5 answers
Adarsh
Student
New Delhi, Delhi, India
1
Question
Asked
410 views
Optimized path way to become a data scientist?
I am currently a college student and passionate about machine learning and automating tasks with AI.
What is the best optimized path way to become a data scientist and AI engineer.
Login to comment
4 answers
Vibha Chaudhary
Data Engineer - Manager
5
Answers
Wesley Chapel, Florida
Updated
Vibha’s Answer
Hi Adarsh, breaking it down into steps
Step 1: Build core foundations
Learn basic programming (Python first), statistics, probability, and linear algebra. At the same time, get comfortable with data tools like Excel, SQL, and basic data visualization. This is about thinking logically and becoming data-literate.
Step 2: Transition into data work
Start with data analysis projects: clean datasets, explore patterns, and communicate insights. Use Python (pandas, NumPy, matplotlib) and simple machine learning models. This step bridges you into the data world even if your major isn’t technical.
Step 3: Choose a direction
Decide whether you’re leaning toward Data Science (analysis, modeling, experiments) or AI Engineering (building and deploying ML systems). Tailor your learning accordingly—more statistics and modeling for data science; more software engineering, APIs, and systems for AI engineering.
Step 4: Build proof of skill
Create a small but focused portfolio: 3–5 solid projects, internships, research work, hackathons, or open-source contributions. Employers care more about what you’ve built than what you studied.
Step 5: Get real experience and iterate
Apply for internships, junior roles, or assistant positions related to data or software. Learn on the job, strengthen gaps, and gradually move into more advanced ML and AI work.
This pathway keeps things realistic, efficient, and flexible—perfect for someone starting outside the field but aiming to break in.
Step 1: Build core foundations
Learn basic programming (Python first), statistics, probability, and linear algebra. At the same time, get comfortable with data tools like Excel, SQL, and basic data visualization. This is about thinking logically and becoming data-literate.
Step 2: Transition into data work
Start with data analysis projects: clean datasets, explore patterns, and communicate insights. Use Python (pandas, NumPy, matplotlib) and simple machine learning models. This step bridges you into the data world even if your major isn’t technical.
Step 3: Choose a direction
Decide whether you’re leaning toward Data Science (analysis, modeling, experiments) or AI Engineering (building and deploying ML systems). Tailor your learning accordingly—more statistics and modeling for data science; more software engineering, APIs, and systems for AI engineering.
Step 4: Build proof of skill
Create a small but focused portfolio: 3–5 solid projects, internships, research work, hackathons, or open-source contributions. Employers care more about what you’ve built than what you studied.
Step 5: Get real experience and iterate
Apply for internships, junior roles, or assistant positions related to data or software. Learn on the job, strengthen gaps, and gradually move into more advanced ML and AI work.
This pathway keeps things realistic, efficient, and flexible—perfect for someone starting outside the field but aiming to break in.
Laila B
12
Answers
New York, New York
Updated
Laila’s Answer
Hey Adarsh! Great question. Here's a pathway to becoming a DS/AI Engineer:
1. Build a Strong Foundation
Math: Focus on linear algebra and calculus to understand how models learn.
Stats: Study probability and hypothesis testing to validate results.
2. Develop Core Skills
SQL and Python: Essential skills. Get good at using Pandas and writing SQL queries.
Classical Machine Learning: Learn Scikit-learn for techniques like regression and random forests.
3. Gain the AI Advantage
Deep Learning: Get comfortable with PyTorch.
Generative AI: Explore RAG and agentic workflows to create AI that performs tasks effectively.
4. Quick Certifications
Consider courses from Google or IBM on Coursera for basics, and DeepLearning.AI for advanced AI concepts.
Master the "Big Three" tools - Focus on Python (for logic), SQL (for data), and GitHub (to show off your work)
1. Build a Strong Foundation
Math: Focus on linear algebra and calculus to understand how models learn.
Stats: Study probability and hypothesis testing to validate results.
2. Develop Core Skills
SQL and Python: Essential skills. Get good at using Pandas and writing SQL queries.
Classical Machine Learning: Learn Scikit-learn for techniques like regression and random forests.
3. Gain the AI Advantage
Deep Learning: Get comfortable with PyTorch.
Generative AI: Explore RAG and agentic workflows to create AI that performs tasks effectively.
4. Quick Certifications
Consider courses from Google or IBM on Coursera for basics, and DeepLearning.AI for advanced AI concepts.
Laila recommends the following next steps:
Pramit Yeole
AI Engineer
1
Answer
San Jose, California
Updated
Pramit’s Answer
Hey Adarsh, Outlining the basics to becoming a Data Science in todays market, this is more about building your foundation first:
Foundation:
- Statistics (Advanced)
- Probability
- Calculus & Linear Algebra
Programming:
- Python
- SQL
Machine Learning:
- ML Algorithms & the math behind them
- Ideology behind framing Machine Learning problems
Deep Learning:
- Neural Networks & the math behind them
- Computer Vision
- Transformers
- Large Language Models
Generative AI:
- Learn the basics of how LLMs function
- RAG
- AI Agents
- Building AI Agentic workflows
Hands-on:
- Pick any dataset and begin analysing the data for any patterns & statistical analysis
- Important thing is to devise a hypothesis for the data you picked.
