Data Science & ML with GenAI Training In Nepal
AI INTEGRATED COURSE

Data Science & ML with GenAI Training In Nepal

Data Science

Course Overview

Skills You'll Learn

  • Python Libraries (NumPy, Pandas, Matplotlib): Handle data and perform analyses.
  • Data Cleaning & Manipulation: Prepare datasets for accurate insights.
  • Exploratory Data Analysis: Identify trends and patterns through visualization.
  • Basic Machine Learning Concepts: Apply foundational algorithms to real data.

Skill you'll gain

We provide industry aligned training with practical sessions & mentorship. Grow with our rich portfolio of skill enhancement courses & highly successful placements.
Rich History
Rich History
With over 16 years of excellence in IT training, we’ve transformed a myriad of careers with quality training & innovations.
Qualified Trainers
Qualified Trainers
Our faculty comprises experienced industry experts with a deep understanding of the real world experience & skills.
Support Excellence
Support Excellence
Our dedicated support team guides you all the way to the learning to placement process including growth opportunities.
Successful Alumnis
Successful Alumnis
Our graduates excel at big companies with their presence, itself being the testament to the reputed quality.

Here is what our students say

“
I feel positive about my teacher and Broadway Infosys. The teacher is knowledgeable, supportive, and explains the concepts clearly with practical examples.
Testimonial from Mr. Ananda Pun
Mr. Ananda Pun
Data Science & ML with GenAI Training
“
Overall, my experience with the Data Science course at Broadway Infosys has been good. The course has helped me build a foundation in important topics such as Python, SQL, Pandas, data analysis, and machine learning. I particul...
Successful student from Broadway Infosys Mr. Mahesh Sah Teli
Mr. Mahesh Sah Teli
Data Science & ML with GenAI Training

What we cover

What we cover
CLASS FORMAT
Online Classes (Day and Night)

  • 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

  • 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

  • Introduction to APIs and HTTP methods (GET, POST, PUT, DELETE requests)
  • Sending parameters, headers, and JSON data
  • Handling responses and status codes
  • API authentication (API keys, tokens)
  • Error handling and exceptions
  • Working with sessions and pagination
  • Consuming public APIs (practical examples)

  • Functions as first-class objects
  • Closures and nested functions
  • Creating and using decorators
  • Decorators with arguments
  • Using functools.wraps
  • Built-in decorators (@staticmethod, @classmethod, @property)
  • Practical use cases (logging, timing, authorization)

  • Introduction to type annotations
  • Function and variable type hints
  • Built-in and collection types
  • typing module (Optional, Union, Any)
  • Type hints in classes and methods
  • Static type checking basics (mypy)
  • Best practices for clean and safe code

  • Installing & configuring Git
  • Setting up local & remote repositories
  • Making commits & branching
  • Integrating local repositories to GitHub
  • Cloning repositories and pushing

AI Tools:

  • GitHub Copilot for Git commands

  1. Web Scraping + Database + File Operations: Scrape data, store it in SQL, & export to CSV/JSON
  2. Desktop Application (Data Entry System): Develop an application to manage data in JSON/CSV format
  3. CLI Application with CRUD Operations: Design a CLI app with basic CRUD operations & database integration

Topics

  • What is Data Science?
  • Data Science vs. Data Analytics
  • Data Science vs. Artificial Intelligence
  • Machine Learning and Deep Learning
  • Types of Data Science problems:
    • Descriptive
    • Diagnostic
    • Predictive
    • Prescriptive
  • Real-world applications
  • Data Science career roles
  • Common Data Science tools and languages (Python, R, SQL, BI tools)
  • Data Science team roles and responsibilities
  • Ethical considerations and bias in Data Science

Practical: Analyze several real-world problems and identify:

  • The business problem
  • Required data
  • Possible analytical approach
  • Expected outcome
  • Map real job postings to Data Science role types

Topics

  • Data Science project lifecycle
  • Problem definition
  • Data collection
  • Data understanding
  • Data cleaning
  • Exploratory Data Analysis
  • Feature engineering
  • Model development
  • Model evaluation
  • Deployment
  • Monitoring and improvement
  • Business understanding and communication
  • CRISP-DM framework
  • Agile approaches in Data Science projects
  • Version control basics (Git) for Data Science projects

