Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

Learning Path to Become a Data Scientist in 2025

A
Akash Sharma Last Updated : 16 Dec, 2024
13 min read

The field of data science is experiencing unprecedented growth, making it an attractive career path for aspiring professionals. As we approach 2025, the demand for data scientists is projected to surge, with the U.S. Bureau of Labor Statistics forecasting a remarkable 36% increase in employment for data science roles by 2031, positioning it among the most sought-after careers in technology. This trend reflects a broader shift as companies increasingly rely on data-driven insights to enhance their operations and drive growth.

Recent statistics underscore the urgency of this demand. The World Economic Forum anticipates 11.5 million job openings for data-related roles by 2026, highlighting a significant skill gap that needs to be addressed. In India alone, the data science industry is expected to grow at an annual rate of 33.7%, driven by rapid digital transformation and the increasing reliance on data analytics across various sectors. 

As organizations strive to leverage vast amounts of structured and unstructured data, they are prepared to invest significantly in skilled professionals who can navigate this complex landscape. Join us on a transformative journey as we unveil a step-by-step blueprint to become a data scientist in 2025 by leveraging the cutting-edge insights and methodologies that define this thrilling era of technological evolution.

Skills Required to Become a Data Scientist in 2024

Table of contents

Overview of Data Scientist Learning Path 2025

At the core of this roadmap lies a simple equation: To become a Data Scientist, you need the right set of tools, a diverse range of techniques, and the skill to design impactful solutions. These skills complement each other multiplicatively, unlocking new possibilities. For instance, mastering Python, a tool, empowers you to delve into techniques like Exploratory Data Analysis (EDA).

In this comprehensive learning roadmap for aspiring Data Scientists in 2025, we offer a step-by-step framework, detailing the essential tools and techniques to master, coupled with the cultivation of the highly sought-after design skill.

On this note, let the exploration begin!

Quarter 1: Foundations & Programming (January – March)

This quarter, the roadmap to becoming a data scientist focuses on the following topics, with all necessary resources provided to ensure a comprehensive learning experience.

1. Python Programming Fundamentals

  • Core Python (data types, control structures, functions, OOP basics): Get the hang of Python’s building blocks, writing simple scripts that don’t break easily.
  • Essential libraries (NumPy for arrays, Pandas for data manipulation): Use NumPy and Pandas to handle messy data so you can slice, dice, and reshape at will.
  • Best practices and code organization (functions, modules, virtual environments): Keep your code tidy, modular, and running smoothly in isolated environments.

Resources:

2. Statistics and Probability Essentials

  • Descriptive statistics (central tendency, dispersion, distributions): Quickly size up your data’s behaviour with measures like averages and how spread out it is.
  • Probability concepts and common distributions: Know the odds of different outcomes and learn which distributions fit your data’s quirks.
  • Hypothesis testing and A/B testing fundamentals: Test your assumptions, compare outcomes, and see which version works better.

Resources to become a data scientist:

3. SQL and Database Integration

  • SQL fundamentals (queries, joins, window functions): Effortlessly query and combine tables, and run rolling calculations right in your database.
  • Modern data warehousing (Databricks, Snowflake basics): Taste the power of cloud warehouses to store and process data at scale without breaking a sweat.
  • Database integration with Python (connections, basic operations): Hook Python into your database so you can run queries and tweak data straight from your code.

Resources to become a data scientist:

Now that you have learned about Python and SQL language, along with mathematical concepts like Statistics and Probability, it’s time to go a little further and apply these concepts to learn about Exploratory data analysis, where we will explore data and fetch hidden trends and insights from it.

4. EDA (Exploratory Data Analysis)

  • Data cleaning and preprocessing using Python: Turn messy raw data into something neat and workable before any fancy modelling.
  • Visualization using Matplotlib/Seaborn: Whip up visuals that reveal patterns, trends, and outliers without scaring off non-tech folks.
  • Dashboard creation using Power BI/Tableau basics: Present your insights on easy-to-use dashboards so decisions can be made at a glance.

