Data Science & Artificial Intelligence

Data Science & AI Engineering Mastery 2024

10 months
0 enrolled
31 views
Intermediate Level

This comprehensive roadmap covers everything you need to become a proficient data scientist and AI engineer. From data manipulation and statistical analysis to machine learning algorithms, deep learning, and AI system deployment. You'll learn to work with real-world datasets, build predictive models, create intelligent applications, and deploy AI solutions to production. Perfect for beginners looking to start a career in data science or experienced professionals wanting to advance their AI skills.

Login to Enroll View Curriculum
Rating
0.0 (0 reviews)
Details
Duration: 10 months
Level: Intermediate
Steps: 10
Created by: Admin User
Last updated: Sep 01, 2025

Prerequisites

What you should know before starting this roadmap

  • • Basic programming knowledge (Python preferred)
  • • Understanding of fundamental mathematics and statistics
  • • Familiarity with basic algebra and calculus concepts
  • • Logical thinking and problem-solving abilities
  • • Access to a computer with internet connection
  • • Willingness to work with large datasets and complex algorithms

Roadmap Resources

Curated resources to help you on your journey

Visual Learning Path

See the structure, skill levels, and prerequisites at a glance

graph TD S1["1. Python for Data Science\n(Beginner)\n[Foundation]"] style S1 fill:#e0e0e0,color:#222 S2["2. Statistics & Mathematics for Data Science\n(Beginner)\n[Foundation]"] style S2 fill:#e0e0e0,color:#222 S3["3. Data Wrangling & Preprocessing\n(Intermediate)\n[Data Engineering]"] style S3 fill:#e0e0e0,color:#222 S4["4. Exploratory Data Analysis (EDA)\n(Intermediate)\n[Data Analysis]"] style S4 fill:#e0e0e0,color:#222 S5["5. Machine Learning Fundamentals\n(Intermediate)\n[Machine Learning]"] style S5 fill:#e0e0e0,color:#222 S6["6. Deep Learning & Neural Networks\n(Advanced)\n[Deep Learning]"] style S6 fill:#e0e0e0,color:#222 S7["7. Natural Language Processing (NLP)\n(Advanced)\n[NLP]"] style S7 fill:#e0e0e0,color:#222 S8["8. Big Data & Cloud Computing\n(Advanced)\n[Big Data]"] style S8 fill:#e0e0e0,color:#222 S9["9. Model Deployment & MLOps\n(Advanced)\n[MLOps]"] style S9 fill:#e0e0e0,color:#222 S10["10. Capstone Project: AI-Powered Application\n(expert)\n[Capstone]"] style S10 fill:#e0e0e0,color:#222

Learning Path

Follow this structured curriculum to master Data Science & AI Engineering Mastery 2024

1

Python for Data Science

Learning Beginner Foundation

Master Python programming fundamentals specifically tailored for data science and machine learning applications. Learn essential libraries and tools.

50h
Resources:
Python for Data Analysis - Wes McKinney
Document NumPy Documentation
Document Pandas Documentation
Skills:
Python Programming Data Manipulation Data Visualization Statistical Computing Jupyter Development File I/O Operations
2

Statistics & Mathematics for Data Science

Learning Beginner Foundation

Build a strong foundation in statistics, probability, and mathematical concepts essential for understanding machine learning algorithms and data analysis.

60h
Resources:
Course Statistics for Data Science - Coursera
Think Stats - Allen B. Downey
Skills:
Statistical Analysis Probability Theory Hypothesis Testing Regression Analysis Statistical Inference Data Interpretation
3

Data Wrangling & Preprocessing

Learning Intermediate Data Engineering

Learn essential data cleaning, preprocessing, and feature engineering techniques to prepare raw data for machine learning models.

