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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top Data Science Projects to add to your Portfolio in 2021

Sakshi Raheja
Sakshi Raheja Last Updated : 23 Dec, 2021
6 min read

Introduction

2021 is a year that proved nothing is better than a Proof of Work to evaluate any candidate’s worth, initiative, and skill.

Pursuing any data science project will help you polish your resume. These projects will not only deepen an understanding of the concepts but also, help you gain some practical experience in the data science industry. Moreover, they serve as a great proof of work rather than merely completing courses.

Students and even professionals create their own portfolios or do professional projects that are available on various websites. These projects will give you an opportunity to network with other professionals in the same industry.

Data Scientists Meeting

To develop a professional portfolio, it is important for you to have different projects. Each project should be well-structured and handled professionally. With your delivery skills for a particular project, you could get a job opportunity, as well. Thus, it is important for you to make sure that you develop specific skills via these projects.

 

As a data scientist, you must have the following skillsets in your portfolio:

  • communication
  • collaboration
  • technical competence
  • know the ‘data’ at a deeper level
  • take initiatives and experiment
  • domain expertise

 

Components that a Data Science Project must entail:

  • Problem statement: This is the prime component of any project. Your project will solve this problem and state various approaches to resolve the issues in the current model.
  • Dataset: This is one of the most important features of your project. It isn’t easy to find genuine, huge data. So, take your time and find datasets from authentic sources.
  • Algorithm: There are different algorithms that could be used to analyze the data and predict the results. Some of these algorithms are Regression Algorithms, Regression Trees, Naive Bayes Algorithm, and Vector Quantization.
  • Training Models: These models will help you to predict accurate outcomes of your project. Thus, it is important for you to use proper training techniques, against various inputs and outputs.

Read this article to understand how you can choose the most appropriate project for yourself.

Add these Projects to your professional journey!

Computer Vision Projects

computer vision | Projects of Data Science 2021

Real-ESRG

Language: Python

This project aims at developing practical algorithms which will help to restore the damaged images. We know how important it is for us to have crystal clear images, be it our lost photos or uploading images to our blog.

Images, in the end, are a way to tempt people (food, travel, and fashion industry) and attract us which leads to the business generation of various industries. The ESRGAN uses a discriminator that help us to understand what percent of the image is being real or fake. So, do you wish to find out? If someone is misleading you with fake images?

Link: https://github.com/xinntao/Real-ESRGAN

Robust Video Matting (RVM)

Language: Python

RVM is a new branch of art performance. It can perform matting in real-time, uses a recurrent neural network to process the videos.

This project is going to be fun! This one is specially going to be great for people who aspire to become influencers or might like to create videos, recording all your special videos.  Here, you will be able to have a green screen behind or any other background of your choice. So, while sitting at home. You could take some pleasure by feeling the beach or mountains…VIRTUALLY!

Do try this project and share the YouTube link of your video in the comments section with us.

Link: https://github.com/xinntao/Real-ESRGAN

GFPGAN

Language: Python

GFPGAN develops a practical algorithm for real-face (or blind) restoration. In this project, you will be able to work on images that aren’t clear in quality and are of blind people. So, you will be required to make their facial features, especially their eyes clear.

Sometimes, it could be challenging because not all features of your face can be restored properly. This project will help you learn the technology where you will be able to restore low-quality face images via semantic aware style transformation.

 

Link: https://github.com/TencentARC/GFPGAN

 

Read our latest article on Implementing Computer Vision.

Natural Language Processing Projects

Projects of data science 2021 | NLP

WHAT

Language: Python and DockerFile

Has it happened to you? That you receive a text from someone whom you don’t know and they happen to reveal your personal details, maybe some friends playing a prank on you or someone blackmailing you?

Well, not anymore! Once you pursue, the ‘what’ project, you will be able to know the unknown. Hahaha…sounds mysterious? Jokes apart, this project will help you to find details like emails, IP addresses, and more.

Pursue this project to know more!