Practical: Create a workflow for a real-world problem such as:

  • Customer churn
  • House price prediction
  • Sales analysis
  • Student performance
  • Fraud detection
  • Identify the lifecycle stage for a set of real project scenarios

Topics

  • Population and sample
  • Parameter and statistic
  • Types of variables
  • Qualitative and quantitative data
  • Discrete and continuous variables
  • Measurement scales
  • Mean, median, and mode
  • Range
  • Variance and standard deviation
  • Percentiles and quartiles
  • Interquartile range
  • Outliers
  • Skewness and kurtosis
  • Coefficient of variation
  • Common distribution shapes

Practical

  • Generate statistical summaries from a dataset.
  • Compare mean and median.
  • Identify possible outliers.
  • Interpret the spread and distribution of data.
  • Visualize and compare skewed vs. symmetric distributions

Topics

  • Basic probability
  • Events and sample spaces
  • Addition and multiplication rules
  • Conditional probability
  • Independent and dependent events
  • Bayes’ theorem: intuitive understanding
  • Random variables
  • Normal distribution
  • Bernoulli and binomial distributions
  • Poisson distribution
  • z-scores
  • Law of Large Numbers
  • Uniform and exponential distributions
  • Central tendency of probability distributions

Practical

  • Simulate simple probability experiments.
  • Visualize common probability distributions.
  • Calculate and interpret z-scores.
  • Compare simulated results with theoretical probability distributions

Topics

  • Sampling methods
  • Sampling bias
  • Sampling distribution
  • Central Limit Theorem
  • Point estimation
  • Confidence intervals
  • Margin of error
  • Statistical uncertainty
  • Stratified and cluster sampling
  • Bootstrapping: conceptual introduction
  • Standard error

Practical

  • Compare population and sample statistics.
  • Calculate confidence intervals.
  • Demonstrate the effect of sample size.
  • Perform a simple bootstrap resampling exercise

Topics

  • Null and alternative hypotheses
  • Significance level
  • p-value
  • Type I and Type II errors
  • One-sample and two-sample t-tests
  • Chi-square test
  • Pearson correlation
  • Spearman correlation
  • Correlation vs. causation
  • Statistical vs. practical significance
  • ANOVA: conceptual introduction
  • Effect size
  • Multiple comparisons: brief overview

Practical

  • Conduct a t-test.
  • Conduct a chi-square test.
  • Calculate and interpret correlations.
  • Write conclusions in plain language.
  • Run and interpret a one-way ANOVA

Topics

  • What is a database?
  • Relational database concepts
  • Tables, rows, and columns
  • Primary and foreign keys
  • Relationships between tables
  • SQL databases
  • SELECT
  • WHERE
  • ORDER BY
  • LIMIT
  • DISTINCT
  • NULL handling
  • IN, BETWEEN, and LIKE operators
  • SQL data types
  • Query formatting best practices

Practical: Query a sample database containing customers, products, and orders. Additionally, filter results using pattern matching (LIKE) and handle NULL values correctly.

Topics

  • COUNT
  • SUM
  • AVG
  • MIN and MAX
  • GROUP BY
  • HAVING
  • CASE statements
  • Date functions
  • String functions
  • Business questions using SQL
  • COALESCE and ROUND
  • Nested aggregations
  • Indexing: conceptual introduction

Practical: Answer questions such as:

  • Which products generate the most revenue?
  • Which customers purchase most frequently?
  • Which regions have the highest sales?
  • What is the average order value?
  • Identify how indexing could speed up a slow query

Topics

  • INNER JOIN
  • LEFT JOIN
  • Self-join
  • Joining multiple tables
  • Subqueries
  • Common Table Expressions
  • Data relationship analysis
  • RIGHT JOIN and FULL OUTER JOIN
  • UNION and UNION ALL
  • Recursive CTEs: conceptual introduction

Practical: Combine customer, order, and product information to create an analytical dataset. Additionally, use UNION to merge results from multiple queries into one multi-source report.

