In this blog post, we’ll explore five project ideas that can help you build expertise in computer vision, natural language processing (NLP), sales forecasting, cancer detection, and predictive maintenance using Python.
As a data science student, it is important to continually build and improve your skills by working on projects that are both challenging and relevant to the field.
Computer vision with Python and OpenCV
Computer vision is a field of artificial intelligence that focuses on the development of algorithms and models that can interpret and understand visual information. One project idea in this area could be to build a facial recognition system using Python and OpenCV.
The project would involve training a model to detect and recognize faces in images and video and comparing the performance of different algorithms. To get started, you’ll want to become familiar with the OpenCV library, which is a powerful tool for image and video processing in Python.
NLP with Python and NLTK/spaCy
NLP is a field of AI that deals with the interaction between computers and human language. A great project idea in this area would be to develop a text classification system to automatically categorize news articles into different topics.
This project could use Python libraries such as NLTK or spaCy to preprocess the text data, and then train a machine learning model to make predictions. The NLTK library has many useful functions for text preprocessing, such as tokenization, stemming and lemmatization, and the spaCy library is a modern library for performing complex NLP tasks.
Sales forecasting is an important part of business operations, and as a data science student, you should have a good understanding of how to build models that can predict future sales. A project idea in this area could be to create a sales forecasting model using Python and Pandas.
The project would involve using historical sales data to train a model that can predict future sales numbers for a particular product or market. To get started, you’ll want to become familiar with the Pandas library, which is a powerful tool for data manipulation and analysis in Python.
Cancer detection with Python and scikit-learn
Cancer detection is a critical area of healthcare, and machine learning can play an important role in this field. A project idea in this area could be to build a machine learning model to predict the likelihood of a patient having a certain type of cancer.
The project would use a dataset of patient medical records and explore the use of different features and algorithms for making predictions. The scikit-learn library is a powerful tool for building machine learning models in Python and it provides easy to use interface to train, test and evaluate your model.
Predictive maintenance with Python and Scikit-learn
Predictive maintenance is a field of industrial operations that focuses on using data and machine learning to predict when equipment is likely to fail, so that maintenance can be scheduled in advance. A project idea in this area could be to develop a system that can analyze sensor data from the equipment, and use machine learning to identify patterns that indicate an imminent failure.
To get started, you’ll want to become familiar with the scikit-learn library and the concepts of clustering, classification and regression, as well as the python libraries for working with sensor data and machine learning.
In a nutshell:
These are just a few project ideas to help you build your skills as a data science student. Each of these projects offers the opportunity to work with real-world data, use powerful Python libraries and tools, and develop models that can make predictions and solve complex problems. As you work on these projects, you’ll gain valuable experience that will help you advance your career in.
As a beginner in data science, one of the hardest things is to land their first job and to build an impressive portfolio. We are all aware of the vicious cycle of not getting a job because of no experience, and no experience because of no job.
Most of us get stuck in this cycle either when we are starting our careers or when we are transitioning into another career. A career in data science is no different, but the question arises of how to break through this cycle and land your first job.
To answer this, Data Science Dojo collaborated with Avery Smith to conduct a webinar for every beginner in data science who is stepping into the real world. He discussed some useful tips to help data scientists build a data science portfolio.
Avery’s secret to breaking into the data science industry is through “Projects”, which you can create to show off your skills and knowledge in your next interview. In this session, Avery took us through the best practices for creating a project that makes you stand out and helps you land your dream job.
5 tips to create the best projects to improve portfolio
1. Choose the right topic
Choosing a topic that you can write passionately about is very important because that is the only way you will feel motivated to finish the project. If you are wondering where passion comes from, it could be something out of your hobbies or your next/dream job. The fun trick taught by Avery is to think about any hobby or industry you are passionate about.
Next, go to your LinkedIn job section and search for data-related roles in the fields you are interested in. After that, find a job or company that you would like to work in, and scroll down to look for the qualifications required for that job.