- Try to frame a Machine Learning problem based on the data you choose & build at least 2 Machine Learning models for the problem.
- Similarly, pick any unstructured documents and develop a chatbot or a GenAI based AI Agentic solution
In addition to technical aspects, few things that can further strengthen your path to becoming a Data Scientist is identifying the industries or domains you find interesting - this will further help you in narrowing down your options in your career.
Foundation:
- Statistics (Advanced)
- Probability
- Calculus & Linear Algebra
Programming:
- Python
- SQL
Machine Learning:
- ML Algorithms & the math behind them
- Ideology behind framing Machine Learning problems
Deep Learning:
- Neural Networks & the math behind them
- Computer Vision
- Transformers
- Large Language Models
Generative AI:
- Learn the basics of how LLMs function
- RAG
- AI Agents
- Building AI Agentic workflows
Hands-on:
- Pick any dataset and begin analysing the data for any patterns & statistical analysis
- Important thing is to devise a hypothesis for the data you picked.
- Try to frame a Machine Learning problem based on the data you choose & build at least 2 Machine Learning models for the problem.
- Similarly, pick any unstructured documents and develop a chatbot or a GenAI based AI Agentic solution
In addition to technical aspects, few things that can further strengthen your path to becoming a Data Scientist is identifying the industries or domains you find interesting - this will further help you in narrowing down your options in your career.
Sanjay Kumar
Software Developer
2
Answers
Dehradun
Updated
Sanjay’s Answer
Hello Adarsh,
Following could be the most optimized way to learn AI/ML:
1. Build Strong Foundations in Mathematics:
Essentials topics for AIML:
• Linear Algebra (vectors, matrices, eigenvalues)
• Calculus (derivatives, gradients)
• Probability & Statistics (distributions, Bayes rule)
2. Programming (Python)
• Python fundamentals with NumPy, Pandas,Seaborn
3. Learn Core Data Science & ML
Understand:
• Regression, Classification
• Decision Trees, Random Forests, XGBoost
• Clustering (K-means, DBSCAN)
• PCA
Learn both theory and hands-on implementation
4. Data Cleaning & Feature Engineering
This is 70% of real-world data science:
• Handling missing values
• Outliers
• Scaling/encoding
• Time-series processing
5. Deep Learning Basics
Learn:
• Neural Networks
• CNNs
• RNNs, LSTMs
• Transfer Learning
Frameworks:
• TensorFlow
• PyTorch (industry-preferred)
6. Generative AI & LLMs
You must learn:
• Transformer architecture
• Fine-tuning LLMs
• Prompt engineering
• Vector databases
• RAG (Retrieval-Augmented Generation)
• Model deployment
Tools to master:
• Hugging Face
• LangChain
• OpenAI APIs
• Ollama / Llama models
Start with the basics and do some projects by own. Data to model are easily available over internet, one example is Kaggle. Once you start learning , further path will be opened automatically.
Start with smaller project like data cleaning, visualization
Then after building base, start to create your own model.
Following could be the most optimized way to learn AI/ML:
1. Build Strong Foundations in Mathematics:
Essentials topics for AIML:
• Linear Algebra (vectors, matrices, eigenvalues)
• Calculus (derivatives, gradients)
• Probability & Statistics (distributions, Bayes rule)
2. Programming (Python)
• Python fundamentals with NumPy, Pandas,Seaborn
3. Learn Core Data Science & ML
Understand:
• Regression, Classification
• Decision Trees, Random Forests, XGBoost
• Clustering (K-means, DBSCAN)
• PCA
Learn both theory and hands-on implementation
4. Data Cleaning & Feature Engineering
This is 70% of real-world data science:
• Handling missing values
• Outliers
• Scaling/encoding
• Time-series processing
5. Deep Learning Basics
Learn:
• Neural Networks
• CNNs
• RNNs, LSTMs
• Transfer Learning
Frameworks:
• TensorFlow
• PyTorch (industry-preferred)
6. Generative AI & LLMs
You must learn:
• Transformer architecture
• Fine-tuning LLMs
• Prompt engineering
• Vector databases
• RAG (Retrieval-Augmented Generation)
• Model deployment
Tools to master:
• Hugging Face
• LangChain
• OpenAI APIs
• Ollama / Llama models
Start with the basics and do some projects by own. Data to model are easily available over internet, one example is Kaggle. Once you start learning , further path will be opened automatically.
Sanjay recommends the following next steps:
I'm excited to put your great advice to good use!