tools and technologies in their toolkit arsenal.
The data scientist job is challenging and mentally satisfying as it gives you decision-making power within the organization. You also get to learn diverse skills on the job, both technical and non-technical. As data science is a comparatively new field, there is a gap between the demand and supply, creating an opportunity for the certified data scientist to get hired almost instantly and enhance his career profile.
Top Data Science Projects for Beginners
To be a data science professional, the perfect way to begin is by starting on some real projects. Move beyond the theoretical knowledge to practical projects that test your grasp of the core concepts. Begin with simple projects. Build confidence and move on to more complex ones.
Projects are the highlight of your portfolio and help you land your dream job. Projects test your working knowledge and implementation of statistics, programming, and algorithms.
Here is a list of beginner projects for you to get started:
Deep learning number recognition
This data science project uses the concept of computer vision. It also teaches you the fundamentals of neural networks and classification methods. In this project, you identify digits from a dataset of thousands of handwritten images.
Fake news detection
This is very popular as fake news is a hot topic in media and social media. Building a capability to filter fake news feeds or content from the rest is a great beginner project. The project also tests your understanding of natural language processing. Python can be used with the help of NumPy, Pandas, and sci-kit libraries.
Sentiment analysis
Sentiments and opinions are evaluated to understand whether they reflect positive or negative sentiments or dissatisfied unhappy customers. This insight into sentiments is known as sentimental analysis. The categorization of sentiments is either binary (optimistic/pessimistic) or multiple (happy/angry/dissatisfied/sad, etc.).The project can be executed in R Language, and the results presented in a word cloud.
Credit card fraud detection
Credit card is one of the most popular financial transactions worldwide, and credit card frauds are common. They are a type of identity theft and pain points of financial institutions. As fraud occurs in moments, detection methods must detect the fraud before substantial damage occurs. The project studies customers’ spend patterns, spending by location, and periods, to spot fraudulent transactions from genuine. The project of credit card fraud detection can be done using machine learning algorithms to develop a classifier. Then, implement in real-time to detect fraud almost instantly. Besides, R language or Python can also be used for the project. A customer’s transaction can be ingested as decision trees and artificial neural networks or logistic regression used for detecting fraud.
Customer segmentation
Customers are grouped into segments based on their spending patterns, customer behavior, amount of spending, etc. Customers with similar behavior are segmented for targeted marketing and promotional campaigns for higher sales. Clustering algorithms in machine learning can be used for grouping similar customers, using attributes such as gender, age, and spending score (high, low, medium). Customer segmentation also uses unsupervised learning. Gender and spend patterns can be interpolated using K-means clustering. There are various ways to do this project. The ultimate goal is to demonstrate your practical knowledge of the concepts you have learned.
Character recognition
The recognition and classification of handwritten data is another beginner’s project in data science. The project utilizes convolutional neural networks. The MNIST dataset is a good place to start as it has the required data of handwritten images that enable the calculation of the percentage match for any character.
Movie recommendation
Recommendation engines are a very popular application of data science. They are used for movie and music recommendations, YouTube, content streaming sites, eCommerce, and even Internet searches. You can undertake any simple project based on content-filtering, like a movie recommendation engine that suggests movies to watch based on customer viewing habits. The project can be made in Python.
To get ideas about what data science projects to work on, follow these techniques:
- Check out Github
- Subscribe to data science blogs
- Adopt a problem-solving mindset in your current job place.
- Get familiar with as many tools as you can to develop your data science toolbox.
- Learn Python, R language
- Work on your statistics and mathematics skills
- Start learning machine learning.
- Trawl the web to discover data, tutorials and begin working on projects.
Conclusion
Ultimately, a good grasp of theoretical knowledge is necessary. At the same time, working on projects helps you fill the gaps in understanding and give you practical experience working on various languages and technologies. The above data science projects have their source code on Github and allow you to get started right away.