For instance, if the job requires SQL, Python, and Tableau skills, you should create a project that involves these three. You will also look at what the company does and its job requirements, to make your project as relevant as possible.
2. Get good data
If you have successfully decided on a topic to work on, now you must be thinking about where to find relevant data. There are four main ways of gathering data, as Avery pointed out:
Using an API
Collecting your data
These four ways are mentioned in order to increase the difficulty to get each and the more unique it is. Although downloading a CSV is easy, it’s not overly impressive. Collecting your own data is exceedingly difficult but is unique and will make a larger impact in showing off your skillset.
3. Decide on the type of project
There are three types of projects:
Skillshare- a few steps in Python or a SQL query or a graph in the dashboard. It’s not like a whole project but a section of the project.
Data Story- a whole paragraph with multiple lines of codes, and multiple graphs which is more like a complete article.
Product- a tool or app that you can give to someone, and they can use it.
The types are in order of increasing difficulty and impressiveness, skill share is easiest to do but not very impressive while on the other hand product is very difficult but highly impressive. In the webinar, Avery explained these using examples for each type of project.
4. Focus on visualization
Visualization is one of the easiest to do, looks impressive, and you can start it today. For beginners who feel like they are not ready to work on a big project, data visualization is something you can start working on day one. There are several tools and software available which are easy to learn and can help in creating amazing projects, you can learn more about visualization tips and techniques.
5. The best project is the one you can finish
Many data scientists have several projects that they started but never got the chance to finish. A very little-known fact is that these projects can become their marketeers by attracting recruiters and helping them land the right job. For that you need to get these projects out there, nothing is going to happen if keep them restricted to your computer.
For this reason, we need to finish and publish these projects. Avery’s advice has been to avoid the scenario where you have several unfinished projects and you decide to start another, the goal is to have published projects. To better understand it, Avery introduced us to the concept of Modular Projects.
What are modular projects
Avery explained the concept of modular projects with marathons. People who run a marathon don’t do it all at once. First, they run 5k, then 10k, maybe a half marathon, and probably then they can run a full marathon. Similarly for a project, don’t go for a marathon project off the start. Instead, start with 5k.
You can always imagine a marathon, but try to reach a 5k first, publish, and then move ahead for a 10k. The idea of a modular project is to pick a low finish line and work your way up.
In nutshell, Avery provided all beginners with a starting point to enter their careers and prove themselves. This is your sign to start building a project right away, considering all the tips and tricks given in the webinar.
The best data science toolkit to help you succeed. Find leading blogs, podcasts, YouTube channels, project ideas, and numerous other data science resources in one place.
100s of 1000s of people are everyday planning to get started with their data science journey while most of them are actively & continuously looking out for data science resources & sites, to begin with. With tons of resources & sites available, one might wonder where to get the most useful & up-to-date data science material
And, if you are one of those, then you have landed on the correct page because I have curated a list of resources that have helped me a lot to learn data science & should probably help you out too.
Data science blogs
Being a data scientist, you would want to stay updated with the recent happenings in data science, machine learning, and artificial intelligence. There are some quality blogs producing engaging and interesting content day in day out. These blogs can also be regarded as excellent data science resources.
It has always been on the top of my list; they provide new blogs on data science, machine learning, artificial intelligence, and analytics on a routine basis. So, if you need a new data science blog frequently or on daily basis then KDnuggets is your option to go with.
With towards data science, you can clarify your data science, ML, and AI basics & fundamentals. Additionally, they provide a wide range of blogs on statistics & mathematics that can further aid your learning journey. Link to site: https://towardsdatascience.com/
4. Data Science Dojo
From big data to data analytics to statistics, Data Science Dojo is providing some useful blogs on several different areas of data science. Though, the blogs are limited in number but are highly recommended for all those who are just getting started with data science. Link to site: https://datasciencedojo.com/blog/
5. R Bloggers
Undoubtedly, you can find some most amazing blogs on R, Python, regression, and statistics here. Hence, if you are looking for clarity in any of the aforementioned areas, then R bloggers is your way to go. Link to site: https://www.r-bloggers.com/
Online forums and communities can be a really interactive way of learning data science. With a number of enthusiasts all over the world, these online spaces can serve as a resource for staying on track and updated, being a valuable contribution to your data science toolkit.