Resources to become a data scientist:

Now when you have pretty much all the skills you need to be a Data Analyst, it’s time you learn about how to frame a business problem how to approach the problem, and define clear metrics and hypotheses for business. Along the way, you need to work on your communication skills as well, as a Data Analyst or a Data Scientist, you will have to communicate your findings and work with stakeholders and you must have good communication skills and storytelling skills to put up your points with power and making them relevant.

5. Business Problem Frameworks

  • Learn how to break down a real-world business problem and convert it into a data problem using TOSCAR and CASED frameworks. Apply these structured frameworks so you don’t wander blindly when solving business puzzles.
  • Metrics and KPI development: Define clear success measures so you know when you’ve won.

Resources:

6. Communication Skills for Data Science

  • Technical communication (documentation, code presentation): Write docs and show code that others can grasp without scratching their heads.
  • Business communication (stakeholder management, non-technical presentations): Share insights in simple terms so even non-experts get the story.
  • Data storytelling fundamentals: Craft a narrative around your numbers that sparks interest and drives action.

Resources:

When all this is done, you should get into the world of the cloud and learn the basics of the cloud environment and its services, as cloud platforms provide scalable storage, high computational power, and tools for big data processing, making them essential for handling large datasets and running complex data science workflows efficiently.

7. Cloud Environment Basics (AWS Focus)

  • Core services (EC2, S3, basic networking): Launch servers, store files, and get comfy running things in the cloud.
  • Analytics tools (QuickSight or similar): Visualize and explore your data online without juggling extra installs.
  • Basic security and cost management: Keep your stuff safe and your cloud bill in check by managing access and usage.

Resources:

Technical Projects

  • SQL project: Market basket analysis using transaction data
    Spot which products hang out together so you can boost your cross-selling game.
  • Python project: Analyze customer data using Python and basic statistics:
    Turn raw customer info into actionable insights that guide smarter decisions.
  • Statistics project: A/B test analysis with hypothesis testing
    See if your new website layout actually beats the old one or just got lucky.
  • Sales Performance Analysis:
    • Analyse e-commerce sales data using Python/SQL: Dig into sales records to figure out what drives revenue up or down.
    • Create interactive dashboards using Power BI: Present insights in a sleek, interactive dashboard that is easy to understand.

By the end of the first quarter, you’ll have a solid foundation in programming languages and basic mathematical concepts. This is the perfect time to start pursuing Data Analyst roles. Leverage tools like ChatGPT to quickly craft a polished Resume, Cover Letter, and LinkedIn profile. Furthermore, dedicating time to stay updated on the latest advancements in the Generative AI ecosystem will be highly beneficial during this phase.

End of Quarter Goals

Listed below are the end-of-quarter goals on the roadmap to becoming a data scientist.

  • Portfolio: 2-3 end-to-end analysis projects
    Wrap up a few solid projects so hiring teams know you can do the real thing.
  • Technical Skills: Python, SQL, basic cloud, visualization
    Be comfortable working with data, writing queries, running code in the cloud, and making neat charts.
  • Job Readiness: Ready for entry-level data analyst positions
    Feel confident applying for analyst roles, equipped with just enough know-how to get started.

Quarter 2: Applied Machine Learning & Data Engineering (April – June)

As part of this quarter’s roadmap toward becoming a data scientist, we’ll cover these key areas, supported by curated resources for effective learning.

1. Classical ML & Sikit-learn

  • Linear/Logistic Regression, Decision Trees, SVM with scikit-learn implementation: Build and tune basic models using scikit-learn to solve simple predictive problems.
  • Model evaluation, cross-validation, hyperparameter tuning: Ensure your models perform well by testing them thoroughly and tweaking settings for optimal results.
  • Pipeline creation and model selection strategies: Streamline your workflow by creating pipelines and choosing the best models for your data.

Resources:

2. Feature Engineering

  • Advanced preprocessing (scaling, encoding, handling imbalanced data): Prepare your data by scaling numbers, encoding categories, and balancing classes for better model performance.
  • Feature selection methods (filter, wrapper, embedded): Pick the most important features using different techniques to simplify models and boost accuracy.
  • Automated feature engineering tools and techniques: Speed up feature creation with tools that automatically generate and select the best features for your models.