45h
Resources:
Data Wrangling with Python - Jacqueline Kazil
Document Scikit-learn Preprocessing
Skills:
Data Cleaning Feature Engineering Data Preprocessing Data Validation Pipeline Development Data Quality Management
4

Exploratory Data Analysis (EDA)

Learning Intermediate Data Analysis

Master the art of exploring and understanding data through visualization, statistical analysis, and storytelling techniques.

40h
Resources:
Storytelling with Data - Cole Nussbaumer Knaflic
Document Seaborn Visualization Gallery
Skills:
Data Exploration Data Visualization Statistical Analysis Data Storytelling Pattern Recognition Insight Generation
5

Machine Learning Fundamentals

Learning Intermediate Machine Learning

Learn core machine learning concepts, algorithms, and implementation using scikit-learn and other popular libraries.

70h
Resources:
Introduction to Machine Learning with Python - Andreas Müller
Document Scikit-learn User Guide
Skills:
Machine Learning Algorithms Model Evaluation Cross-Validation Feature Engineering Hyperparameter Tuning Model Deployment
6

Deep Learning & Neural Networks

Learning Advanced Deep Learning

Dive into deep learning with neural networks, convolutional networks, recurrent networks, and modern deep learning frameworks.

80h
Resources:
Deep Learning - Ian Goodfellow
TensorFlow Tutorials
Skills:
Neural Networks Deep Learning Computer Vision Natural Language Processing Model Optimization Framework Mastery
7

Natural Language Processing (NLP)

Learning Advanced NLP

Learn to process, analyze, and generate human language using modern NLP techniques and transformer models.

60h
Resources:
Natural Language Processing with Python - Steven Bird
Document Hugging Face Transformers
Skills:
Text Processing Language Models Transformer Architecture NLP Applications Model Fine-tuning Text Generation
8

Big Data & Cloud Computing

Learning Advanced Big Data

Learn to work with large-scale data using distributed computing, cloud platforms, and big data technologies.

55h
Resources:
Learning Spark - Holden Karau
Document AWS Machine Learning
Skills:
Distributed Computing Cloud Platforms Big Data Processing Containerization Scalable Architecture Performance Optimization
9

Model Deployment & MLOps

Learning Advanced MLOps

Learn to deploy machine learning models in production environments and implement MLOps best practices.

50h
Resources:
MLOps Engineering on AWS - Noah Gift
Document MLflow Documentation
Skills:
Model Deployment API Development MLOps Model Monitoring CI/CD Pipelines Production Systems
10

Capstone Project: AI-Powered Application 🎓 Capstone Project

Project expert Capstone

Apply all your skills to build a comprehensive AI-powered application that solves a real-world problem.

100h
Resources:
Building Machine Learning Powered Applications - Emmanuel Ameisen
Document FastAPI Documentation
Skills:
Project Management Full-Stack Development System Integration Production Deployment Technical Communication Problem Solving

What You'll Learn

Skills and knowledge you'll gain from this roadmap

  • • Master Python programming for data science and machine learning
  • • Perform comprehensive data analysis and visualization using pandas, numpy, and matplotlib
  • • Build and evaluate machine learning models for classification, regression, and clustering
  • • Implement deep learning solutions using TensorFlow and PyTorch
  • • Design and optimize database queries for data extraction and manipulation
  • • Deploy machine learning models to production environments
  • • Apply statistical methods and hypothesis testing to real-world problems
  • • Create interactive dashboards and data storytelling presentations

Career Opportunities

Where this roadmap can take your career

  • • Data Scientist
  • • Machine Learning Engineer
  • • AI Engineer
  • • Data Analyst
  • • Business Intelligence Developer
  • • Research Scientist
  • • Quantitative Analyst
  • • Data Engineer
  • • AI Research Engineer
  • • Freelance Data Consultant

Estimated Salary Range

  • India: ₹8 LPA – ₹35 LPA
  • US: $80k – $180k
Sources: Glassdoor, Payscale, LinkedIn Salary Insights, Indeed, AI/ML Salary Surveys
Share this roadmap: