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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 COVID19 Pandemic Has Been Tackled By Data Science?

Mrinal Singh
Mrinal Singh Last Updated : 16 Jun, 2021
5 min read

This article was published as a part of the Data Science Blogathon

COVID19 data science 1

Machine learning and data science have played a vital role in the fight against the recent deadly pandemic known as COVID19. Almost all countries have been using some way or the other this data science, machine learning, and other means of statistical analysis to see the growth rate of the curve of the infected patients and to realize the death rate caused due to this pandemic.

Nevertheless, there are several difficulties in this domain, and it is high time that we address those issues.

The prime struggle in the data science applicability towards covid19 is collecting the data. This appears to be one of the usual challenging obstacles that all data scientists are suffering right now. One aspect of collecting numerical data is to predict the curve of death and affected people.

The other application of machine learning is to apply deep knowledge and advanced machine learning to classify X-Ray images of COVID19 patients and regular pneumonia patients. The CT scan images of COVID19 patients are also available for machine learning and deep learning applications. To make these successful applications, one needs powerful high-end processors that may not be available in this challenging time to all countries’ medical scientists.

Besides, data science and machine learning can help to do the following performances as well.

1. It can help discover the need for this hour; drug discovery technology can be uplifted using machine learning and deep learning.

2. It can help decide the red zone, orange zone, and green zone based on the density of the infected patients.

3. It can do the feature extractions from the images of covid19 patients to understand the virus’s behavior.

4. It can also justify the source from where the virus originated; that will help estimate the next significant outbreak.

Data science and machine learning can significantly help segregate the patients based on the severity of the infection. Doctors can decide whom to keep quarantine and who to provide proper medications or treatments.

Data Science can predict the possible outcome of the treatment.

The geographic segregation based on the locality in terms of country wise, state wise, district wise, and then finally as continent wise are also possible using machine learning and data science. For example, the COVID19 pandemic has been more severe in European countries and the USA than in Asia, Australia. etc. Using the pattern from this data, the experts can normalize it to build a generic statement related to the growth of infection rate, death rate, and a decision made when the COVID patient graph shall be flattening (possibility)?

In this pandemic time, deep learning can also be a valuable tool. It may analyze the facial expression by the facial screening of infected patients(This may be a possibility).

The other significant point is to ignore the outlier from the data sets, such as image and numeric data related to covid19 patients and infection rates. However, it needs a piece of expert knowledge.

Lastly, the data science expert must build their model, keeping in mind that it can not be completely perfect. They have to consider their models with some reservations. The catch is that these models are just helping hands towards the successful treatment of COVID19 infection.

We hope that we will soon achieve success by eliminating coronavirus cases from the earth using multi-dimensional treatments, including data science and machine learning. Let us pray to god together.

Do We Have Enough Data Scientist Jobs Amid COVID-19 Pandemic?

 

COVID19 data science 2

The recent ongoing coronavirus pandemic has engendered many job losses across all industries. The rate of unemployment is growing like never before. Almost 3.8 million people lost their jobs last year in the USA because of COVID19. Moreover, the global economy likely to be gravely affected in the coming times.

In this recession, the data science jobs are bestowing some sort of good news as a hatful of hiring can be observed in analytics companies. Surprisingly, the salary reduction is also a bit ok with the data science sector. According to The Gardian1, many big US-based organizations have declared the pay-cuts for the organization or are intending layoffs.

“Boeing, last year, announced that the coronavirus had delivered “a body blow” to its business and is weighing laying off 16,000 people, a 10% cut to its 161,000 workforces. Hertz, the car rental company, recently laid off about 10,000 employees in North America and is reportedly considering bankruptcy”-The Guardian.

However, data science jobs have found more minor layoff, fewer pay cuts in comparison with other jobs, and the reasons are as below,

  • In data science jobs, techies need to know a specific domain with solid fundamentals in statistics, machine learning, data mining, and A.I. It is tough to find such talents. So with unexpected layoffs, companies have to pay a heavy price as finding such new data-scientists would be an arduous task.
  • Data analytics would play a pivotal role in analyzing the cost, reducing the expenditure, segmenting the targetted market at this hard pandemic time. So, at this distasteful time when other industries such as service industries have fluffed up by the COVID19 pandemic, data analytics can do justice to the analytics part, at least.
  • Most significantly, countries need health analytics like never before to predict the infection rate, death rate, and tentative timing of an end to this deadly pandemic.
  • Employees from analytics companies can efficiently work from home without any hustle. However, this could be true for any software company. In fact, work from hope has resulted in bad outcomes for companies like TCS, etc2.
  • “TCS Mulls 75% Of Its Workforce To Work From Home By 2025- says M.D. Rajesh Gopinathan”, “A.I. is the new electricity,”- Andrew Ng says. As long as the company has the data to work with, it would survive.
  • It is expected that in the coming times of 2021-22, a massive scarcity of data scientists and A.I. professionals could surge as companies need more and more automation in collaboration with IoT, machine learning, and deep understanding. So, hiring data science experts will increase in 2021-22.

Hence, the above points ground why the ceases or layoffs are less for data science and AI Jobs even during the COVID19 pandemic.

The author’s point of view is the author’s personal opinion, personal beliefs, personal perspective, and personal perspective.

Thanks for Browsing my Article. Kindly comment and don’t forget to share this blog as it will motivate me to deliver more quality blogs on ML & DL-related topics.
Thank you so much for your help, cooperation, and support!

About Author

Mrinal Walia is a professional Python Developer with a Bachelors’s degree in computer science specializing in Machine Learning, Artificial Intelligence, and Computer Vision. In addition to this, Mrinal is an interactive blogger, author, and geek with over four years of experience in his work. With a background working through most areas of computer science, Mrinal currently works as a Testing and Automation Engineer at Versa Networks, India. My aim to reach my creative goals one step at a time, and I believe in doing everything with a smile.

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Thanks for Browsing my Article. Kindly comment and don’t forget to share this blog as it will motivate me to deliver more quality blogs on ML & DL-related topics. Thank you so much for your help, cooperation, and support!

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

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Mrinal Singh
Mrinal Singh

Data Scientist and a Technical Writer! I will give you the best of Open-Source and AI.

Talks about #chatgpt, #opensource, #contentcreation, #communitybuilding, and #artificialintelligence

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