One of the most useful online communities for data scientists & practitioners; with Kaggle you can learn some of the most essential Python, machine learning, and data science concepts.
There are a number of YouTube channels sharing the concepts of data science. You, obviously, will not be able to go through all the videos. Here are some of the best data science resources when it comes to YouTube.
1. Ken Jee
Following his channel can make it a lot easier for you to break into the field of data science. Ken Jee shares his own learning experience & makes some useful career-related suggestions.
Are you new to the field of data science? Then Data Science Dojo can help you with learning some of the most significant concepts of machine learning, artificial intelligence, Python programming, and R programming.
For more Data Science related information, check out our other blog posts.
Data science podcasts
Podcasts are an excellent way of staying updated in the world of data science. I have listed a few useful data science podcasts on Sound-Cloud, Apple Podcast, and Spotify to help you learn and make the most out of your time.
a. Data Skeptic
These podcasts from data skeptic will bring amazing tutorials on statistics, machine learning, big data, and data science. Start learning now with them.
The world of data science is not just about writing code and building models. Data science has a lot of influence, both directly and indirectly, on the entertainment industry. In fact, movies and tv shows can be used as one of the data science resources by aspiring scientists and engineers. When you need to freshen up or take a break from tough problems, these movies can be of great help.
1. Minority Report (2002)
An action-thriller directed by Steven Spielberg, starring Tom Cruise. We have generally seen data being used to infer new information, but here the data is being used to predict crime predisposition.
2. Interstellar (2014)
Christopher Nolan’s cinematic success won an Oscar for best visual effects and grossed over $677 million worldwide. The movie includes quadrilateral robots like TARS & CASE that are true examples of the that we have made within the AI domain.
3. The Imitation Game (2014)
The move is based on the real-life story of Alan Turing & also describes the process of creating the first-ever machine within the field of cybersecurity & cryptography.
4. The Queen’s Gambit (2020)
One of the most popular Netflix series, with over 62 million viewers, tells the story of Beth Harmon; a made-up chess star who beats all odds in life from being orphaned as a child to battling drug addiction & chess competitions. Though the series is not really related to data science, but how Beth mentally plays the game by visualizing the chessboard on the ceiling is much like how an AI system works. For the past few years, AI researchers are trying to build a computer-generated bot version of Beth.
Top data science books
Books are one of the best additions to any individual’s data science toolkit. There is an immense amount of literature out there, helping aspiring data scientists clarify some concepts and acquire valuable information.
1. An Introduction to Statistical Learning- With Applications in R
This book provides an overview of the field of statistical learning, which covers essential tools that can help in handling vast data sets varying from biology to marketing to finance.
2. The Hundred-Page Machine Learning Book
This book covers a wide range of topics in just 100 pages. Some of machine learning’s core concepts are explained here in just a few words.
3. The Cartoon Guide to Statistics
By using cartoons & humor, the author explains some of the essential statistical concepts that one might find difficult to comprehend. This book is highly recommended if you are just getting started with data science & statistics.
4. Forecasting- Principles & Practice
Making decisions based on the future forecasts is required at several instances, for example, whether to build up a new power plant in the next five years or not? Such decisions can only be based on forecasts. This book can assist you with understanding the basics & principles of forecasting.
Data science newsletters
Similar to blogs and podcasts, Newsletters can be a valuable addition to your data science toolkit. You’ll get curated articles at regular intervals to stay on top of things.
1. Mode Analytics
This collaborative platform combines SQL, Python, and R together in one place. You can subscribe to amazing data science-related newsletters with mode.
They provide a weekly newsletter on a wide array of topics including data, programming, AI, infrastructure, Ops, data science, and ML. With them, you are subscribing to blogs & articles that are relevant to you & your learning.
