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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

These 7 Signs Show you have Data Scientist Potential!

r
ram_dewani Last Updated : 03 Dec, 2020
6 min read

Overview

  • Let’s have a look at these 7 signs to know whether you are a potential data scientist or not
  • By no means is this list exhaustive. Feel free to add more qualities in the comment section

 

Introduction

Data Science has not just become the “Sexiest Job of the 21st Century” but one of the most exciting job roles. You get to make an impact at the company-wide level with the latest technology and algorithms.

How does a person know that he needs to pursue Data Science? What are the qualities of a Data Scientist that one must look for? He may be interested in coding, building new tech, is patient at debugging. For example, a mathematics aspirant knows he wants to become a mathematician due to his interest in mathematical concepts and a knack for solving problems. So what are the signs that define a data scientist and one must look within themselves to know if they are a potential data scientist or not?

Potential Data Scientist - Meme

The role of a data scientist is really crucial to the whole organization and the economy as a whole. But the problem is – there is a shortage of “Skilled” data scientists globally. The AI and ML Blackbelt+ program aims to make you an industry-ready certified data science professional with 14+ courses, 39+ real-life projects, and 1:1 mentorship sessions so that you are never off-track.

If you are new to the world of data science, I suggest you go through the data science roadmap –

 

Table of Contents

  1. Love Number Crunching
  2. You are always up for solving Puzzles
  3. Enjoy solving unstructured problems
  4. You are curious – always asking “Why?”
  5. You have a knack for problem-solving
  6. Enjoy deep research
  7. Love telling Stories – Great at presenting

 

1. Love Number Crunching and Solving Puzzles

Potential Data Scientist - Number Crunching and Solving problems

If you love crunching numbers and solving logical problems based on probability, statistics, puzzles then chances are that you have a natural tendency of becoming a data scientist or a business analyst. By “love”, I don’t mean calculating the bill split among your friends accurately, but a craze that reaches to next level.

For example, guessing the number of boys in India that are under 15 years. This is known as guess estimate and Data science interviewers love asking these questions. I have found the perfect article for you to understand problem-solving as data scientists –

 

2. Enjoy solving unstructured problems

Potential Data Scientist - Solve Unstructured Problems

Unstructured problems are everywhere around us. In an Edtech company, the management may be asking – how do we increase the revenue on our courses? Whereas in a social networking company, the question maybe – How do we increase the user retention on our app. Do you notice something wrong with these questions? Well, these are unstructured problems.

An important aspect of being a data scientist is the ability to form well-defined goals. A structured approach to the above question can be – To retain 20% more customers through the slide-down feature in the next 3 months. The goal should represent What? Why? How?

Next time, if you find yourself in the middle of a problem and you find yourself breaking down the problem into smaller goals, this might be a good starting sign of becoming a data scientist.

 

3. You are curious – always asking “Why?”

Potential Data Scientist - Curiosity

Do you find yourself questioning other people’s assumptions? Are you not able to end your day without asking “Why this? Why that? Why this over that?” You may be a natural fit to become a data scientist.

For example, in the above example, you may want to question the management and ask – Why do we want to retain customers? Can we retain only the paying customers? why focus on retention rather than user acquisition?

Some of the best data scientists would stop anyone and ask for a rationale if they are not clear – Why did you ask this question? What was your thought process? Why do you assume so? are just a few examples of these questions!

Do you find yourself in the middle of these signs? You love problem-solving but don’t love mathematics by heart? A lot of these qualities can be gained with practice. The AI and ML Blackbelt+ certified Data Science program aims to take you from zero-to-hero with its 14+ courses, 39+ real-life projects, and 1:1 mentorship sessions with experts!

 

4. You have a knack for problem-solving

Problem Solving

The easiest sign that tells you are a potential data scientist is the addiction to solving problems. The fridge is not working? You try to identify the root cause of the problem. The sales of your company have decreased the quarter, you try to understand the root cause of the problem.

Data Scientist’s role is not just to apply machine learning algorithms to build an accurate model, it is to formulate a problem statement, form a hypothesis, data analysis, data model, and then finding the best results and communicating to the management. Each of these steps requires a knack for solving problems.

 

5. Enjoy Deep Research

Deep Research

A Data scientist needs to sit on a single problem statement for a long duration of time, going back and forth to the stakeholders to understand their requirements, trying out different hypotheses, mining data, different modeling techniques until the results have been achieved.

Are you someone who finds themselves not giving up until the problem has been solved? Studying and Googling and researching on a single problem. When was the last time you spent hours and hours immersed in solving a problem? Can you do that again and again?

 

6. Love telling Stories – Great at presenting

Love Storytelling

There is always a person in the room who tells amazing stories. No one can resist listening to him and he impresses everyone with his storytelling skills. Are you that person?

A Data scientist needs to be great at storytelling. What is the use of all the hard work, if he is not able to influence his stakeholders? Communicating with data and presenting stories backed by data is one of the most important elements in the life of a data scientist.

 

7. Love Experimentations

Love Experimentations

Do you find yourself experimenting at any time in the day? I don’t mean science experiments. A part of being a data scientist is to be curious.

What is the best mode of transportation to the office? If I find traffic at the X traffic signal, what is my estimated time of arrival? What if I travel halfway through the bus and then take the metro at the signal where I find traffic 90% of the time? These problems seem trivial but a large part of your life is going to be filled with experimentations.

If you love to experiment and play with curiosity in your daily life then being a data scientist is going to be a fun job for you!

 

End Notes

Being a data scientist is one of the most exciting roles and I love being part of this awesome community.

Were you able to relate to the points mentioned above? You are probably going to enjoy being a data scientist and do some amazing work in this field. The AI and ML Blackbelt+ program aims to nurture your talent as a data scientist and make you an industry-ready professional. You can check out the program here.

Which point were you able to relate to the most?

Let me know in the comments if you have some other points or signs that make a person a natural fit or has the potential for the role of a data scientist.

careercareer advicestructured thinking
r
ram_dewani

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