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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

How to Become a Data Scientist in 2025?

Himanshi Singh
Himanshi Singh Last Updated : 02 Jan, 2025
7 min read

Have you ever wondered what Data Scientists actually do all day? They analyze sales data to boost profits, build machine learning models that predict user behavior, and even harness the power of AI to solve some of the biggest challenges companies face. But how do you get there—especially if you’re starting from scratch?

In this article, we’ll walk through a 12-month roadmap designed to take you from a total beginner to an advanced Data Scientist. Whether you’re just starting out or looking to level up your skills, this guide will help you navigate the journey. Let’s dive in!

Downlaod the roadmap to become a Data Scientist in 2025!

Table of contents

Step 1: Learn to Read Data (Months 1-2)

The first two months are all about laying the groundwork. Focus on these key areas:

  • Python Fundamentals:
    • Start with the basics: data types, functions, loops, and control flow.
    • Dive into libraries like pandas for data manipulation and numpy for numerical computations.
    • Learn data visualization with matplotlib and seaborn to create charts and graphs that reveal trends and outliers.
  • Data Cleaning & Preprocessing:
    • Practice handling messy data: remove duplicates, handle missing values, and correct inconsistencies.
    • Learn techniques like outlier detection, data normalization, and feature scaling.
    • Work with real-world datasets to understand the importance of clean data for accurate analysis.
  • SQL for Data Retrieval:
    • Master SQL basics: SELECT, WHERE, GROUP BY, JOIN, and aggregate functions.
    • Practice querying databases using platforms like MySQL, PostgreSQL, or free tools like SQLite.
    • Explore advanced SQL concepts like subqueries, window functions, and indexing.

Learning Data Visualization

  • Data Visualization with BI Tools:
    • Experiment with tools like Power BI or Tableau to create interactive dashboards.
    • Learn to present data insights effectively to stakeholders.
    • Practice storytelling with data to make your visualizations impactful.
  • Cloud Basics (AWS):
    • Get familiar with cloud platforms like AWS.
    • Learn to spin up an EC2 instance, store data in S3, and use SageMaker for basic machine learning tasks.
    • Understand the importance of cloud computing in modern data science workflows.
  • Basic Statistics:
    • Learn foundational concepts: mean, median, standard deviation, and distributions (normal, binomial).
    • Understand hypothesis testing, p-values, and confidence intervals.
    • Apply statistical methods to analyze datasets and draw meaningful conclusions.
  • GenAI Tools:
    • Use tools like ChatGPT or Claude to debug code, brainstorm ideas, or explain complex concepts.
    • Always verify AI-generated answers and use these tools as supplements to your learning.

By the end of Month 2, you should have completed a couple of small projects—like a sales analysis or a simple dashboard. For a deeper dive, check out Practical Statistics for Data Scientists by Peter Bruce & Andrew Bruce.

Reading List:

Step 2: Prediction and Forecasting (Months 3-4)

In Step 2, we expand from data cleaning to building predictive models for both structured and unstructured data.

Structured Data — Prediction & Forecasting

  • Machine Learning Fundamentals:
  • Time Series Analysis:
    • Learn models like ARIMA, SARIMA, and Prophet for forecasting.
    • Explore advanced techniques like RNNs and LSTMs for time series data.
    • Work on projects like predicting stock prices, sales trends, or website traffic.
  • Practical Work:
    • Participate in Kaggle competitions like House Prices or Store Sales Forecasting.
    • Build mini-projects like a spam filter, customer segmentation model, or sales forecasting pipeline.

Unstructured Data — Text, Audio, Image

  • Reading & Interpreting Unstructured Data:
    • For text: Learn tokenization, stemming, lemmatization, and sentiment analysis.
    • For audio: Explore speech recognition using MFCC transformations and libraries like Librosa.
    • For images: Start with basic classification using OpenCV or PIL.
  • Intro to Deep Learning:
    • Learn neural network basics: weights, biases, activation functions, and backpropagation.
    • Explore CNNs for image classification and RNNs for sequential data.
    • Work through tutorials like MNIST digit classification or IMDB sentiment analysis.
  • Hands-On Practice:
    • Try beginner ML/DL competitions—like sentiment analysis or basic image classification.
    • Experiment with projects like object detection or topic modeling.

For reference, check out Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron.

Reading List:

Step 3: Model Deployment & Monitoring (Months 5-6)

Time to make your models useful in the real world. Step 3 focuses on deployment and monitoring.

  • Deployment Workflows:
    • Package your ML app into a Docker container for easy deployment.
    • Explore Kubernetes for scaling and managing containerized applications.
  • Model Serving & Monitoring:
    • Use MLflow to track experiments, log parameters, and manage model versions.
    • Monitor model performance in production with tools like Prometheus and Grafana.
    • Learn about A/B testing and drift detection to ensure model reliability.
  • APIs for Inference:
    • Build REST APIs using Flask or FastAPI for real-time or batch inference.
    • Learn to integrate your model with web or mobile applications.
  • Career Development:
    • Update your resume and LinkedIn profile with your new skills.
    • Showcase your projects on GitHub to build a strong portfolio.

For a deeper dive, read Building Machine Learning Pipelines by Hannes Hapke & Catherine Nelson.

Reading List:

Step 4: Get a Data Science Internship (Months 7-8)

Nothing beats hands-on experience. Apply for internships to solidify your skills.