LinkedIn can serve as another top data science resource, particularly if you’re looking to read short, engaging articles and get inspired by the stories of individuals. The pages listed below are worth following.
1. Machine Learning Mastery
You can find some useful machine learning articles & resources here that can help you to get started with applied ML. So, if you are into ML then Machine Learning Mastery is your place, to begin with.
2. Towards AI
With having 1800+ contributing writers from university professors to industry experts, they have a wide range of articles on tech, science, mathematics, engineering, and the future. If you are looking for some high-quality articles, then start scrolling through them.
3. Machine Learning India
Looking for useful infographics & PDFs? Then start following Machine Learning India because they have a ton of useful infographics, data science PDFs, and cheat sheets.
4. Data Science Dojo
Are you new to data science? Do you need daily content? Then I highly recommend you to start following Data Science Dojo. They share useful data science resources; be it an infographic, a cheat sheet, a blog, or a joke for humor. It doesn’t really matter if you are a beginner or an expert in the field, they have the right mix of content for everyone. Adding on, their weekly polls can help you test your data science skills, while their frequently held online webinars can help you with enhancing your knowledge.
5. Data Science Central
Similar to their blog, they have amazing data science articles on their LinkedIn profile as well. If you are a LinkedIn Freak, then you should start following their page now.
Data science free tools
A data science toolkit devoid of tools and software is not really a toolkit, to be fair. There are some quality tools out there, including open-source software, that a data scientist can benefit from. Here are the best data science resources in the realm of software applications.
It is a free & open-source software library for machine learning. TensorFlow is commonly used for neural networks, though, it can be used for a wide range of tasks.
It is used for the scientific computing of Python & R programming languages, which helps in package management & deployment. The distribution includes data science packages for Windows, Linux, and macOS.
This amazing product of google allows anyone to write & execute random Python code through the browser. Generally, it is a good fit for machine learning, data analysis, and education. Additionally, colab is a hosted Jupyter notebook that requires no setup & provides free access to computing resources.
The world of data science is nothing without practical experience and real-world projects. In your data science toolkit, therefore, you should have some quality projects. This will not only help you gain valuable experience but also strengthen your portfolio.
1. Beginner Level
a. Fake News Detection
If you are new to data science then this project can assist you to level up your data science career. Using Python, you can detect false & misleading news across social media & online channels.
b. Forest Fire Prediction
Using K-means clustering one can identify the hotspots of forest fires & severity, which can help in lessening & controlling the ecosystem damage.
c. Twitter Sentiment Analysis
One of the widely used text mining techniques, this project includes sentiment analysis of the text (tweets) in form of positive, negative, and neutral.
2. Intermediate Level
a. Recognition of Speech Emotion
Willing to learn on the usage of different libraries? Then you must go with this project idea. With different editor tools, you can tell how the speech emotion is appearing. This program model can be built as a data science project.
b. Gender & Age Detection with Data Science
This type of real-time project can help you grab the recruiter’s attention during an interview. Additionally, with this project, you can also learn convolutional neural networks.
One of the highly demanded & crucial elements for all businesses these days. Thereby, working on this data science project can help you uplift your career.
a. Credit Card Fraud Detection
Once you are through practicing the beginner & intermediate level of projects, you can move to this level. With the Credit Card Fraud Detection project, you can learn about how to use R with different algorithms like decision trees & logistic regression.
b. Traffic Sign Recognition
The purpose of this project is to achieve a higher level of accuracy in self-driving car technologies using CNN techniques, which can help in identifying different types of traffic signals by the input of an image.
c. Customer Segmentations
One of the most popular & important data science projects that can help marketers to reach the targeted & relevant group of people via marketing activities. Methods of clustering can play a vital role here that can assist in dividing the audience within age brackets, income, gender, and interest.
Whether you are a beginner or an expert in the field of data science, this comprehensive data science toolkit can be your ultimate support at all career levels. Bookmark this post for future assistance & use.