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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

What were you thinking when you choose your Data Scientist Profile?

Mrinal Singh
Mrinal Singh Last Updated : 07 Mar, 2022
6 min read

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

Data Scientist Profile
Source: Unsplash.com

There are many data science fields, you will have to work closely with your business to identify issues. You will get many articles telling tips for creating a solid profile, but no one will tell you which profile you should pick as your entire professional career depends upon it.

In today’s article, I want to share four significant reasons for going through different data scientist profiles before you frankly select one for you:

Reason 1: Cultivating Self-Awareness

Cultivating self awareness | Data Scientist Profile
https://unsplash.com/@jareddrice

 

– I want you to think about who you are now when it comes to data science.
— I want you to think about your goals regarding data science and how you would like your data scientist profile to change over the next 6 months.

Become a specialist in one thing or a generalist? or some mix? There are career benefits and disadvantages to each nonetheless of whether you’re in academia or industry.

Reason 2: Illustrate the Importance of Standardization in Visualization

Visualisation | Data Scientist Profile
https://unsplash.com/@goumbik

I wanted to reveal standardizing visualizations of users as a mix of characteristics. (You should think about how you will do it, and then also question yourself whether you think a standardized visualization has any significance.)

In this particular case:

(a) Standardizing The X-Axis: I used the main buckets that I thought were approximately some of the skills one lacks as a data scientist. I’m not tied to these buckets, but it seemed helpful in the starting days, and we can revise this going forward.

The chosen buckets- “Data Viz,” “Software Engineer,” “Math, “Statistics,” “Machine Learning(ML),” “Communication skills,” and “Field expertise” are convenient and contestable.

Also, I said, “maybe software engineer should be CS, I don’t know,” and then didn’t really make a decision, and you didn’t seem to mind (thanks!), but it did result in some people having different labels than others.

I pointed out that we had to evaluate whether the labels would be ordered or not. One way would be to go from left to right in terms of harder to softer skills. But felt stating Software Engineering was a more complex (more technical) skill than ML or Mathematics was problematic.

Alternatively, we could believe ordering according to the “data science pipeline,” starting with engineering, moving towards analysis with math, statistics, ML (would have to choose an order), and then moving into visualization, reporting, storytelling, and communication.

The complexity of the pipeline makes left to proper ordering non-obvious. So rather than resolve this at the moment because I could see it going either of several ways, I decided to not think of them as requested.

So once we think we are not interpreting them as instructed, we have to be careful not to see patterns that aren’t there but are just a manifestation of the (arbitrarily) selected order.

Also, some people in the industry might feel that I wasn’t being granular or broad enough, depending on their structure of reference. So I believe this is flawed, but again you have to start somewhere, and usually someplace reasonably uncomplicated, and that’s part of EDA!

(b) Standardizing The Y-Axis: I drew my profile on the panel and showed my data scientist profile when I completed my bachelor’s and how it changed after working on a great data science team learning from my collaborators and colleagues.

Here the comparison is before and after. I decided not to label the scale because I didn’t want my notion of expertise to influence you. One man’s specialty is another man’s poser.

A student just learning this stuff has a different scale than someone who has been doing this for years. Each would have a different interpretation of “expertise,” reflecting over-or under-confidence.

So we have to accept that our scales will be subjective if we label them. (We should think about what it would mean to standardize the scale. How would we do it? What would the consequences of it be? How do we define “expert”?)

Reason 3: Our First Step to Thinking about Data Science Teams

Data Science Teams
https://unsplash.com/@jasongoodman_youxventures

I want you to join a data science community. One way to think about going about it would be to combine complementary profiles. It helps you understand the role, meet like-minded people and learn beforehand.

Reason 4: Demonstrate your Thought Process before you do EDA

Thought Process
https://unsplash.com/@uxindo

It’s a mix of intuition and math/stats know-how. I first came up with a simple, standardized visualization, which I could then compare different profiles. The lack of standardization means I would try to focus on relative conditions. Did I know what I would see before I did it? No. But I had a hunch that some of the following would happen:
(a) I’d discover something new
(b) I’d witness natural clusters of profiles. Some people are similar to each other. (Think: what does “similar” mean? What is the “distance” between two profiles? How do I measure similarity?)
(c) I’d obtain a sense of the distribution across profiles
(d) I’d begin getting an intuition for joining a data science community.
(e) I’d begin thinking of machine learning or analysis problems I could potentially work on with this data set or a generalized version of it.

Just let your imagination go here as a data scientist. How would you use these profiles or something along these bars as a method to think about or construct functional teams?

My Meta-thoughts And Analysis Before You Show The Results

My thoughts about this, who I am as a data scientist, my strengths relative to others, and what I contribute to a team have been shaped and influenced by many conversations I’ve had with my collaborator, mentors and friends.

Final Things for you to Think About

Thought experiment: Generalize this problem by visualizing a team rather than a person.

Thought experiment: Some data sets could be millions of users/humans. (unlikely to be a set of millions of potential data scientists!). So how would you think about scaling this process? Is there a difference in what you would do if the numbers were self-reported vs. logged user actions on a website?

Think of a social networking or online dating website to get concrete about this. How would you explore a data set of users and their attributes? If the attributes were self-reported attributes like “how happy are you on a scale of 1-10″, how would you handle the subjectivity of “10”? How would you visualize it, cluster it, represent the distribution over it?

Scaling also suggests that you start by sampling and doing it by eye yourself to gain intuition, but then build an algorithm to automate. (This is an example of machine learning)

Also, remind yourself that I asked you to question standardization and think about how having un-standardized input might impact all this. Does the importance of standardization change for you when we are dealing with smaller data sets vs millions?

Final Words

I hope this article was helpful for you to understand the importance of visualization and EDA before you select any Data Scientist Profile.

Thanks for reading my article on data scientist profile, and have a good day 🙂

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About Author

I am a Data Scientist with a Bachelors’s degree in computer science specializing in Machine Learning, Artificial Intelligence, and Computer Vision. Mrinal is also a freelance blogger, author, and geek with five years of experience in his work. With a background working through most areas of computer science, I am currently pursuing Masters in Applied Computing with a specialization in AI from the University of Windsor, and I am a Freelance content writer and content analyst.

Connect with me on my social media profiles and follow me for a quick virtual cup of coffee.

LinkedIn | Github | Email | Medium | Instagram | Facebook | Portfolio

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

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

Technical Writer | Data Science, ML, AI, Open-Source | Do More with Data - Litmus

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