Suman is a Data Scientist working for a Fortune Top 5 company. His expertise lies in the field of Machine Learning, Time Series & NLP. He has built scalable solutions for retail & manufacturing organisations.
Whether you are a student or a working professional, the journey to be an industry-ready Data Scientist would never be fulfilled unless there is a good portfolio of projects considerably demonstrated in the resume.
There are hundreds of online courses and certifications out there which makes it nearly impossible to choose the right one. However, the Data Science Bootcamp Curriculum provides best-in-class data science content which would significantly prepare you for the market.
Choosing the right project is crucial as it could set you apart from the other Data Scientists. To understand the right set of projects to do, you could enroll in the best Data Science Courses in India.
In this blog, we will cover the top 5 data science projects in python you could do to elevate your skills and knowledge in this competitive Data Science market. You would also find an exhaustive list of projects in the Knowledgehut Data Science bootcamp curriculum.
Problem Statement - Manufacturing industries rely on small, medium, and large-scale equipment to drive their process and aid in the preparation of final product. Hence, it becomes utmost important to ensure that the machines operate in their normal operating regime without fail. However, in real life scenario, that’s not the case quite often, and as a result equipment breaks resulting into the disruption of supply-chain. To prevent such occurrence, it’s necessary to build a robust health monitoring system which would generate alerts in real time. It’s one of the most important python data science projects in an industry.
Solution Approach – Most hardware devices have sensors attached to them. Those sensors generate real-time data of several parameters such as temperature, vibration, pressure, and so on. These IoT sensors generate real-time data which are validated by the operators in an industry to determine the working condition of any system. Building a real-time anomaly detection system that would track the health of these sensors and generate alerts if any or all of them start to behave abnormally. We could build a linear model if labelled data is available of past anomalous records. In case the labels are not present, we could use several unsupervised algorithms such as Isolation forest, Variational Autoencoder, One Class SVM, etc., to detect anomalies. The alerts could be classified into normal, warning, and danger groups. An email or SMS notification tool could be built on top of this which would allow the operator to get instant notification whenever the status changes. Additionally having features like sensor type, brand, manufacturer, buying date, etc., would also help in predicting the remaining useful life of an equipment.
Get to know more top industries for a data science professionals.
Problem Statement - The ability to adjust and monitor a given process to get a desired output is referred to as Process Control. Manufacturing plants need a system which could streamline its process and ensure smooth operation. Manual control of such processes is prone to bias and are less generic in nature.
Data Science and AI based controls are data-driven which gathers experience from the historical operations and works without manual intervention. It is cost-efficient and improves overall performance by giving a closed loop system control.
Solution Approach – An industrial application of Process Control could be in Steel Manufacturing Industry. To prepare final product from a blast furnace, we need to control certain parameters like heat loss, hot metal temperature, tensile strength, elongation, and so on. Building a machine learning model to capture historical patterns of operation of these parameters and recommending certain range of operation by considering different factors in real time would enable the process to run smoothly and produce good quality product. A Decision tree model would be a good starting point followed by a more complex deep neural network to capture the non-linear patterns in the data. A feedback loop could be added on top of this wherein the user could approve or reject the model generated recommendations. This would allow the model to update its weights and re-align based on user needs.
Problem Statement - Companies these days who rely on manual pricing systems are leaving out the effects of several exogenous factors which are crucial in setting up the right prices of their product. Retail giants like Walmart, Amazon, Target depend heavily on product prices to increase sales and generate revenue at the year-end. Hence, it’s crucial to get rid of the manual pricing system and set up a dynamic engine to enable smart pricing. Price elasticity is one such application of smart pricing where the variation in demand pattern of a product is estimated by different price points.
Solution Approach – A product is said to be highly elastic if its demand changes with a slight change in a price point whereas products that are inelastic don’t change too much in demand despite price changes. Price Elasticity is a time series problem where sales, price, holiday, and other exogenous variables of a product are considered, and its demand is forecasted. The variation in demand reflects the volatility of a product in reference to a price change. A naive approach could be a simple univariate moving average of the price points followed by a more complex multi-variate solution leveraging advanced time series algorithms such as Prophet, BSTS, etc. If a product is inelastic, its price point could be set marginally higher than earlier without disturbing the sale volume. This would ensure consistent sales with higher revenue. However, for an elastic product, the price point should reflect the business objective as marginal change could have a large impact on sales. Such a dynamic pricing system is the core of any product firm that wants to have an edge over its competitors.