Resources to become a data scientist:

3. Advanced ML & Ensemble Methods

  • Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost): Enhance your models with powerful ensemble techniques that combine multiple algorithms for better predictions.
  • Stacking and blending techniques: Blend different models to use their strengths and improve the overall performance of your system.
  • Recommendation systems: Create systems that suggest products or content by analyzing user behaviour and preferences by using algorithms like collaborative filtering, and content-based filtering.

Resources:

4. Time Series Analysis

  • Time series preprocessing and feature engineering: Process and clean your dependent data to uncover insights, trends and patterns.
  • ARIMA and SARIMA using statsmodels: Apply forecasting models to predict future values based on past data.
  • Facebook Prophet for forecasting: Use Prophet to handle complex seasonality and make reliable forecasts with minimal tuning.
  • Advanced forecasting with “sktime” library: Explore sophisticated forecasting techniques and leverage the powerful “sktime” library for your projects.

Resources to become a data scientist:

5. Big Data Processing

  • PySpark (RDDs, DataFrames, MLlib): Handle large datasets efficiently using PySpark’s powerful distributed computing capabilities.
  • Dask for parallel computing: Scale your Python workflows with Dask to process data faster across multiple cores or machines.
  • Distributed ML model training: Train machine learning models on big data by distributing the workload across a cluster.

Resources:

6. MLOps Fundamentals

  • Experiment tracking with MLflow: Keep track of your experiments, parameters, and results to streamline your ML workflow.
  • Model versioning and metadata with Weights & Biases: Manage different versions of your models and their metadata to maintain consistency and reproducibility.
  • Model registry and lifecycle management: Organize and oversee your models from development to deployment with a centralized registry.

Resources:

7. Model Deployment

  • REST API development with Flask: Turn your models into web services by creating APIs that others can easily access.
  • Interactive apps with Streamlit and Gradio: Build user-friendly applications to showcase your models and allow others to interact with them.
  • Containerization basics with Docker: Ensure your applications run smoothly anywhere by containerizing them with Docker.

Resources:

8. Cloud ML Development

  • SageMaker (notebooks, training, deployment): Utilize AWS SageMaker to develop, train, and deploy your machine learning models in the cloud.
  • Azure ML Studio/Vertex AI fundamentals: Explore other cloud ML platforms like Azure ML Studio and Google’s Vertex AI for diverse tooling options.
  • Model endpoints and monitoring: Set up endpoints for your models and monitor their performance to ensure they run smoothly in production.

Resources:

Technical Projects

  1. Customer Segmentation & Recommendation System: Group customers based on behaviour and build a system that suggests products they’ll love.
  2. Sales Forecasting using Time Series: Predict future sales trends by analyzing historical sales data with time series models.
  3. ML Model Deployment on Cloud: Deploy your trained machine learning models to the cloud and make them accessible via APIs.

End of Quarter Goals

These are the end-of-quarter goals outlined on the roadmap to becoming a data scientist.

  • Build production-ready ML models: Create and fine-tune machine learning models that are ready to be used in real-world applications.
  • Deploy models as APIs: Learn how to make your models accessible through APIs, allowing other applications to use them seamlessly.
  • Handle large-scale data processing: Master the tools and techniques needed to process and analyze big data efficiently.
  • Ready for junior data scientist positions: Equip yourself with the necessary skills and projects to confidently apply for junior data scientist roles.

Quarter 3: Applied Deep Learning (July – September)

This quarter, the roadmap to become a data scientist includes exploring these topics, with tailored resources available to guide you through.

1. Deep Learning Foundations

  • Neural network architectures in PyTorch and Keras: Start creating various neural networks with PyTorch and Keras to tackle some of the more challenging problems.
  • Loss functions and optimisers, Regularization and dropout techniques: Learn how to train your models efficiently by selecting the right loss functions and using techniques like regularization and dropout to prevent overfitting.
  • Training on GPUs: Speed up your model training by taking advantage of GPUs, which make computations much faster.

