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Data Science with Python
Data Science with Python Course – Learn Data Analysis, Visualization & Machine Learning
Course Overview
Join Nepal’s Best & Most Affordable Python with Data Science Training
Code IT offers Nepal’s most affordable Python with Data Science training, designed for students, fresh graduates, and IT professionals from Dharan, Itahari, Biratnagar, Koshi, Mechi, Kathmandu, and every region of Nepal. This practical course is perfect for those looking to build a career in data science, analytics, machine learning, or AI-powered technologies. Whether you want to upskill or shift careers, this job-ready Python Data Science course in Nepal delivers the hands-on knowledge and industry tools you need to succeed.
What You’ll Learn in This Data Science Course:
- Python programming for data analysis and machine learning
- Data manipulation with Pandas and NumPy
- Data visualization using Seaborn and Matplotlib
- Real-world data cleaning and preparation techniques
- Foundations of machine learning with Python
- Building and deploying data science projects
- Python Data Science training with guaranteed internship in Nepal
This Python with Data Science training course in Nepal combines theory, coding, and real-world projects to prepare you for a successful tech career. With certification and internship opportunities, you’ll gain the confidence and experience needed in today’s data-driven job market.
Why Choose Code IT?
- Code IT offers Nepal’s best-value Data Science course with certification
- Includes top tools like Pandas, NumPy, Matplotlib, and Scikit-learn
- Guaranteed internship and live project experience
- Online and in-person classes for students across Nepal
- Certification to boost your job prospects and credibility
Join the most trusted and affordable Python with Data Science course in Nepal today — and start your journey toward becoming a certified data science professional with real-world skills and industry-ready training.
What's Included in the Course
Pre-Recorded Video
Perfect if you're outside Nepal or can't attend live sessions.
- Lifetime access to recordings
Course Syllabus
Explore the complete course syllabus to see what you'll learn from start to finish.
Day 1: Introduction to Python Programming
- Overview of Python for Data Science.
- Variables, data types, and basic input/output.
- Lists, tuples, dictionaries, and sets.
Day 2: Python Control Structures and Functions
- Conditional statements and loops.
- Writing and using functions.
- Lambda, map, filter, and list comprehensions.
Day 3: NumPy Essentials
- Creating arrays, reshaping, indexing, and slicing.
- Basic mathematical operations on arrays.
- Broadcasting and aggregation.
Day 4: Pandas Basics for Data Handling
- Series and DataFrames overview.
- Reading/writing data (CSV, Excel).
- Indexing, filtering, and sorting data.
Day 5: Data Cleaning with Pandas
- Handling missing data (fillna, dropna).
- Renaming, adding, and dropping columns.
- Transforming and replacing data.
Day 6: Aggregation and Merging
- GroupBy operations and aggregation (sum, mean, etc.).
- Pivot tables and multi-indexing.
- Joining and merging datasets.
Day 7: Introduction to Data Visualization
- Basics of visualization: when and why to plot.
- Introduction to Matplotlib: line, bar, and scatter plots.
Day 8: Advanced Plotting with Matplotlib & Seaborn
- Customizing plots (titles, legends, grid).
- Introduction to Seaborn for statistical visualizations.
- Histograms, KDE plots, and boxplots.
Day 9: Relationship and Distribution Visuals
- Pair plots and heatmaps with Seaborn.
- Identifying relationships and patterns.
- Scatter matrices and correlation analysis.
Day 10: Time Series Analysis Basics
- Introduction to time series data in Pandas.
- Resampling, shifting, and rolling windows.
- Time series visualization.
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.
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.
Day 13: Understanding Machine Learning Basics
- What is Machine Learning?
- Types: Supervised, Unsupervised, and Reinforcement.
- Workflow: Data preprocessing, training, evaluation.
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.
Day 15: Classification with Logistic Regression
- Introduction to classification problems
- Binary classification using logistic regression.
- Performance metrics: Accuracy, Precision, Recall, F1-Score.
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.
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.
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.
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.
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.
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.
Day 22: Neural Networks (ANN) Basics
- Architecture: Input, hidden, and output layers.
- Activation functions and backpropagation.
- Build a simple ANN using PyTorch.
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.
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.
Day 25: Final Capstone Project & Kaggle Competition
- Capstone Project:
- Final Kaggle Competition:
Need More Information About This Course?
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Frequently Asked Questions
Code IT is a professional IT training institute that offers both online and offline courses in various fields like Web Development, Networking, Graphic Design, and more.
Yes, you will receive a certificate upon successful completion of the course.
Internship opportunities are available for most students; however, some courses do not include internships.
Yes, we offer job placement support. Terms and conditions apply.
Yes, the course fee must be paid during registration to confirm your seat.
Yes, demo classes are available. You can find them at the top of this syllabus — click the "Watch Demo" button.
Yes, you will get access to recorded class videos, which you can watch anytime with lifetime access.
Yes, Code IT provides lifetime support to all students, even after course completion.
No, the fee is non-refundable. However, you can transfer to another class if you inform the administrator within 1 day of the course start date.
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