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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

Conquering Imposter Syndrome in the Data Science Ecosystem

Mrinal Singh
Mrinal Singh Last Updated : 31 May, 2021
5 min read
This article was published as a part of the Data Science Blogathon
imposter syndrome | image 1

 

Hello there, buddies! I believed I’d take a meager break from the ordinary tutorial-oriented posts to include a diverse topic that is nevertheless data science-oriented and one that is underrated, in my belief.

Of course, I’m talking regarding imposter syndrome. If you are new to the thought, imposter syndrome believes that you feel like you aren’t suitable enough to be in a demanding position.

It’s not a notion limited only to the data science world, but I think data scientists and machine learning engineers get kicked the hardest by imposter syndrome.

As somebody who genuinely desires to see others thrive in this field, I craved to take some time to discuss this topic.

Having mentored a comprehensive range of populations in machine learning and data science, numerous of them have formulated this same fear.

I hope this blog will share some ideas to reflect on to help you defeat your imposter syndrome.

With that, let’s dive into five points to think nearly in concerns to imposter syndrome.

1.Everybody begins fresh in any data science position due to domain understanding and expertise.

 

imposter syndrome | image 2

 

  • Whether you address primary logistic regression models or decorative deep learning algorithms, the actuality is that domain knowledge is key to success in any data science job, even if you get the math under the hat.
  • In that sense, everybody must start anew when they enter a new office in a data science position.
  • Even if an individual pivots from one section to another inside the same company, that personality will have to study a new collection of domain knowledge, which places them at a difficulty in the beginning.
  • There is no avoiding this even amongst the several experienced data science practitioners. Whether this is your primary or tenth data science job, everybody has to start new with a distinct set of domain knowledge.

2.Multiple data science career postings are crudely written

 

imposter syndrome | image 3

 

  • I have sat down with remarkable experts to examine some job sites people are interested in, and it’s no shock that my mentees get super horrified after analyzing the private positions.
  • There are two common issues I see very commonly with data science work postings.
  • The first is that a business posting was addressed by an HR recruiter who doesn’t have any background operating in a data science capacity. The following is that the job posting will demand an insane amount of skills and experiences.
  • I have seen unusual postings that will require that a nominee be an expert in Python, Java, Scala, R, C++, Kubernetes, AWS, and more extra.
  • I had worked with a group of data science folks from startups to MNCs, and let me answer this: nobody — including me — comes near to comparing the box off on all desired skills.
  • And most outstanding employers don’t demand the full extent. Don’t let an ironing list of skills prevent you from appealing for a job you’re interested in.

3.Keep in subconscious how agile technology advances

 

imposter syndrome | image 4

 

  • Technology, notably in the data science environment, transfers so fast that it’s highly likely that yet seasoned practitioners will continuously sharpen their skills overhead time.
  • It’s kind of wild to deem that something like Docker and Kubernetes, which I manage daily, haven’t been nearby all that long.
  • Even after I started in this position about 14 months ago, Amazon Web Services (AWS) has attached many new highlights to the SageMaker service, especially in the framework of SageMaker Studio.
  • I have not ought the event yet to acquire those things growing out of SageMaker Studio, but I comprehend that if I don’t attempt to follow up, I will fall back.
  • The pace of development will not slow down any point soon, so retain that if you observe that you’re having a tough time staying up, the possibilities are that despite the seasoned practitioners striving to keep up, too.

4.Don’t be hesitant to take responsibility for your mental energy

 

image 5

 

  • This is a highly underrated thing to reflect on, notably if you concurrently cope with stressors outside of the office.
  • For me, COVID-19 hit hardly three months into my role, right at the seeming end of my imposter syndrome. While I wasn’t specifically worried about dying from the disease, I had enough stress about the possible ripple consequences of the virus.
  • How would this influence the marketplace? What does this imply for my forthcoming employment status? Will this infection entirely change the destiny of humanity? It was inconvenient timing all around, and to be fair, I sought expert help.
  • As men in selective strife to do this, I feel like this something we assume, like showing indecision. Even typing this information here feels very uncomfortable and exhausted.
  • Still, I think it’s necessary to be straightforward with you to influence you better to receive the guidance you demand.
  • I realize how annoying it can be to seek and allow professional assistance for your mental well-being, but it is a judgment I do not depreciate at all, and I am fortunate that I did.

5.Retain that broad expertise is a delusion

 

imposter syndrome | image 6

 

  • I need to say; I still encounter a random bit of imposter syndrome personally when I see a magazine like Analytics Vidhya.
  • Further, I know how ridiculous that is, given that I am a standard contributor to the publication myself. Still, I am invariably influenced by things people commit to the data science association.
  • I have to stress that remembering that I am a contributor to the alliance but admittedly way under-educated in certain viewpoints of data science, I have to believe most people are the corresponding way.
  • That’s not at all occurring patronizing to anybody. It’s just a fact that there is so important to hear that I don’t think it’s even feasible for a particular personality to study everything.
  • We all might have expertise in a slender slice of society, but I very profoundly doubt anybody is a scholar of everything.

Inference

That winds up this blog, folks. I believe you find it cheering as you advance along with your data science venture. Remember that we all have to begin scattered; nobody rises as a specialist.

Thanks for stopping by my article, and if you fancy reading this, I am sure that we share similar interests and are/will be in similar industries. So let’s connect via LinkedIn and Github. Please do not hesitate to send a contact request!

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

blogathoncareer advicedata science
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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