Having hands-on experience is considered more valuable today, which is for the best because proactive students get a one-up over everyone else through all their practical knowledge in the field. Data Science is no exception to that rule. It is considered one of the most pragmatic fields out there, and in order to grow in the same one needs a lot of hands-on experience to be able to tackle the work, pressure and all successfully. For the sake of this article, let me reiterate what Data Science actually is – in its most basic terms, Data Science is applied to various fields where it provides insights and information, and anything of value from a sea of data. Pretty straightforward, right?
For organic growth in this field, it has become a prerequisite to be have created innovative solutions, something beyond merely having a specialisation in Data Science. To have a portfolio that stands out and that can only be achieved through participation in data science challenges and using the diverse datasets provided, and produce solutions for the problems posed. Sounds a little overwhelming, no? Do not worry, here are 7 project ideas that will not only help you check everything from the pragmatic experience checklist, but also impress your audience (here: the hiring manager).
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- Forecast a supermarket’s sales on a major Holiday (Holi, Diwali, etc.):
A supermarket has numerous departments, so, using Data Science, you could predict which departments are affected mostly by holiday, and what is the scope of that effect. For this, you can use the historical dataset of the company.
- Movie recommender: The objective of this challenge is pretty straightforward- make suggestions for movies to its users. For this, you can use the Movie Lens Dataset. It is one of the most quoted datasets in Data science. This project will help you dive a bit deeper into how your favourite streaming platform works, and who knows, maybe an idea to improve the existing system strikes you?
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- Predicting the traffic on a new mode of transport: This project will allow you to predict the traffic and footfall on any new mode of transportation and give their two cents on how to increase and decrease the same. For this, you can use the Time Series Analysis Dataset. This dataset is also a popular go-to among students. It can be used in an array of fields— predicting sales, the weather, yearly trends that come up etc. The dataset that is specific to time series, where the challenge is to forecast the traffic on any mode of transportation in the city. This whole exercise includes rows and columns.
- Predict the age of actors:
If you want to dive deeper into Deep Learning, then should be your ideal starting point. For this, you can use the Age Detection of Indian Actors Dataset. It contains thousands of images which are manually selected and cropped from videos, so you can expect some variety in scale, expressions, resolution, and more.
- ImageNet Large Scale Visual Recognition Challenge (ILSVRC):
The two objectives of this challenge are to localise the objects and the detection of objects from the videos. It makes for a compelling challenge as it creates the best algorithm for the detection of objects and classification of images on a large scale. The primary aim of the competition, which is held annually is the comparison of progress in the area of image classification and detection, along with the merging of excellent research with more data. It also measures progress made in indexing for annotation and retrieval from computer vision.
- Predict the survival rate from all the passengers that the RMS Titanic had on board:
The Titanic Dataset provides the data on who was aboard the RMS Titanic when it met with its catastrophic end on the 15th of April, 1912 after colliding with an iceberg in the Atlantic ocean. It is perfect for beginners and is also the most commonly used one. With 891 rows and 12 columns, the set provides the variables and their combination based on personal characteristics such as the sex, age, class of the ticket, and test the classification skills.
- Answer open-ended questions about images:
This one goes out to all the Computer Vision enthusiasts. For this, you can use the VisualQA Dataset which contains more than 200,000 images, 3 questions per image, and 10 ground truth answers per question. Your task will be to use your understanding of Computer Vision and answer the open-ended questions present in the said dataset.
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How to make a good Data Science project?
The following points should be kept in mind before starting any Data Science project: Choose the programming language that you are comfortable with. However, the language chosen should be one of the in-demand languages such as Python, R, and Scala. Use datasets from trusted sources. You can use Kaggle datasets. Moreover, make sure that the dataset you are using does not contain errors. Find errors or outliers in your dataset and rectify them before training your model. You can use visualization tools to find the errors in your dataset.
Describe the major components that a Data Science project should have.
The following components highlight the most general architecture of a Data Science project - Problem Statement is the fundamental component on which the whole project is based. It defines the problem that your model is going to solve and discusses the approach that your project will follow. Dataset is a very crucial component for your project and should be chosen carefully. Only large enough datasets from trusted sources should be used for the project. The algorithm you are using to analyze your data and predict the results. Popular algorithmic techniques include Regression Algorithms, Regression Trees, Naive Bayes Algorithm, and Vector Quantization. Training Models involves training your model against various inputs and predicting the output. This component decides the accuracy of your project. Using proper training techniques can produce better outcomes.
What are the skills required to be a Data Scientist?
The following are the essential skills and tools any Data Science enthusiast should master - statistical Skills including Probability, Analytical Skills to analyze and test the data , Programming languages such as Python, R, Scala, and JAVA, Data Visualization Tools such as Power BI, Tableau, Algorithms including Regression, Decision Trees, Bayes Algorithm, Calculus and Algebra, Communication and Presentation Skills, Databases such as SQL, Cloud Computing to manage the resources. Apart from these technical skills, a professional Data Scientist should also have some soft skills to provide value to the company and improve interpersonal relationships. These skills include critical and curious thinking, business orientation, smart communication skills, problem-solving, team management, and creativity.