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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

How to make an impressive Data Science Portfolio?

A
Anjali Last Updated : 15 Apr, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

” A good first impression can work wonders” – J. K. Rowling

Introduction

Gone are the days, when people just used to view CV and decide whether you’re a suitable candidate for an internship or job. In the technological sector, people now see the overall profile and projects (Portfolio) to shortlist candidates, especially for Data Science. But with time, educational companies have started to provide paid online projects, so,  almost every student is now doing projects and internships (paid/unpaid/voluntary).

So, the candidates should not only proactively do projects but also showcase their skills to stand out for an opportunity. By showcasing I mean you should make a brand of yourself. When someone sees your data science portfolio, they should get an exact idea of your interests, previous work, accomplishments and be interested in having a talk with you.

 

Tips to Make an Amazing Data Science Portfolio

1. Have an active Github profile

GitHub allows you to host a remote version of your project where others can see it and even collaborate to form a better version. Always have an active GitHub profile and put the link on your CV. By active profile, I mean you should work on it regularly because your contributions on daily basis are recorded on it and can be seen by viewers. Also, make sure to make a readme.md (read more about it at Click on this) for your profile to customize your homepage.

Here is a sample of my profile to get an idea:

Github profile
Contributions

 

2. Start Using Kaggle

Having a Kaggle account is very important. Not only for displaying your skills but also for practicing them regularly. Many companies like ZS Analytics, KPMG, Bain & Co., JPM, etc. have a Data Science Competition like the ones available on Kaggle.

Apart from this, the learning competitions available on Kaggle helps to understand more about the techniques and tips to apply when dealing with different kinds of data. Kaggle is also a great platform to showcase your skills. You can learn medals and titles (Kaggle 1X/2X/3X/4X Expert, Kaggle Grandmaster) which have a great impact if you put them on the headline of your LinkedIn profile. You can also add a link to Kaggle on your CV.

Below is a random public profile of a Kaggle 3X expert from India,

 

Data Science Portfolio Kaggle

 

3. Participate in Competitions and Hackathons

Competitions and Hackathons help us to develop our skills and know our position in our peer group. Success in competitions and hackathons can be put as achievements that will add credibility to your work. For example, the Analytics Vidhya platform can be used to participate in Hackathons. Top Approaches of any competition can also be viewed on it to learn new and improved approaches.

Right now, you participate in around 67 hackathons on it for learning purposes and see the top approaches for previous competitions. They also have Job-A-thons and hiring hackathons several times a year, so stay tuned with them to participate in those.

Data Sciecne Hackathons

 

 

4. Practice questions using HackerRank

HackerRank is a great platform to enhance your Python skills. It has questions that can help you improve your programming skills. Along with this, it also offers stars based on points you achieve for solving those questions correctly. Put ( HackerRank 5 star) on your LinkedIn headline to show their competence in Python or Data Structures/Algorithms.

Data Science Portfolio Practice

 

5. Read Blogs

Reading blogs keeps you updated about recent developments in the field. They might also be helpful in discussions during the interview. Also, blogs can be used as a tool to learn new skills. Reading blogs on personal experiences would help you to learn more about the industry and what you should do to find a suitable role in the future. You can follow Christopher Zita, Analytics India Magazine, Analytics Vidhya Blog/Medium Channel, Towards Data Science (On Medium), KDnuggets, etc. for this purpose.

 

6. Make your portfolio website

Make a very simple portfolio website. You can either code in HTML or use Wix/Weebly for making one. Once, you host your website, make sure you put it on your CV too. A website will have a great impact on a recruiter who sees your profile. It will enhance your skills and also give an opportunity to them to see your projects and work in the field. The image below shows a snapshot of my portfolio website.

Data Science portfolio website

 

7. Have a LinkedIn profile

A Linkedin profile is extremely important for everyone. This helps you to connect with people from all over the world who might be working in the field you’re interested in. LinkedIn also helps in sharing your work with the community. Many recruiters now use LinkedIn’s recommendation system to contact candidates for any openings in their firms. Also, follow hashtags in the field of DS and ML-like  #66daysofdata, #MavenAnalytics (For Data Visualisation).

 

8. Do small projects

Start with projects on well-known datasets like Boston Pricing, Iris, XOR, MNIST, etc. After this move on to making big projects like recommendation engine, full analysis of some data, etc. Datasets can be found on Kaggle. HR analytics, Image Analysis, Customer Segmentation, Netflix Data Analysis, Uber Data Analysis are some examples to start projects. Feel free to make your own dataset and then do an analysis.

 

9. Deploy code

Once you’ve made a project, then try to deploy it either on Heroku or AWS or any other cloud platform. This helps you to build a full-fledged data science application that can be used. For example, if you make a movie recommendation engine, then using Heroku or AWS, build a website where people can actually come, choose movies they like and your algorithm predicts which movies they can watch based on their interests. This code deploying impresses HR’s a lot and can surely help you to score an interview.

 

10. Focus on community building

The above methods will surely help you to build an extremely great profile, but apart from it, knowing opportunities is also important. For this, participate in the community and build great connections. LinkedIn, Discord, Slack, Telegram are some platforms where you can join groups of data scientists who regularly post messages for opportunities that you can make use of.

I hope you liked this article.

If you have any doubts, drop a comment.

Follow me here, my bot Grey will be there to help you out.

The media shown in this article on building Data Science Portfolio are not owned by Analytics Vidhya and is used at the Author’s discretion. 

A
Anjali

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Your write up is really educating and inspiring. Thank you so much. I'm new to programming and I'm enthusiastic about AI and data science. I would like to know you in personasikn

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