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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

Top 8 Coding Platforms for Data Science Beginners

Pankaj Singh
Pankaj Singh Last Updated : 24 Apr, 2024
6 min read

Introduction

Data science is an interdisciplinary field encompassing statistics, mathematics, programming, and domain knowledge to derive insights and knowledge from it. With the democratization of data and overwhelming pressure on decision-makers to make data-driven decisions in every nook and corner, the market for data scientists has spiked unprecedentedly. But it can become overwhelming for beginners to navigate, especially when it comes to which coding platform is the best to commence the journey. This article will discuss the top 8 well-suited coding platforms for beginning one’s data science journey and receiving optimal support and coverage.

data science

Table of contents

Why You Would Need a Coding Platform for Data Science Beginners? 

Several reasons make coding platforms for data science beginners essential. First, they provide a structured learning path and introduce concepts, programming languages, and relevant libraries in a well-organized manner. As a result, beginners gain a solid foundation without feeling overwhelmed. These platforms also have interactive coding environments, which allow users to write and execute code directly. Consequently, users can practice their coding while learning. Furthermore, coding platforms usually include access to real-world datasets and projects. This helps the transition from theory to application and facilitates readiness for real-world data science tasks. 

Coding platforms for beginners also offer exercises and challenges related to complex topics. These platforms simplify difficult concepts into smaller pieces that are easier to understand and practice. Additionally, many coding platforms offer vibrant online communities. Users can ask questions, seek help, and collaborate with peers or mentors. Other platforms have assessment tools and feedback mechanisms to help beginners see where they can improve and track their learning progress. Finally, these coding platforms are accessible and flexible. Users can include them in their schedules and specific learning preferences. Therefore, it is more accessible for beginners to start their data science journey.

Coding Platforms for Data Science Beginners

Here are some of the coding platforms for data science beginners:

LeetCode

Official Link: LeetCode

Coding Platforms for Data Science

LeetCode is a popular online platform that provides thousands of coding challenges and interview questions. Its primary goal is to help developers improve their problem-solving abilities and prepare for professional interviews. LeetCode is a user-friendly online source that presents a wide range of solutions, making it an excellent resource for coders at any level. It also serves as a source for code interview problem practice, offering hundreds of real interview questions from some leading tech firms.

HackerRank

Official Link: HackerRank

Coding Platforms for Data Science

HackerRank is a well-known coding practice platform featuring coding challenges, competitions, and skill evaluations. It supports various programming languages and equips developers with an interactive zone to perfect their programming skills. HackerRank is also favored by companies for recruitment and individuals looking to practice their coding. It is the world’s leading technology recruiting platform and has consistently been the top recommendation for recruiters to test developers’ skills for the past decade. 3,000+ global enterprises trust it. HackerRank also equips tech recruiters and hiring managers with an impartial lens to measure talent during each stage of the recruitment process, allowing companies to find expert developers to boost their innovation processes.

CodeChef

Official Link: CodeChef

Coding Platforms for Data Science

CodeChef was founded as an extraordinary initiative to drive talented young coders into the world of algorithms, computer programming, and competitive coding. The platform presents a top tier of monthly contests, including the Long Challenge, CookOff, LunchTime, and Starters, with top performers graciously rewarded for their victory. However, beyond these prominent monthly contests, the platform welcomes the programming community, including institutions and student bodies, to run contests. CodeChef’s platform has been adopted by colleges and corporations in India and worldwide for their internal assessments and external coding challenges. On average, the platform has more than 30 leading external contests, which defines CodeChef as a hub of programming excellence and a brand of team code battles.

CoderByte

Official Link: CoderByte

Coding Platforms for Data Science

CoderByte is a web-based platform that promotes coding challenges and puzzles to help programmers develop problem-solving skills. It is the number one platform for technical assessments, interviews, and projects. It offers multiple challenges across programming languages and difficulty levels. The great user interface and extensive solutions make CoderByte a valuable coding practice platform.

FreeCodeCamp

Official Link: FreeCodeCamp

Coding Platforms for Data Science

FreeCodeCamp is a renowned non-profit organization that provides a comprehensive curriculum and interactive learning platform for individuals interested in web development. It offers a combination of coding tutorials, projects, and certifications, enabling learners to acquire practical skills and build a professional portfolio.

Here, you can Learn to code for free, build projects, and earn certifications. So, what are you waiting for? Let’s get started.

Codewars

Official Link: Codewars

Coding Platforms for Data Science

Codewars is a gamified coding platform that presents coding challenges through “kata” exercises. It offers various challenges spanning various programming languages and difficulty levels. Codewars encourages a collaborative learning environment where users can solve challenges, compare solutions, and engage with a vibrant coding community.

Attain expertise via multiple practice questions.

Enhance your developmental abilities by engaging in code kata sessions alongside your peers, where you’ll constantly confront and elevate your coding proficiency.

StrataScratch

Official Link: StrataScratch

Coding Platforms for Data Science

StrataScratch is a coding platform designed for data science and machine learning enthusiasts. It offers various challenges and coding exercises covering various aspects of data analysis, machine learning algorithms, and data manipulation techniques. StrataScratch provides a practical learning experience for aspiring data professionals.

Codecademy

Official Link: Codecademy

Coding Platforms for Data Science

Codecademy, an interactive online platform based in the United States, provides complimentary coding courses across 12 diverse programming languages. These include Python, Java, Go, JavaScript, Ruby, SQL, C++, C#, Swift, and markup languages like HTML and CSS. It provides a structured learning experience with hands-on exercises, projects, and quizzes, making it an excellent resource for beginners and those seeking to expand their coding skills.

Bonus Chimmed in For You

DataHack

Official Link: DataHack

Coding Platforms for Data Science

DataHack Analytics Vidhya is a premier online destination tailored for mastering data science. Its interactive coding challenges, engaging projects, and structured learning journeys encompass vital subjects like Python programming, data analysis, and machine learning. With its hands-on methodology and vibrant community, DataHack is the ideal platform for novices seeking to hone practical data science abilities.

Empowering the Future AI Trailblazers

At the forefront of nurturing AI talent lies Avian’s commitment to providing comprehensive insights, top-tier courses, dynamic contests, and a supportive network of industry pioneers. Analytics Vidhya is dedicated to fostering the next generation of AI leaders through immersive learning experiences and invaluable connections.

Talking about the project offered by DataHack Analytics Vidhya:

  1. You can Build a solution to automate the loan approval process for a retail bank using Machine Learning & take your first step in the Data Science World!
  2. Practice on “Sales of a given product at a retail store can depend on store and product attributes. Build a data science solution to predict the same for ‘Big Mart’!”

And many more projects to get hands-on experience and connect with like-minded data scientists.

Conclusion

To sum it up, choosing the suitable online coding platform among the provided list of data science platforms for a beginner depends on various aspects, including the coding platform’s learning approach, course content, interactive coding environments, community support, and alignment with individual learning goals and preferences.

In addition, many first-class coding platforms also provide free introductory courses or a trial period, making it ideal to try some of them before choosing a particular platform. Nonetheless, data science is a never-ending learning adventure, and newcomers should be patient, tenacious, and open-minded toward various resources and platforms. Combined with practice, hands-on projects, and a few years of learning the ABCs of professional data science, these coding platforms can set the formative basis for fulfilling data science career paths and keeping up to date with this ever-changing domain.

I hope this article helped you with coding platforms for data science. Moreover, if you are looking for a Certified AI & ML course, go for our BlackBelt Plus Program on Artificial Intelligence and Machine Learning.

Pankaj Singh
Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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