Resources:

2. Natural Language Processing

  • NLP (NLTK, spaCy, GENSIM): Get practical experience with NLP tools like NLTK, spaCy, and Gensim to efficiently process and analyze text data.
  • Word embeddings and language models: Learn to turn words into numerical vectors and build models that understand the context of language.
  • Transformer architectures (BERT, GPT family): Know more about advanced transformer models such as BERT and the GPT to tackle more complex language tasks.
  • Hugging Face ecosystem: Use Hugging Face’s libraries and pre-trained models to make your NLP projects smoother and faster.

Resources:

3. Computer Vision

  • CNN architectures (ResNet, EfficientNet): Build powerful image recognition models using state-of-the-art CNN architectures like ResNet and EfficientNet.
  • Transfer learning strategies: Boost your models’ performance by applying transfer learning with pre-trained networks.
  • Image preprocessing and augmentation: Enhance your image data with preprocessing techniques and augmentation to improve model accuracy.

Resources:

4. Deep Learning Deployment

  • TensorFlow Serving: Deploy your TensorFlow models seamlessly with TensorFlow Serving for scalable applications.
  • Model optimization for production: Optimize your models to run efficiently in production environments without sacrificing performance.
  • Cloud deployment (SageMaker Deep Learning containers): Use AWS SageMaker containers to deploy your deep learning models on the cloud effortlessly.
  • GPU instance management: Manage GPU resources effectively to ensure your deployed models run smoothly and cost-effectively.

Resources:

5. Responsible AI

  • Model interpretability (SHAP, LIME): Make your models transparent by using tools like SHAP and LIME to explain their decisions.
  • Bias detection and mitigation: Identify and reduce biases in your models to ensure fair and unbiased outcomes.
  • Model monitoring and drift detection: Keep your models reliable by monitoring their performance and detecting any drift over time.
  • Ethical AI considerations: Adopt ethical practices in AI development to build trustworthy and responsible applications.

Resources:

Technical Projects

  1. Text Classification/Sentiment Analysis System: Build a system that classifies text data and analyzes sentiment using NLP techniques.
  2. Object Detection Application: Create an application that detects and identifies objects within images using CNNs.
  3. End-to-end Deep Learning Pipeline: Develop a complete deep learning pipeline from data preprocessing to model deployment for a specific use case.

End of Quarter Goals

Here are the end-of-quarter goals for your roadmap to becoming a data scientist.

  • Build complex deep-learning models: Develop sophisticated models that tackle challenging data science problems.
  • Deploy GPU-accelerated applications: Gain hands-on experience in deploying models that leverage GPU acceleration for enhanced performance.
  • Understand advanced model architectures: Deepen your knowledge of cutting-edge neural network architectures and their applications.
  • Ready for intermediate data scientist positions: Equip yourself with advanced skills and projects to pursue intermediate-level data scientist roles confidently.

Quarter 4: Specialization & Production (October – December)

Over this quarter, this data scientist roadmap emphasizes these crucial subjects, accompanied by resources designed to enhance your understanding and skills.

1. Version Control & Collaboration

  • Git workflows and best practices: Streamline your coding process and collaborate effectively by adopting standard Git workflows.
  • Code review processes: Improve code quality and share knowledge by participating in regular code reviews.
  • CI/CD for ML projects: Set up automated pipelines to build, test, and deploy your machine learning models seamlessly.

Resources:

2. Production API Development

  • FastAPI for high-performance APIs: Create fast and efficient APIs using FastAPI to serve your machine learning models.
  • API security and testing: Ensure your APIs are secure and reliable by implementing robust testing and security measures.
  • Asynchronous processing: Enhance your applications’ performance by handling multiple requests simultaneously with asynchronous processing.

Resources:

3. Real-time ML Systems

  • Streaming data processing (Kafka): Manage and process live data streams efficiently using Apache Kafka.
  • Online learning systems: Develop models that learn continuously from new data in real time.
  • Real-time feature engineering: Extract and utilize features on the fly to keep your models up-to-date with the latest data.

