Data Science Course in Nepal, 4-Month Practical Training with AI
Learn data science and AI from Python fundamentals to machine learning, deep learning, natural language processing, and generative AI. Build and deploy real models using TensorFlow, Scikit-learn, Pandas, and SQL. Live classes at New Baneshwor, Kathmandu with weekend and evening batches for working professionals.
What This Course Is
The Data Science and AI course at Next Minds Infosys is a 4-month, project-intensive training program that takes you from Python fundamentals to building and deploying machine learning models, working with neural networks, and applying natural language processing and generative AI techniques. This is not a surface-level "learn data science in 30 days" overview. You will work with real datasets, build real models, and deploy real applications.
We designed this course around what companies actually hire for. When Fusemachines, Leapfrog Technology or Deerwalk interviews a data science candidate, they do not ask you to define supervised learning. They give you a messy dataset and ask you to clean it, explore it, build a model, evaluate its performance and explain your decisions. Every module prepares you for exactly that kind of assessment.
Data science and AI roles are among the highest-paying technical positions in Nepal. Junior data scientists in Kathmandu earn NPR 50,000 to 80,000 per month, and mid-level professionals earn NPR 1,00,000 to 2,00,000 or more. The demand is driven by Nepal's banking sector, fintech companies, telecom operators and a growing number of outsourcing firms building AI teams for international clients. Remote positions with global companies pay significantly higher.
The fee is NPR 35,000 as of August 2026. Installment payment options are available.
Common Mistakes We See in Every Cohort
We have trained 165+ students in this course. These are the three patterns we correct most often.
Jumping to deep learning before understanding basic ML. Every cohort has students who want to skip decision trees and logistic regression and go straight to neural networks. Deep learning is not always the answer. For most tabular data problems — which is what most Nepali companies work with — a well-tuned random forest or XGBoost model outperforms a neural network and is far easier to explain to stakeholders.
Building models on Kaggle datasets and calling it a portfolio. Running a notebook on the Titanic dataset does not impress hiring managers at Fusemachines or Deerwalk; they have seen it a thousand times. That is why the capstone requires you to define your own problem, source your own data and build end to end. Originality and problem-solving matter more than accuracy scores on well-known datasets.
Ignoring SQL entirely. Students get excited about Python and ML and dismiss SQL as "basic". Then they walk into an interview at a bank or fintech company and the first technical question is a query across three joined tables. In most data roles in Nepal you spend more time writing SQL to extract and prepare data than building models.
What You Will Be Able to Do After This Course
- Write Python programs for data manipulation, analysis and visualization from scratch
- Clean, transform and explore messy real-world datasets using Pandas and NumPy
- Build meaningful data visualizations using Matplotlib, Seaborn and Plotly
- Apply statistical concepts — probability, hypothesis testing, regression — to real data problems
- Build, train and evaluate machine learning models for classification, regression and clustering with Scikit-learn
- Apply feature engineering techniques that measurably improve model performance
- Build and train deep learning models using TensorFlow and Keras
- Apply NLP techniques: text preprocessing, sentiment analysis and text classification
- Understand the fundamentals of generative AI and large language model applications
- Write SQL queries to extract, transform and analyze data from relational databases
- Deploy a machine learning model as a web API using Flask or FastAPI
- Present a complete end-to-end capstone: problem definition, EDA, modeling, evaluation, deployment and documentation
Who Is This Data Science & AI Course For?
Software developers moving into AI and ML roles who can code but have never trained, tuned or deployed a model
Analysts and statisticians who have hit the ceiling of spreadsheets and want to work at scale in Python
Researchers in agriculture, healthcare, environmental science or social research who want to model their own data
Fresh graduates targeting the highest-paying entry-level IT roles in Nepal with a real portfolio behind them
Career switchers from non-technical backgrounds — data science rewards curiosity and analytical thinking as much as raw coding
What You Will Learn
6 modules- Python syntax, data types, control flow, functions and object-oriented basics
- Working with Jupyter Notebooks as your primary data science environment
- NumPy: arrays, vectorized operations, broadcasting and linear algebra
- Pandas: DataFrames, Series, indexing, filtering, grouping, merging and reshaping
- Data cleaning: missing values, duplicates, outliers and inconsistent formats
- Visualization with Matplotlib: line, bar, histogram, scatter and subplots
- Advanced visualization with Seaborn: heatmaps, pair plots and distribution plots
- Interactive visualization with Plotly for dashboards and presentations
- Working with CSV, JSON, Excel and API data sources
Tools You Will Get Hands-On Practice With
Real Projects, Not Kaggle Competitions
Every project uses real-world datasets relevant to Nepal's market and industries. You will not download a perfectly clean Kaggle CSV and follow a step-by-step tutorial. You will face messy data, missing values, feature engineering decisions and model selection tradeoffs — the same challenges you will face in your first week on the job.