Know more about what is factor analysis in data science.
Problem Statement - One extension of Price Elasticity use case is Demand Forecasting. Companies who have their own product needs to make financial plan for the fiscal year. They must set the right inventory, resources, infrastructure in place for the upcoming year. However, estimating such labor and dollar value could be a challenge if the sales or order estimation for the foreseeable future is not determined. Hence, forecasting the right demand of a product is important as it makes planning better.
Solution Approach – Demand Forecasting is one such use case applicable across any domain. We would need product information such as price, order, inventory data such as lead times, external factors such as holidays, special events, etc. While building a demand forecasting model, it’s important to check for a cross-product relationship as well. This would ensure that the model is scalable. Moreover, determining the right metric to validate the forecast is crucial and it should align with the business goals as well. The use of metrics such as bias, over-forecast, under-forecast, etc., should be leveraged as it gives better reflection into the model performance.
Problem Statement - A report by Business Insider states that the Recommendation Engine of Netflix is worth around one billion dollar per year.
When an user logs in their Netflix account, certain shows or movies are recommended by the streaming platform. Once, they start interacting with the recommended shows, Netflix captures it’s behavioral patterns and generates similar recommendations based on their interests. It’s a continuous process where the algorithms behind the scenes are learning new patterns every time a user views or clicks a show in the platform. This process of getting recommended similar movies is driven by Machine Learning algorithms which understand the behavior of a user and recommends accordingly. The recommendation engine is one of the most important use cases deployed across many e-commerce and internet companies. A poor recommendation engine could lead to massive business impact.
Solution Approach – To build a recommendation engine, we need to leverage user behavior data. It could range from user’s demographics to the URL clicks or the page visits by the user. A recommendation engine could be based on Content-Based Filtering or Collaborative Filtering. In Content-Based Filtering, only the user’s own information is captured whereas in Collaborative Filtering the relation with other users is also modeled and recommendation is given based on other’s behavior as well. Recently, various state of the art deep learning methods is also being developed for recommendation systems.
Problem Statement - Whenever a customer visits an e-commerce website, the purpose is to buy a specific product. To execute that, the user could type such textual description of the product and get recommended similar items. These text-based search has certain limitations specially in home décor, & fashion domain as it’s difficult to find the exact description of the product the user wants. These challenges could be mitigated using a Visual Based Product search which gives the flexibility to upload an image or a photograph and get recommended items which matches to that uploaded image. Moreover, Visual Search is trendier and appealing to the young shoppers.
Solution Approach – A visual search-based tool would require huge corpus of image data of various products to train the model on. We would build a two-layer pipeline. In the first layer, an object detection model such as YOLOv5 would detect the category of product to search (i.e., top or a shirt), The identified object would then be vectorized and compared against the vectors of all the other images within the same category. There are several models such as ResNet, VGG which could be used to generate embeddings. The images with the highest cosine similarity against the query image would then be recommended to the customer. Visual Search could be extended to other use cases such as automated image tagging.
There are different types of projects we discussed in this blog which are applicable across various domains. E.g. - Someone working in Manufacturing Industry could consider picking up use cases such as Predictive Maintenance, Process Control, etc. Additionally, a recommender system could be deployed across various industries. Doing some or all would put in a more competitive position in the job market.
Python is the preferred language among analytics professionals to build pipelines, models, and dashboards. It’s simplistic in nature and provides a wide range of functionalities which makes it the choice of programming language for a Data Scientist.
The libraries and packages in Python provides various rich set of functions that are used by Data Scientist in their day-to-day to work. Simply importing those libraires could solve very complex problems easily and bring value to the project.
Python is a very big language with a diverse set of topics. A knowledge of basic data structures such as list, sets, dictionary, tuples & strings are required. Alongside, the data analytics libraries like Pandas, Numpy, Matplotlib, sklearn, plotly are one of the most widely used topics in Data Science.
A python Data Scientist would spend time on data cleaning, exploratory analysis, feature engineering. Additionally they would build Machine Learning models and prepare production ready pipeline to deploy models.