Resources:

4. Advanced MLOps

  • Model monitoring and alerting: Keep track of your models’ performance in production and set up alerts for any issues.
  • A/B testing frameworks: Compare different model versions to determine which one performs better using A/B testing.
  • SageMaker endpoints and AutoScaling: Deploy your models on SageMaker endpoints and automatically scale resources based on demand.
  • Multi-model deployment: Serve multiple models from a single endpoint to optimize resource usage and deployment efficiency.

Resources:

5. Specialization Tracks

  • Computer Vision Track
    • Advanced object detection (YOLO, Mask R-CNN): Implement cutting-edge object detection models like YOLO and Mask R-CNN for accurate image analysis.
    • SOTA Image segmentation & Object tracking techniques: Master state-of-the-art techniques for segmenting and tracking objects in images and videos.
    • Image generation (VAEs, GANs, Diffusion models): Create realistic images using advanced generative models like VAEs, GANs, and diffusion models.
    • Vision transformers (ViT): Explore the latest vision transformer architectures to enhance your image processing capabilities.
    • Video processing pipelines: Build efficient pipelines to process and analyze video data in real time.
  • NLP & LLMs Track
    • Advanced PyTorch and GPU optimization: Optimize your PyTorch models to run efficiently on GPUs for faster training and inference.
    • Transformer architecture implementation: Implement complex transformer architectures to tackle advanced NLP tasks.
    • LLM fine-tuning techniques: Fine-tune large language models to specialize them for specific applications and datasets.
    • Prompt engineering strategies: Develop effective prompt engineering techniques to improve the performance of language models.
    • RAG applications: Build Retrieval-Augmented Generation applications to enhance model responses with external data.
    • Document processing pipelines: Create pipelines to automate the extraction and analysis of information from large volumes of documents.

Resources:

6. Portfolio Development

  • Production-grade projects in specialization: Develop high-quality projects in your chosen specialization to showcase your expertise.
  • System design and scalability: Design scalable systems that can handle real-world demands and large-scale data.
  • Documentation and testing: Maintain thorough documentation and rigorous testing to ensure your projects are reliable and understandable.
  • Performance optimisation: Optimize your projects for better performance and efficiency, making them production-ready.

Technical Projects

  1. Real-time ML System (e.g., streaming predictions): Build a system that provides real-time predictions by processing streaming data.
  2. Specialized Track Project:
    • CV: Multi-stage video analysis system: Create a comprehensive video analysis system that handles multiple stages of object detection and tracking.
    • NLP: Custom LLM-powered application: Develop an application powered by a fine-tuned large language model to perform specialized tasks.

End of Quarter Goals

Below are the end-of-quarter goals set out on the data science roadmap.

  • Ready for senior data scientist interviews: Prepare for advanced data scientist roles with a strong portfolio and in-depth knowledge.
  • Build production-grade ML systems: Create and deploy robust machine learning systems that are ready for real-world use.
  • Handle real-time data and predictions: Master the skills needed to process and predict data in real-time environments.
  • Specialize in the chosen domain: Deepen your expertise in either Computer Vision or NLP & LLMs to stand out in your field.

How Can You Speed up the Process of Becoming a Data Scientist in 2025?

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Conclusion

Concluding our comprehensive guide to becoming a Data Scientist in 2025, this journey is not just a roadmap; it’s a gateway to embracing the forefront of technological evolution. In a year marked by remarkable technological strides, the landscape of data science and analytics has surged forward, demanding a spectrum of skills to navigate this dynamic field.

By dedicating yourself to diligently following this guide, you’re not just acquiring skills; you’re building a solid foundation. This empowers you to innovate, create, and contribute significantly to the exciting landscape of data science in 2025 and beyond.

Moreover, Join our Analytics Vidhya community platform for an immersive experience. Tailored Data Science and Generative AI community groups await your interests, providing opportunities to learn alongside your peers. Along with that, enjoy free access to live webinars and AMA sessions from industry experts.

A
Akash Sharma

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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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