Data Science Course in Nepal

Data Science with Python Course – Learn Data Analysis, Visualization & Machine Learning

Data Science with Python

Looking for the best Data Science with Python course in Nepal?
Join the professional Data Science Training at Code IT and learn how to analyze data, build predictive models, and make data-driven decisions using Python — the world’s most popular programming language for data science.

This hands-on course is perfect for students across Nepal who want to master data science, gain practical experience, and develop job-ready analytics skills. You’ll work with data analysis, visualization, machine learning, and real-world projects applied in business, AI, and analytics domains.

Our training program offers live online classes accessible from anywhere in Nepal, along with hands-on classroom sessions in Dharan. Develop practical data science skills, work on real datasets, and prepare for professional opportunities in analytics, AI, and business intelligence.

Learn Python for data science, build real-world predictive models, and gain the confidence to work as a professional data scientist in Nepal’s growing data-driven industry.

Prerequisites

Basic maths/statistics knowledge is helpful but not mandatory
Basic understanding of Python (variables loops functions lists etc.)
No prior data science experience required
Data Science with Python
🎯 Starts in 22 Days

Data Science with Python

Starts: March 29, 2026
Mode: Online (Google Meet) Google Meet
Duration: 1 month
Class Time: 8:00pm-9:30pm
Seats available - Register now
Rs.2,499/-
Rs.30,000 Save 91%
Enroll Now
Watch Demo Video

Have any Question?

WhatsApp: 9862130505
Telephone: 025-575163

Everything You Receive

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

Live Classes

Google Meet
8:00pm-9:30pm

Lifetime Videos

Re-watch anytime

Certification

Industry recognized

Internship

Internship is not currently available.

Course Curriculum

Everything you'll learn — from fundamentals to advanced concepts

What you will learn

  • Python & Data Handling

  • Data Visualization

  • Statistics & Exploratory Data Analysis (EDA)

  • Machine Learning Basics

  • Advanced Data Science & Projects

Enroll Now
01 Day 1: Introduction to Python Programming
Overview of Python for Data Science.
Variables, data types, and basic input/output.
Lists, tuples, dictionaries, and sets.
02 Day 2: Python Control Structures and Functions
Conditional statements and loops.
Writing and using functions.
Lambda, map, filter, and list comprehensions.
03 Day 3: NumPy Essentials
Creating arrays, reshaping, indexing, and slicing.
Basic mathematical operations on arrays.
Broadcasting and aggregation.
04 Day 4: Pandas Basics for Data Handling
Series and DataFrames overview.
Reading/writing data (CSV, Excel).
Indexing, filtering, and sorting data.
05 Day 5: Data Cleaning with Pandas
Handling missing data (fillna, dropna).
Renaming, adding, and dropping columns.
Transforming and replacing data.
06 Day 6: Aggregation and Merging
GroupBy operations and aggregation (sum, mean, etc.).
Pivot tables and multi-indexing.
Joining and merging datasets.
07 Day 7: Introduction to Data Visualization
Basics of visualization: when and why to plot.
Introduction to Matplotlib: line, bar, and scatter plots.
08 Day 8: Advanced Plotting with Matplotlib & Seaborn
Customizing plots (titles, legends, grid).
Introduction to Seaborn for statistical visualizations.
Histograms, KDE plots, and boxplots.
09 Day 9: Relationship and Distribution Visuals
Pair plots and heatmaps with Seaborn.
Identifying relationships and patterns.
Scatter matrices and correlation analysis.
10 Day 10: Time Series Analysis Basics
Introduction to time series data in Pandas.
Resampling, shifting, and rolling windows.
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.
12 Day 12: Mini Project — Exploratory Data Analysis
Load, clean, and visualize a real-world dataset (e.g., Titanic or Sales data).
Perform summary statistics and trend identification.
Present visual insights using Matplotlib/Seaborn.
13 Day 13: Understanding Machine Learning Basics
What is Machine Learning?
Types: Supervised, Unsupervised, and Reinforcement.
Workflow: Data preprocessing, training, evaluation.
14 Day 14: Linear and Multiple Linear Regression
Understanding linear regression concepts.
Hands-on: Linear and Multiple Linear Regression using Scikit-learn.
Evaluation metrics: RMSE, R².
15 Day 15: Classification with Logistic Regression
Introduction to classification problems
Binary classification using logistic regression.
Performance metrics: Accuracy, Precision, Recall, F1-Score.
16 Day 16: Naive Bayes & Support Vector Machines (SVM)
Understanding Naive Bayes: Gaussian, Multinomial, and Bernoulli models.
Implementing Naive Bayes using Scikit-learn.
Introduction to SVM: margin, kernels, and hyperplanes.
Implementing SVM with Scikit-learn.
17 Day 17: Decision Trees and Overfitting
Decision tree fundamentals for classification and regression.
Tree pruning techniques to avoid overfitting.
Visualizing decision trees using Scikit-learn.
18 Day 18: Introduction to Hyperparameter Tuning Day 6: Introduction to Hyperparameter Tuning
Concept of hyperparameters vs model parameters.
Grid Search and Randomized Search using Scikit-learn
Cross-validation and model selection.
19 Day 19: k-Nearest Neighbors (kNN)
Concept of distance metrics (Euclidean, Manhattan).
Implementing kNN for classification using Scikit-learn.
Choosing the right k-value and evaluating performance.
20 Day 20: Clustering with K-Means and PCA Introduction
K-Means clustering algorithm explained.
Introduction to Principal Component Analysis (PCA).
Dimensionality reduction with PCA + visualization.
21 Day 21: Ensemble Learning & Boosting
Overview of ensemble learning (Bagging vs Boosting).
Introduction to AdaBoost and Gradient Boosting.
Hands-on: Implementing boosting algorithms using Scikit-learn.
22 Day 22: Neural Networks (ANN) Basics
Architecture: Input, hidden, and output layers.
Activation functions and backpropagation.
Build a simple ANN using PyTorch.
23 Day 23: Introduction to Convolutional Neural Networks (CNN)
Basic concepts of CNNs: filters, pooling, and feature extraction.
Use-case of CNNs in image classification.
Simple CNN implementation using PyTorch.
24 Day 24: Recap & Open Discussion
Review of all covered algorithms and concepts.
Clarify doubts on ANN, CNN, SVM, Naive Bayes, Decision Trees, PCA, etc.
Preparing for final project and Kaggle competition.
25 Day 25: Final Capstone Project & Kaggle Competition
Capstone Project:
Final Kaggle Competition:

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

Manoj Chhetri

Manoj Chhetri

Sr.Data analyst

Code IT, Nepal 10+ Years Experience
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