Data Science with Python

Professional, job-ready data science training for NPR 2,499 — live classes online and in-person at our Dharan institute. Master Python, Pandas, Machine Learning, and real-world projects in just 1 month.

Data Science with Python Course in Nepal

Learn data science the way industry actually uses it — not just theory. NPR 2,499 only.

If you've been searching for the best Data Science with Python course in Nepal, you just found it. Code IT's 1-month intensive program teaches you exactly what Nepal's fastest-growing tech, business, and AI sectors are hiring for — Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and real machine learning — all hands-on, all practical, all applied to real-world datasets.

This is not a course where you watch someone write code. You write it yourself. Day by day, dataset by dataset, you'll clean messy data, build stunning visualizations, run statistical analyses, and train your first machine learning models — the same tools used by data analysts and data scientists at companies worldwide.

By the time you finish, you won't just have a certificate. You'll have a portfolio of real data science projects that proves to any employer — in Nepal or abroad — that you can work with data professionally.

Our live online Data Science classes in Nepal run every evening via Google Meet from 8:00 PM to 9:30 PM, making it easy to join from Kathmandu, Pokhara, Biratnagar, Butwal, or anywhere with an internet connection. Prefer face-to-face learning? In-person classroom sessions are available at our institute in Dharan, Koshi Province.

Whether you're a fresh graduate, a student in your final year, a working professional looking to pivot into data, or simply someone who wants to understand how AI and machine learning work — this course was built for you. No prior data science experience is needed. Basic programming knowledge in any language is enough to get started.

Prerequisites

Basic programming knowledge in any language is enough to get started.
Data Science with Python

Data Science with Python

Next batch starting soon

Mode: Online (Google Meet) Google Meet
Duration: 1 month
Rs.2,499/-
Rs.24,500 Save 89%
Enquiry

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WhatsApp: 9862130505
Telephone: 025-575163

Everything You Receive

All-inclusive support — from training to real-world experience

Live Classes

Google Meet
8:00 PM - 9:30 PM

Lifetime Videos

Re-watch anytime

Certification

Industry recognized

Internship

No internships are available right now.

Course Curriculum

Everything you'll learn — from fundamentals to advanced concepts

Course Outlines

  • Python programming

  • Data cleaning

  • Data visualization

  • Business analytics

  • Basic Machine Learning

  • Project Work

Full Curriculum

01 Day 1: Introduction to Python Programming
Overview of Python for Data Science.
Installing Python, Jupyter Notebook / Anaconda
Variables and Data Types
Basic Input / Output
Lists, Tuples, Dictionaries, Sets
02 Day 2: Python Control Structures and Functions
Conditional statements (if, elif, else)
Loops (for, while)
Writing Functions
Lambda Functions
map(), filter()
List Comprehensions
03 Day 3: NumPy Essentials
Why NumPy for Data Science
Creating NumPy Arrays
Reshaping Arrays
Indexing & Slicing
Mathematical Operations
Broadcasting & Aggregation
04 Day 4: Pandas Basics
Series and DataFrames
Loading datasets (CSV, Excel)
Exploring data (head, info, describe)
Indexing & Filtering
Sorting Data
05 Day 5: Data Cleaning with Pandas
Handling missing data (fillna, dropna).
Renaming columns.
Adding and Dropping Columns
Data Type Conversion
Replacing Values
06 Day 6: Aggregation and Data Merging
GroupBy operations
Aggregation (sum, mean, count)
Pivot Tables
Multi-Indexing
Merge, Join, and Concatenate
07 Day 7: Introduction to Data Visualization
Why visualization matters
Choosing the right chart
Matplotlib basics
Line Chart
Bar Chart
Scatter Plot
08 Day 8: Advanced Visualization
Customizing plots (titles, legends, grid).
Introduction to Seaborn
Histograms, KDE plots, and boxplots.
09 Day 9: Relationship & Correlation Visualization
Pair plots and heatmaps
Correlation Matrix
Scatter Matrix
Finding patterns in data
10 Day 10: Time Series Analysis Basics
Time series datasets
DateTime in Pandas
Resampling
Rolling Window Analysis
Time Series Visualization
11 Day 11: Introduction to Basic Statistics for Data Science
Measures of central tendency (mean, median, mode).
Measures of dispersion (variance, standard deviation).
Probability basics for data science.
Distribution concepts
12 Day 12: SQL Fundamentals
Databases and Tables
SELECT statements
WHERE filters
ORDER BY
GROUP BY
Aggregate Functions
13 Day 13: Advanced SQL
Joins (INNER, LEFT, RIGHT)
Subqueries
Window Functions
SQL for Data Analysis
14 Day 14: Exploratory Data Analysis Project
Project using Titanic / Sales Dataset
Task: Load dataset
Clean data
Perform EDA
Create visualizations
Extract insights
15 Day 15: Introduction to Machine Learning
What is Machine Learning
Types: Supervised vs Unsupervised
ML Workflow
Train/Test Split
16 Day 16: Linear Regression
Linear Regression Theory
Multiple Linear Regression
Implement using Scikit-Learn
Evaluation Metrics (RMSE, R²)
17 Day 17: Logistic Regression
Classification Problems
Binary Classification
Logistic Regression Implementation
Metrics: Accuracy, Precision, Recall, F1
18 Day 18: Naive Bayes & Support Vector Machine
Naive Bayes (Gaussian, Multinomial)
Implement Naive Bayes
SVM Concepts (margin, kernel)
Implement SVM
19 Day 19: Decision Trees
Decision Tree Concept
Overfitting Problem
Pruning Techniques
Visualizing Trees
20 Day 20: k-Nearest Neighbors
Distance Metrics
Implement kNN
Choosing Optimal k
Model Evaluation
21 Day 21: Clustering & Dimensionality Reduction
K-Means Clustering
Cluster Evaluation
PCA Concept
Data Visualization using PCA
22 Day 22: Hyperparameter Tuning
Model Parameters vs Hyperparameters
Grid Search
Random Search
Cross Validation
23 Day 23: Ensemble Learning
Bagging vs Boosting
Random Forest
AdaBoost
Gradient Boosting
24 Day 24: Recap & Interview Preparation
Review all algorithms
Compare models
Common ML interview questions
Kaggle introduction
25 Day 25: Final Capstone Project
Capstone Project:
Customer Churn Prediction
House Price Prediction
Sales Forecasting
Credit Risk Model
26 Final Kaggle Competition:
Build ML Model
Submit predictions
Compare leaderboard scores

Earn Your Certification

After completing the course, you will receive a professional certificate from Code IT, verified by industry leaders in Nepal.

Share your achievement with pride on LinkedIn.
Certificate

Course Mentors

Learn directly from industry experts with years of hands‑on experience

Er.Manoj Chhetri

Er.Manoj Chhetri

Sr.Data analyst

Code IT, Nepal 10+ Years Experience
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Data Science with Python

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