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Data Science & ML with GenAI Training
From Python Programming to Machine Learning, GenAI & Deployment
Thousands of students have started their careers after getting certified by Broadway Infosys
Updated On: 22/07/2026
Created On: 04/04/2018
Course Overview
Broadway Infosys offers comprehensive training in Data Science with Python that is truly hands-on, helping the students master all necessary skills associated with analyzing, visualizing, and interpreting data through Python. The course helps in understanding NumPy, Pandas, Matplotlib, Seaborn, and Plotly. It focuses on data wrangling, visualization, exploratory data analysis, statistical methods, and basic machine learning. The students will necessarily go into a significant amount of practice using real-life datasets alongside a capstone project.
This training is aimed at all students, professionals, and switchers who wish to take a career in data science. Broadway Infosys offers experienced instructors, practical projects, and flexible hours to prepare candidates for data-oriented jobs locally and internationally. Join one of Broadway's most career-focused Data Science with Python training programs in Nepal and learn how to practice skills while gaining real-world experience in Data Science.
Tools Covered
Some of the major industry-relevant tools you'll work with in this course include:
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Python
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Jupyter Notebook
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Google Colab
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NumPy
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Pandas
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Matplotlib
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Seaborn
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Plotly
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Tableau
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Gemini
Our Student’s Voice
Hear from graduates who have completed our courses.
Mr. Amir Dahal
Data Science & ML with GenAI
It's excellent. They always help in different ways. The teachers are good to students and help by giving exercises and suggestions. I have already taken 3 classes.
Mr. Bijay Kharel
Data Science & ML with GenAI
My teacher was knowledgeable, supportive, and explained concepts clearly. The classes were interactive and helped me improve my practical skills. I had a great learning experience at Broadway Infosys Nepal and would recommend it to others.
Mr. Sohan Shrestha
Data Science & ML with GenAI
The Python with Data Science course was highly insightful and practical. I would like to extend my sincere gratitude to both of my instructors for their guidance.Our Python Instructor is an excellent teacher who made complex Python fundamentals like classes, loops,...
College/Faculty: Islington College / BSc (Hons) Computing
Working At: COL Thinkspace
Position: Python Backend Development Intern
College/Faculty: Bangalore Technological Institute / BIT
Working At: Islington College
Position: Python Instructor
College/Faculty: Orchid International College / BSc. CSIT
Working At: Parsedom
Position: Associate Software Developer
College/Faculty: Kathmandu University School of Management / BBA
Working At: Credit Information Bureau Nepal
Position: Data Analyst
Our graduates are hired by 470+ companies in Nepal
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Frequently Asked Questions
No prior professional experience is required. Basic knowledge of Python, mathematics, statistics, and programming fundamentals can help, but beginners interested in data science can also join the training.
Yes. The training suits beginners and learners with basic Python knowledge who want to build their data science skills.
You will learn Python for data science, data analysis, data visualization, statistics, data manipulation, and other essential concepts used in the data science field.
Yes. Practical projects and exercises help you apply Python and data science concepts to real-world datasets and develop problem-solving skills.
After completing the training, you will be able to use Python for data analysis, clean and manipulate datasets, create data visualizations, perform basic statistical analysis, and apply data science techniques to practical problems.
Dedicated labs, certified instructors, and placement support. You will receive guidance from trainers throughout the course, including support with practical exercises, coding tasks, datasets, projects, and technical concepts. You can also ask questions and get clarification during the training.
Yes. Depending on your requirements, you can discuss suitable schedules with the training team. One-to-one training and group-based learning options may also be available.
Our course is available in hybrid training mode. You can attend classes in person or join online, whichever works best for you. If you cannot attend in person on a particular day, you can simply join the class online instead.
The course pricing varies depending on the subject and level. For detailed information on the course price, please contact us directly: +977-9841002000 / +977-1-4111849. Our team will be happy to guide you or send us an email at [email protected].
Our syllabus outlines are only the headlines of the major modules. To ensure a complete understanding of the course, we offer free counseling. Also, if you have specific modules in mind, you can customize the course. Send your inquiry today!
Lesson 1: Course Outline: Python Programming
- What exactly is Python?