Link: https://github.com/bee-san/pyWhat

Textual

Language: Python, MakeFile, and TypeScript

This project is inspired by modern web development. This project uses Rich to render the rich text, so anything that Rich can render could also be done in Textual. Some of the examples: animation, calculator, grid layout, a simple textual app with scrolling markdown view – all of this could be done under this project.

Link: https://github.com/willmcgugan/textual

Change Detection

This project will help you learn about making simple changes on the self-hosted sources, open-source websites that help to monitor the changes and provide notifications for each change that takes place. The focus on change type will be text-related changes.

For example: On the government websites, when COVID-19 related news changes, with respect to the number of new cases/ number of people, died/ number of recovered people, and so on.

Link: https://github.com/dgtlmoon/changedetection.io

Machine Learning Projects

machine learning | Projects of Data Science 2021

SeaLion

This project is designed to teach today’s aspiring MI Engineers, the popular machine learning concepts which give an opportunity to use different ways of application. Once you complete this project, you will be able to learn a lot of topics from machine learning via using different algorithms.

SeaLion was developed by Anish Lakkapragada, as a freshman in high school. The library is meant for beginner-level data science enthusiasts who would be interested in solving the standard libraries like iris, breast cancer, swiss roll, etc.

Link: https://github.com/anish-lakkapragada/SeaLion

Data Engineering Projects

Data Engineering

Deploy Machine Learning Model using Flask (with Code)

This project is one of the most practical projects you would be doing. This will not only help you learn here, in the process of completion. But also, in every sphere of data science. This project will help you learn how you will be able to put any of your machine learning models into production.

This project will introduce you to Flask which is a web application framework written in Python. The flask has multiple modules and will help web developers to write their applications without having to worry about the details like protocol management, thread management, and so on. It gives you an opportunity to work on different web applications and gives you the necessary tools that help you build a web application.

Read more on using flask for Data Science here.

 

Time Series Projects

Time series

 

Time series analysis is a vital component of the Data Science and Engineering industry where important concepts like key statistics and detecting regressions are used to forecast future trends.

 Kats

Kats is a toolkit where you could analyze the time series data. This is a generalized project which can be used even by new people in the data science industry. The project could have an extensive framework where you could perform time series analysis. This would include understanding different concepts like key statistics and characteristics, detecting change points and anomalies.

Link: https://github.com/facebookresearch/Kats

Time Series using Merlion

Merlion is a python project which will help you to polish your concepts of machine learning. This is part of the time series intelligence which includes loading and transforming the data. It will help you to learn various time series learning tasks, which include, forecasting, anomaly detection, etc. This project will specifically focus on providing engineers and researchers with, one-stop solution to develop models for multiple time-series datasets.

This project has different modules which makes it easier for the data scientists to pursue. Also, it provides a unique evaluation framework that simulates the live deployment and re-training of a model in production.

Link: https://github.com/salesforce/Merlion

Conclusion

Share your views with us in the comments below. How did you like our top suggestions for 2021 projects? And, if you’re worried after reading this article that you didn’t pursue any projects in 2021, frankly, then, don’t be. Promise yourself and add at least five of these courses as goals for 2022. Because learning is always an ongoing process.

Read about Software Engineering process for an effective Data Science project.

Happy Learning!

data miningdeep learningmachine learningNLPprojects of 2021statistics
Sakshi Raheja
Sakshi Raheja

I am a passionate writer and avid reader who finds joy in weaving stories through the lens of data analytics and visualization. With a knack for blending creativity with numbers, I transform complex datasets into compelling narratives. Whether it's writing insightful blogs or crafting visual stories from data, I navigate both worlds with ease and enthusiasm. 

A lover of both chai and coffee, I believe the right brew sparks creativity and sharpens focus—fueling my journey in the ever-evolving field of analytics. For me, every dataset holds a story, and I am always on a quest to uncover it.

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15+ Github Machine Learning Repositories for Data Scientists
15+ Github Machine Learning Repositories for Data Scientists
10 Data Science Projects Every Beginner should add to their Portfolio
10 Data Science Projects Every Beginner should add to their Portfolio

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