  • Finding the Right Internship:
    • Look for roles titled “Data Science Intern” or “ML Intern” on platforms like LinkedIn, Indeed, or your university’s career services.
    • Tailor your resume to highlight relevant skills and projects.
  • Practical Implementation:
    • Work with real-world datasets that are often messy and incomplete.
    • Collaborate with domain experts to understand business problems and data requirements.
  • Hackathons & Internal Competitions:
    • Participate in hackathons to hone your problem-solving skills under tight deadlines.
    • Learn to work in teams and present your solutions effectively.
  • Soft Skills:
    • Develop communication skills to explain technical concepts to non-technical stakeholders.
    • Practice time management to balance multiple tasks and deadlines.

For an insider’s perspective, read The Data Science Handbook by Carl Shan and others.

Reading List:

Step 5: Pick a Specialization — NLP or CV (Months 9-10)

Now that you’re comfortable with the foundations, it’s time to specialize.

NLP Path:

CV Path:

  • Focus on object detection (YOLO, Faster R-CNN) and segmentation (Mask R-CNN).
  • Learn image augmentation techniques to improve model performance.
  • Optimize models for real-time inference using GPUs.
  • Use advanced frameworks like TensorFlow and PyTorch for computer vision tasks.

Build a big project—like a custom QA system or a real-time object detection app—to showcase your expertise. For deeper reading, NLP enthusiasts can check out Speech and Language Processing by Dan Jurafsky & James H. Martin, and CV enthusiasts might love Deep Learning for Vision Systems by Mohamed Elgendy.

Reading List:

Step 6: Transformers, Diffusion Models & GenAI (Months 11-12)

The final step is to explore the frontiers of AI—Generative AI using Transformers, GANs, and Diffusion Models.

For NLP Specialists (Transformers):

For CV Specialists (Diffusion & GANs):

  • Explore GANs (Generative Adversarial Networks) for tasks like image translation and style transfer.
  • Learn about diffusion models for image generation and in-painting.
  • Work on projects like synthetic data creation, image restoration, or artistic style generation.

This stage is cutting-edge and will set you apart. For deeper insights, read Natural Language Processing with Transformers by Tunstall, von Werra, and Wolf, or Generative Deep Learning by David Foster.

Reading List:

Conclusion

There you have it—a comprehensive 12-month roadmap to becoming a Data Scientist in 2025. From mastering the basics of Python and SQL to di:ving into machine learning, deploying models, and specializing in cutting-edge fields like NLP and Computer Vision, this plan equips you with the skills needed to thrive in the data science industry.

The journey to becoming a Data Scientist is challenging but incredibly rewarding. By following this roadmap, you’ll not only gain technical expertise but also develop the problem-solving mindset and practical experience that employers value. Remember, consistency and curiosity are your greatest allies.

So, which step are you most excited about? Whether you’re just starting with Python or ready to explore the frontiers of Generative AI, the future of data science is yours to shape. Best of luck on your journey—may it be filled with discovery, growth, and success!

Frequently Asked Questions

Q1. What is the focus of the first two months in this roadmap?

A. The first two months emphasize foundational skills, including Python programming, data manipulation with pandas and numpy, data visualization, SQL for querying databases, basic statistics, and cloud basics using platforms like AWS. You’ll also learn data cleaning and preprocessing techniques and create small projects like sales analysis or dashboards.

Q2. Why is learning data cleaning and preprocessing important?

A. Data cleaning and preprocessing are essential to handle messy data, remove duplicates, address missing values, and normalize datasets. This ensures that the data is accurate and reliable, leading to better model performance and meaningful analysis.

Q3. What are the main machine learning concepts covered in months 3-4?

A. These months cover both supervised learning (e.g., linear regression, logistic regression, random forests) and unsupervised learning (e.g., K-means clustering). You’ll also explore time series forecasting using ARIMA and LSTMs, along with basic deep learning concepts like CNNs for image classification and RNNs for sequential data.

Q4. What kind of projects can I work on during the prediction and forecasting stage?

A. Projects include predicting stock prices, sales trends, or website traffic using structured data. For unstructured data, you can try sentiment analysis, spam filtering, or image classification tasks like MNIST digit recognition.

Q5. How do I deploy machine learning models in months 5-6?

A. You’ll learn to package models into Docker containers, use Kubernetes for scaling, and deploy APIs with Flask or FastAPI. Additionally, you’ll monitor model performance using tools like Prometheus and Grafana, and manage experiments with MLflow.

Himanshi Singh
Himanshi Singh

I’m a data lover who enjoys finding hidden patterns and turning them into useful insights. As the Manager - Content and Growth at Analytics Vidhya, I help data enthusiasts learn, share, and grow together. 

Thanks for stopping by my profile - hope you found something you liked :)

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Responses From Readers

Clear

Prabhakar Reddy
Prabhakar Reddy

Thank you so much, it looks promising path to become Data Scientist. I will look forward and follow this learning path. And make it as 2021 not 2020, a the end of below sentence "you’d be in a great position to start cracking data science interviews by the end of 2020."

123

Harpreet Singh
Harpreet Singh

Should I enroll for "Introduction to Python" before this course? Or is it included in this course.

123

Pulkit Sharma
Pulkit Sharma

Hi Harpreet, Python course is included in this learning path.

123
Inderpreet Kaur
Inderpreet Kaur

Thanks for writing this in depth post. You covered every angle. One word to say, I love it!

123

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