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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

5 Bad Reasons to Become a Data Scientist in 2023

Shweta Rawat
Shweta Rawat Last Updated : 14 Dec, 2023
6 min read

Introduction

We live in a world where more than 2 quintillion bytes of estimated data are generated every day. This is due to the increased use of digital devices for texting, sending images, and emails, doing transactions, searching queries online, and so on. Everything we do is captured in the form of data. This data is going to increase in coming years. Hence, multiple businesses like fintech, ed-tech, service providers, etc., need data scientists so that they can process the data efficiently and thus interpret it.

Big data helps companies to collect a large amount of data on their products, customers, and operation. But only data collection is not enough. Companies need data scientists to analyze the data and derive useful insights from it. Data scientist helps companies in Decision making, optimizing their operational process and building new products/services which is profitable. Data scientists create models which is predictive in nature. These models can predict the outcomes based on old data. Hence, there is high demand for data scientists. This is one of the highest-paying jobs and the hottest topic among job seekers.

But, don’t join the wave of data science without knowing the required skills and the required commitment. This article will look into the 5 reasons you need to know before becoming a data scientist. This article will burst the misconception around this job and how this field is not for you if you follow certain myths.

Learning Objectives

  • To highlight 5 reasons why becoming a data scientist is not a piece of cake.
  • To burst misconceptions around this field and help future aspirants to make a correct decision.

If you are a little impatient and want a quick and summarized context, then watch the below video now.

If you are still here, then keep reading.

Let’s understand the

Table of Contents

1. Don’t Become a Data Scientist Because of Glamour

Data Scientist

Source: freepik.com

Data scientist is one of the sexiest designations in the market, offering very high salary packages. But don’t join this field due to its hype. This job is not the cup of tea for everyone and requires lots of commitment and time. Data scientists spend most of their day in data cleaning, which is very tedious. This work is repetitive and eats most of your daily time.

For example, imagine you are working as a Data Scientist with a Fintech Company and are tasked with building a Credit Scoring Model. For this task, you will need to refer to multiple data sources for extracting data. This will be Followed by aggregation and cleaning, which will surely consume more than half of your total project time.

2. If you Think That Data Scientist Works Independently on flexible hours

data scientist

Data scientists have expertise in the automation of tasks, but they can’t work in complete isolation. They need to collaborate with other professionals like SDE, data analysts, stakeholders, etc., for any project. For this, time management and prioritizing the work are essential. They need to rely on other departments/ professionals who help gather the data. After that, the data analyst must clean, pre-process and transform the data to be used in the model. Stakeholders help convert the business problem into a data science problem so that data scientists further work on them.

Let me retake the Credit Scoring example to explain this. For building this credit scoring model, functionally, you may have zero understanding of what credit scoring is. So, that’s where you shall engage with your stakeholders, who might give you crucial insights on Data Sources, Parameters, Thresholds, etc.

After this, they need to work with statisticians and SMEs who validate the model before putting it into production.

Hence, data scientists completely depend on other departments for processing a task.

3. Becoming a Data Scientist Just for a High Salary Package and Perks

Data Scientist

If you expect that you will earn high salary once after becoming a data scientist, then you are incorrect. Data scientists use numerous technologies in their day-to-day work, and the technologies keep upgrading. So once, after becoming a data scientist, you need to update your knowledge frequently. You have to keep up with new trends, technologies, and methods; otherwise, you have chance of getting replaced. There is much competition in the market you can’t settle for the thought that you can sit comfortably. Hence, you should know the pros and cons of every field.

Let’s retake our Credit Scoring example. Let’s say you trained your first model version using Traditional Machine Learning. Everybody liked it, and you took it to production. However, in due course, you learned that training a Deep Learning RNN Model would give increased model accuracy. And if you haven’t used RNN before, you might need to learn it on the fly and train a model thereafter. While it’s challenging, you are anyway expected to do it.

Hence, if you are not willing to learn after joining as a data scientist, this will be hard on you.

4. If you Think Coding Knowledge is not Required

Data Scientist

Some people prefer the data scientist role over the software developer, thinking it requires less or no coding at all. But, in reality, you need coding to perform your day-to-day task. You need coding to experiment with your machine-learning approaches too. Knowledge of programming languages such as Python, R, and SQL is required to perform tasks like cleaning, pre-processing, visualizing and building machine learning models. Writing clean, efficient, and maintainable code is important for building reproducible and scalable data pipelines.

Again going on our Credit Scoring Model for a Fintech business: You need to write code for Exploratory data analysis and Model building. So, you can’t escape from coding in this field.

5. If you Think Excel Knowledge is Enough

Excel

We all know that excel is a powerful tool for data visualization and analysis. But having alone knowledge of excel or google sheet is not enough. Excel cannot handle large datasets and can’t perform complex data modeling tasks. As a data scientist, you need to have deep knowledge and understanding of Statistics, Machine Learning, and Programming languages like Python and R. You should also have working knowledge of big data platforms like Hadoop and Spark. They regularly use data manipulation and visualization tools like SQL, Tableau, or Power BI.

We can also use excel for making interactive dashboards, but still, it has lots of limitations. A data scientist should have a broad knowledge of technical skills and be comfortable working with various tools. Then, they can extract insights from data useful for making data-driven decisions.

Conclusion

The demand for data scientists has increased due to large data production. It is also an appealing designation in the market which offers an amazing salary package. But, before becoming a data scientist, it is also necessary to know the other side of the coin. It’s not a desk job, and one can’t be settled after becoming a data scientist. You need to work with other departments, need to keep yourself updated with the latest technologies and should have coding knowledge. Understand all the above factors and then decide if this job is the right fit for you or not.

Key Takeaways

  • A data scientist job requires a lot of commitment and time, with most of your daily time spent on data cleaning, which is repetitive and tedious.
  • Data scientists can’t work independently and must collaborate with other professionals like data analysts, stakeholders, and SMEs for any project.
  • You need to update your knowledge of new trends, technologies, and methods on a regular basis to keep up with the competition.
  • Coding knowledge is required for a data scientist role, and you need to write clean, efficient, and maintainable code for building reproducible and scalable data pipelines.
  • Data scientists must work with large datasets requiring specialized tools and programming languages like Python, R, and SQL. Hence, alone knowledge of excel is not enough.
Shweta Rawat
Shweta Rawat

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