Data Science with Python Training in Nepal
Become a Data Scientist – Comprehensive Training in Statistics, Analytics, & AI
Duration: 1 month
Fee: Rs.2499 /-
Rs.30000
Data Science with Python Training in Nepal is growing in popularity. Students, professionals, and businesses want to use data effectively. Python is now the top programming language for data science. It is simple and versatile. Plus, it has strong libraries for data analysis, machine learning, and artificial intelligence.
Why Choose Data Science with Python?
Python is popular in data science. It's due to strong libraries like Pandas, NumPy, Matplotlib, Scikit-Learn, and TensorFlow. These tools help professionals gather, clean, analyze, visualize, and model data effectively. Data-driven decision-making is highly sought after in finance, healthcare, marketing, and technology. This makes data science a valuable skill to have.
Benefits of Data Science with Python Training in Nepal
- Career Opportunities: Data science jobs are in high demand in many areas. This includes banking, e-commerce, IT, and government organizations. This training can help you get jobs such as Data Analyst, Machine Learning Engineer, and AI Specialist.
- Skills You Need: Learn to analyze data, build models, and make smart business choices.
- Hands-On Learning: Practical projects and case studies let learners use theory in real life.
- Global Certification: Many training schools in Nepal provide globally recognized certifications. This can enhance your resume and improve your job opportunities.
- Flexible Learning Options: Learners can pick from online or offline training programs. This way, they can choose courses that fit their schedules.
Who Should Enroll?
- Students and fresh graduates looking to build a career in data science.
- IT professionals who want to upskill and transition into data science roles.
- Business professionals and entrepreneurs who want to leverage data for better decision-making.
- Researchers and academicians looking to analyze and interpret complex datasets.
Conclusion
Big data, AI, and automation are growing fast. Data Science with Python Training in Nepal is a smart choice for those wanting to thrive in tech. Mastering Python for data science helps people improve their analytical skills. It also boosts career prospects and supports Nepal’s growing digital economy. Enroll in a training program today and step into the world of data science!
Materials included
Life time video access
Free Certificate
Future Support
Live sessions on Google Meet
Requirements
Laptop
Course Syllabus
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:
Online Class
April 27, 2025
8:00pm-9:30pm
Class Time
8:00pm-9:30pm