- Python's root and its ecosystem
- Python Installation & IDEs setting up (Google Colab, Jupyter Notebook, VSCode, PyCharm)
- Python framework & Python syntax
- Hands-on writing code on Google Colab
- Data Types & Variables (String, Integer, Float, Complex, Boolean, None)
- Input and Output Functions
- Working with the format() method, f-strings, & escape sequences
- Basic Arithmetic & Operators
- Type casting, type checking, & validation
- If Else Conditional Statements (if, else, elif)
- Loops (for, while)
- Looping over tuples, strings, & dictionaries
- Special loops in Python (for/else)
- Using nested loops and flow control through conditions
- Resolving real-world problems to improve skills
- Special Statements: pass, continue, break
AI Tool:
- Google Colab - Gemini
Lists:
- Overview & fundamental operations
- Indexing, slicing, & negative indexing
- Looping through lists & conditions
- List methods like .insert(), .append(), .remove(), .sort(), etc.
- List comprehension with conditions
Tuples:
- Introduction & operations
- Indexing, slicing, & looping
- List versus Tuple
- Switching between lists and tuples
- Tuple unpacking
Sets:
- Introduction & set operations
- Adding, removing, & discarding items
- Set operations: union, intersection, and difference
- Frozenset versus set
Dictionaries:
- Introduction to dictionaries & methods like .get(), .update(), .keys(), .pop(), etc.
- Dictionary comprehension
- Nested dictionaries
AI Tool:
- Gemini or Codeium
- Defining functions through def keyword
- Parameters, Arguments, & Return Statements
- Returning multiple values
- Default & keyword arguments
- Anonymous functions (lambda)
- Nested functions & closures
- Scopes in Python: Local and Global
Text File Operations:
- Reading & writing text files
- Modes of file (r, w, a, rb, wb)
- File path handling with the os module
Working with CSV Files:
- Basics of CSV format and operations
- Reading & writing CSV files with csv.reader & csv.writer
- Using dictionaries in CSV files
Working with JSON:
- Introduction to JSON & its structure
- Reading & writing JSON data with the json module
- Parsing JSON strings
AI Tool:
- Using ChatGPT for prompt engineering
- Classes & Objects
- Class versus Object attributes
- Initializing object attributes with __init__()
- self keyword
- Inheritance: single, multiple, and multi-level
- Polymorphism & operator overloading
- Function overriding & encapsulation
AI Tools:
- Pythontutor.com
- try-except blocks
- Catching specific exceptions
- Using else & finally
- Generating and creating custom exceptions
- Problem-solving strategies
- Lambda Functions
- Generators & Iterators
- List comprehensions
- Working with *args & **kwargs
Standard libraries: os, random, math, functools, etc.
Data manipulation with Pandas
- Working with DataFrames
- Reading & writing CSV files
- Data manipulation techniques
Data Visualization:
- Using Matplotlib, Seaborn, and Plotly
AI Tools:
- Pandas Profiling
- Designing and changing databases and tables
- CRUD operations (CREATE, SELECT, UPDATE, DELETE)
- Filtering data with the WHERE clause
AI Tools:
- DBeaver for SQL queries
- Optimizing & explaining SQL queries with ChatGPT
- Installing & configuring Git
- Setting up local & remote repositories
- Making commits & branching
- Integrating local repositories to GitHub
- Pushing changes & cloning repositories
AI Tools:
- GitHub Copilot for Git commands
- Web Scraping + Database + File Operations: Scrape data, store it in SQL, & export to CSV/JSON
- Desktop Application (Data Entry System): Develop an application to manage data in JSON/CSV format
- CLI Application with CRUD Operations: Design a CLI app with basic CRUD operations & database integration
Lesson 2: Data Science Course
- Prelude
- The problem landscape
- Defining Data Science
- Demystifying Data Science, Decision Science, AI, ML and DL
- Overview of Data Scientist's Toolbox
- Python - Quick recap ? Python 2.7.x or 3.x?