During the 4 months you will complete projects including:
- 1An exploratory data analysis and visualization project on a real Nepali dataset (banking transactions, telecom usage or e-commerce behavior)
- 2A classification model predicting customer churn or loan default using Scikit-learn
- 3A regression model forecasting a business metric with feature engineering and model comparison
- 4A deep learning image classification project using TensorFlow and CNNs
- 5An NLP sentiment analysis or text classification project on Nepali or English review data
- 6A generative AI application prototype using LLM APIs
- 7A capstone project: end-to-end, from problem definition to model deployment
These projects form your portfolio. When Fusemachines, Deerwalk or any data team asks "show me what you have built", you will have six or more documented, deployed, presentable projects.
The Tools You Will Actually Use
You will write your own code, clean your own data and debug your own models — these are not demonstrations or pre-configured notebooks.
Data Science and AI Career Scope and Salary in Nepal (2026)
Growing fintech adoption, banking-sector digitalization, e-commerce growth and international outsourcing companies building AI teams in Kathmandu mean qualified professionals are in strong demand.
Here is what the current job market looks like based on publicly available listings on Merojob, LinkedIn Nepal, Kumari Job, Glassdoor and PayScale as of August 2026.
Salary by Experience Level
| Experience level | Typical role | Monthly salary (NPR) |
|---|---|---|
| Fresher (0 – 1 year) | Data Analyst, Junior Data Scientist | 40,000 – 80,000 |
| Mid-level (2 – 4 years) | Data Scientist, ML Engineer, Analytics Lead | 1,00,000 – 2,00,000 |
| Senior (5+ years) | Senior Data Scientist, AI Lead, Data Science Manager | 2,00,000 – 3,50,000+ |
| Remote (international clients) | Data Scientist, ML Engineer | USD 2,000 – 6,000+/month |
Salary by Specialization
| Specialization | Fresher (NPR/month) | Mid-level (NPR/month) |
|---|---|---|
| Data Analyst | 40,000 – 60,000 | 70,000 – 1,20,000 |
| Data Scientist (ML) | 50,000 – 80,000 | 1,00,000 – 2,00,000 |
| Machine Learning Engineer | 55,000 – 85,000 | 1,20,000 – 2,50,000 |
| NLP / AI Engineer | 60,000 – 90,000 | 1,30,000 – 2,50,000 |
| Data Engineer | 50,000 – 80,000 | 90,000 – 2,00,000 |
Salary figures vary by company type, specialization and whether you work locally or remotely. These ranges reflect Kathmandu-based roles as of August 2026.
Where do data science professionals work in Nepal?
At AI-focused companies (Fusemachines, Leapfrog Technology), healthcare analytics firms (Deerwalk Services), fintech companies (F1Soft Group, eSewa, Khalti), banks (NMB, Nabil, Himalayan Bank), telecom operators, e-commerce platforms and a growing number of outsourcing firms building data teams for international clients. Remote work through Upwork, Toptal and direct hiring is a strong and growing path.
Roles You Can Apply For After This Course
Course Fee, Batch Details and Payment Options
- Live instructor-led classes, in person at New Baneshwor, Kathmandu or online
- Access to Jupyter Notebook environments, Google Colab and all course datasets
- 7+ hands-on projects including an end-to-end deployed capstone
- Project reviews and code feedback from the instructor
- Course completion certificate
- Career support: resume review, GitHub portfolio building, interview preparation and placement connections
Available Batches
- Duration
- 4 months
- Weekend Batch
- Saturday and Sunday
- Evening Batch
- Sunday to Friday, 6:00 PM to 8:00 PM
NPR 35,000 is the highest course fee in our catalog. Call +977-9716500918 or book a free counselling session to discuss installment plans.
Common Mistakes We See in Every Cohort
We have trained 165+ students in this course. These are the three patterns we correct most often.
Jumping to deep learning before understanding basic ML
Every cohort has students who want to skip decision trees and logistic regression and go straight to neural networks. Deep learning is not always the answer. For most tabular data problems — which is what most Nepali companies work with — a well-tuned random forest or XGBoost model outperforms a neural network and is far easier to explain to stakeholders.
Building models on Kaggle datasets and calling it a portfolio
Running a notebook on the Titanic dataset does not impress hiring managers at Fusemachines or Deerwalk; they have seen it a thousand times. That is why the capstone requires you to define your own problem, source your own data and build end to end. Originality and problem-solving matter more than accuracy scores on well-known datasets.
Ignoring SQL entirely
Students get excited about Python and ML and dismiss SQL as "basic". Then they walk into an interview at a bank or fintech company and the first technical question is a query across three joined tables. In most data roles in Nepal you spend more time writing SQL to extract and prepare data than building models.
Frequently Asked Questions
Ready to Start?
Talk to a course advisor before you enroll. A free 30-minute counselling session will help you decide whether this course fits your goals, which batch timing works, and what payment option makes sense.
Other Courses at Next Minds
NPR 35,000
Online & on campus