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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

Exclusive Data Science Roadmap 2025 [Step-wise Guide]

Pranshu Sharma
Pranshu Sharma Last Updated : 06 Dec, 2024
5 min read

Introduction

In this step-by-step guide to becoming a Data Scientist, we’ll walk you through the essential skills you need to learn. To acquire these skills, various resources are available, such as MOOCs (Massive Open Online Courses), YouTube Channels, and Blog Pages, which offer comprehensive learning materials. Additionally, you can explore Data Science Community Websites like Kaggle, Driven Data, and Analytics Vidhya, which provide opportunities to gain practical experience with datasets and invaluable insights into diverse Machine Learning techniques. By leveraging these resources, you can embark on a rewarding journey toward mastering the art of Data Science.

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

Table of contents

What Is Data Science?

Data Science is all about using various techniques, algorithms to analyze large amounts of datasets (both structured & Unstructured), to extract useful data insights, thus applying them in various business domains.

Why there’s a demand for Data Scientists?

Data is being generated day by day at a massive rate and in order to process such massive data sets, Big Firms, Companies are hunting for good data scientists to extract valuable data insights from these data sets and using them for various business strategies, models, plans

 

Become A Data Scientist meme

1. Learn Python

The First and Foremost Step Towards Data Science should learning be a programming language ( i.e. Python). Python is the most common coding language, used by the Majority of Data Scientist, because of its simplicity, versatil,ity and being pre-equipped with powerful libraries ( like NumPy, SciPy, and Pandas) useful in data analysis and other aspects in Data Science. Python is an open-source language and supports various libraries.

Resource:

2. Learn Statistics

Become A Data Scientist statistics

If Data Science is a language, then statistics is basically the grammar. Statistics is basically the method of analyzing, interpretation of large data sets. When it comes to data analysis and gathering insights, statistics is as noteworthy as air to us. Statistics help us understand the hidden details from large datasets

Resource:

3. Data Collection

This is one of the key and important steps in the field of Data Science. This skill involves knowledge of various tools to import data from both local systems, as CSV files, and scraping data from websites, using beautifulsoup python library. Scrapping can also be API-based. Data collection can be managed with knowledge of Query Language or ETL pipelines in Python

Resource:

4. Data Cleaning

This is the Step where most of the time is being spent as a Data Scientist. Data cleaning is all about obtaining the data, fit for doing work& analysis, by removing unwanted values, missing values, categorical values, outliers, and wrongly submitted records, from the Raw form of Data. Data Cleaning is very important as real-world data is messy in nature and achieving it with help of various python libraries(Pandas and NumPy)is really important for an aspirant Data Scientist

Resource:

Become a Data Scientist clean the data

5.  Acquaintance With EDA( Exploratory Data Analysis)

Become a Data Scientist EDA

EDA( Exploratory data analysis) is the most important aspect in the vast field of data science. It includes analyzing various data, variables, various data patterns, trends and extracting useful insights from them with help of various graphical and statistic l methods. EDA identifies various pattern which Machine learning algorithm might fail to identify. It includes all Data Manipulation, Analysis, and Visualization.

Resource:

6. Machine Learning & Deep Learning

Machine learning is the core skill required to be a Data Scientist. Machine learning is used to build various predictive models, classification models, etc., and is being used By big firms, Companies to Optimize their planning as per the predictions. For example Car Price prediction

 

Deep Learning on the other hand is and an advanced version of Machine Learning which deploys the use of Neural Network, a framework that combines various machine learning algorithms for solving various tasks, for training data. Various Neural networks are recurrent neural network (RNN) or a convolutional neural network (CNN) etc

For Example: Face Recognition

Resources:

7. Learn Deploying of ML model

Learn Deploying of ML model

Deployment is basically the process of making your Machine Learning Model available to end-users for use. This is achieved by the integration of the model with various existing production environments thus implementing the practical use of the ML model for various Business solutions.

There are many services for deploying your ML model like Flask, Pythoneverywhere, MLOps , Microsoft Azure, Google Cloud, Heroku, etc

Resources:

8. Real-World Testing

Testing and Validation of the Machine Learning Model after Deployment Should Be done In order to check its effectiveness and accuracy. Testing is an Important Step In Data Science for keeping the efficiency and effectiveness of the ML model In check.

There is Various Type Of Testing like A/B, AAB Testing, etc.

9. Exploring and Practicing datasets on Kaggle, Analytics Vidhya

Kaggle

World’s Largest Data Science Communities like Kaggle, Analytics Vidhya is very helpful for getting in touch with various datasets and therefore can be used for practicing Various Data analysis techniques, machine learning algorithms. Competitions being held in these communities are also useful for sharpening the skills of data science, thus helping us to achieve our goal of becoming proficient in Data Science faster.

10. Analytical Curiosity 

The data science field is a field that is evolving at a higher pace, therefore it requires inbuilt curiosity to explore more about the field, regularly updating and learning various skills & techniques.

This is the main skill that will always help us maintaining, updating new skills & concepts, thus preventing us from lagging behind various Data Science technological advancements.

11. Non-Technical Skills

  • Non-Technical includes Teamwork, Communication Skills, Task management, Business understanding, etc
  • Teamwork plays an important role while delivering the result to the firms, companies we are working as data scientists.
  • Communication skills allow us to express our technical ideas, concepts to various non-technical staff/ authorities of the Firm.
  • Task Management involves proper management and planning for delivering the solution.
  • Business understanding/ acumen or the understanding about the industry we are working in is very important for various analyses and effective solutions for the problems in those industries.

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

Pranshu Sharma
Pranshu Sharma

Aspiring Data Scientist | M.TECH, CSE at NIT DURGAPUR

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Vaibhav Nahar

Short and to the point! It was a good read, would like to know more in detail.

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I thought this course introduced the topic of data science very well. I think I have a much better idea how to describe data science and common terms associated with the field (like machine learning).

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Thanks for sharing! This website is very informative. I appreciate this website. How long does it take to become a data scientist from scratch?

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