- Installation and setup
- Data types, functions and important packages
- Data manipulation & Data Engineering
- Data Visualization
- Theoretical foundations of statistics, with practical applications
- Describing data, populations, and sampling
- Understanding variables and their measurement scales
- Analyzing data distribution, central tendencies (mean, median, mode), and dispersion (variance, standard deviation)
- Exploring probability distributions like Gaussian, Bernoulli, Binomial, and Poisson
- Conducting statistical tests (z-test, t-test, chi-square test) and understanding errors (Type 1 and Type 2)
- Analyzing correlations, including Pearson and Spearman's rank
- Key rules and concepts in probability, such as addition, multiplication, permutation, and combination
- Introduction to Numpy
- Random Data Generation
- Numpy Array, Indexing & Operations
- Array Data Structures in Numpy
- Array operations and methods
- Course Assignment
- Importing Datasets
- Data Wrangling
- Exploratory Data Analysis and Model Development
- Introduction to SQL
- SQL Queries
- Joins and Subqueries
- Aggregation and Filtering
- Working with SQL Databases
- Introduction to Scipy
- Numerical Computations
- Exploratory Data Analysis
- Model Generation
- Principles of Information Visualization
- Basic Charting
- Charting Fundamentals
- Applied Visualizations
AI Tools AI-powered notebooks
- Introduction to Tableau
- Download and Install Tableau Public
- Load Data from Excel
- Creating Charts and Graphs
- Basic Visual Analysis
- Overview of the Machine Learning methodology
- Exploratory Data Analysis (EDA)
- Introduction to Feature Engineering
- Statistical Inference, Probability Distributions
- Hypothesis Testing
AI Tool: Pandas Profiling
- Machine Learning Introduction
- ML core concepts
- Unsupervised and Supervised Learning
- Clustering, Classification, and Regression
- Supervised Vs Unsupervised
Linear Regression
- Introduction and Concept
- Best Fit Line and its Equation
- Model Training and Evaluation
Logistic Regression
- Introduction to Classification
- Sigmoid Curve and its Application
- Model Evaluation Techniques
Support Vector Machine (SVM)
- Introduction to SVM
- Kernel Trick and Hyperplanes
- Model Training and Evaluation
K-Nearest Neighbors (KNN)
- KNN Algorithm Overview
- Distance Metrics and Classification
- Model Evaluation and Tuning
AI Tool
- AutoML for Model Building
Clustering Overview
- Understanding Unsupervised Learning
- Clustering vs Classification
K-Means Algorithm
- Introduction to K-Means
- K-Means Theory and its Working
- K-Means Algorithm Steps
- Model Training and Evaluation in Python
Principal Component Analysis (PCA)
- Introduction to PCA
- Dimensionality Reduction with PCA
- Understanding Eigenvectors and Eigenvalues
- PCA in Data Preprocessing
- Implementing PCA in Python
- Basic Natural Language Processing
- Working with NLTK
- Text Preprocessing
- Text Cleaning and Regular Expression
- Regex Introduction
- Regex Codes
- Text Extraction with Python Regex
- Stop Word Removal
- Stemming
- Lemmatization
- POS Tagging
- Text Classification
- Introduction to Prompt Engineering
- Techniques for Crafting Effective Prompts
- Applications of LLM in Data Science
- Iterative Improvement
- Integration
AI Tools:ChatGpt, Gemini, DeepSeek
- Introduction to Streamlit
- Setting up Streamlit
- Building an ML Web App
- Interactive Visualizations
- Deploying Streamlit Apps
- Introduction to FastAPI
- Building an API for ML Models
- Handling Requests & Responses
- Asynchronous Processing
- Deployment & Scaling
- Exploratory Data Analysis (EDA) and Hypothesis Testing
- Regression Analysis
- Sentiment Analysis
- Classification-based Projects
- Clustering based project
- Real-time ML Model Deployment
Upcoming Classes (9)
Why Data Science & ML with GenAI?
Zero to Deployed, All in One: Start with Python fundamentals and progress all the way to trained ML models deployed as real web apps with Streamlit and FastAPI, with no gaps between learning and building.
GenAI Woven Into Every Step: From ChatGPT and Gemini for coding help to prompt engineering for data science, AI tools are integrated throughout, not a separate add-on module.
Real Datasets, Real Projects: Practice EDA, regression, classification, clustering, and sentiment analysis on real-world data, capped with a capstone project you can showcase to employers.
Beyond Python Full Toolkit: Gain working knowledge of SQL, Tableau, and Git/GitHub alongside NumPy, Pandas, and Scikit-learn, the complete toolkit data teams actually use.
Strong Statistical Foundation: Master probability, hypothesis testing, and statistical inference, the backbone of every ML model, not just the coding syntax.
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