Topics

  • SQL for data preparation
  • Filtering data before analysis
  • Creating analytical datasets
  • SQL and Data Science workflow
  • SQL with analytical tools
  • Basic window functions:
    • ROW_NUMBER
    • RANK
    • Running totals
  • Exporting query results
  • LAG, LEAD, and NTILE window functions
  • Views
  • Connecting SQL to Python (SQLAlchemy, pandas.read_sql)

Practical: Create a complete SQL analysis of a business dataset. Additionally, load the exported query results into a Python notebook using pandas.read_sql for further analysis.

Topics

  • What is Data Engineering?
  • Data Engineer vs. Data Scientist vs. Data Analyst
  • Data pipeline concept
  • Batch vs. streaming processing: conceptual introduction
  • ETL vs. ELT
  • Data sources and data sinks
  • Structured, semi-structured, and unstructured data
  • Data quality in pipelines

Practical: Design a simple ETL workflow diagram for a real-world business scenario, such as e-commerce orders, hospital records, or banking transactions.

Topics

  • Common file formats: CSV, JSON, Parquet
  • Row-based vs. columnar storage: conceptual introduction
  • Reading and writing files programmatically
  • Working with REST APIs
  • API authentication basics
  • Pagination and rate limits
  • Combining data from multiple sources

Practical: Write a script that ingests data from a public API and merges it with a local file-based dataset.

Topics

  • Databases vs. data warehouses: conceptual introduction
  • OLTP vs. OLAP: conceptual introduction
  • Star schema basics
  • Data orchestration and scheduling: conceptual introduction
  • Data quality monitoring at scale
  • Data lineage: conceptual introduction
  • Cloud data storage: conceptual overview

Practical: Design a simple star schema for a sales or business dataset and outline a scheduled refresh with basic quality checks.

Topics

  • Why data cleaning is important
  • Missing values
  • Duplicate records
  • Incorrect data types
  • Invalid values
  • Inconsistent categories
  • Outliers
  • Data validation
  • Data quality checks
  • Missing-data strategies: deletion vs. imputation
  • Data profiling tools
  • Regular expressions for text cleaning

Practical: Clean a messy real-world dataset. Additionally, use regular expressions to standardize inconsistent text values in the dataset.

Topics

  • Data transformation
  • Encoding categorical variables
  • Scaling numerical variables
  • Date and time features
  • String-based features
  • Binning
  • Log transformation
  • Feature creation
  • Feature selection basics
  • Data leakage
  • One-hot, label, and target encoding
  • Min-max vs. standard scaling
  • Feature interactions

Practical: Prepare a dataset for machine learning. Additionally, compare model-ready features created with different scaling and encoding methods.

Topics

  • Purpose of EDA
  • Univariate analysis
  • Bivariate analysis
  • Multivariate analysis
  • Numerical analysis
  • Categorical analysis
  • Distribution analysis
  • Group-based analysis
  • Identifying trends and patterns
  • Finding anomalies
  • Pairplots and correlation matrices
  • Automated EDA tools (pandas-profiling / ydata-profiling, Sweetviz)

Practical: Perform structured EDA using a real dataset. Additionally, generate an automated EDA report using a tool such as ydata-profiling or Sweetviz and compare it with the manual analysis.

Topics

  • Principles of effective visualization
  • Choosing the right chart
  • Bar charts
  • Line charts
  • Histograms
  • Box plots
  • Scatter plots
  • Count plots
  • Heatmaps
  • Relationship visualization
  • Avoiding misleading charts
  • Interactive visualization with Plotly
  • Color theory for data visualization
  • Small multiples

Practical: Create visualizations for:

  • Comparison
  • Distribution
  • Trend
  • Relationship
  • Category analysis
  • Correlation
  • Build one interactive chart using Plotly

Topics

  • Multivariate analysis
  • Correlation analysis
  • Time-based EDA
  • Segment-based analysis
  • Identifying important findings
  • From chart to insight
  • Data storytelling
  • Communicating results to non-technical audiences
  • Business recommendations
  • Cohort analysis
  • Funnel analysis: conceptual introduction
  • Executive summary writing

Practical: Convert an exploratory analysis into a structured data story:

  • Problem
  • Data overview
  • Important patterns
  • Key findings
  • Recommendations
  • Build a simple cohort analysis for a sales or subscription dataset

Topics

  • End-to-end EDA workflow
  • Data cleaning
  • Descriptive statistics
  • Analytical questions
  • Visualization
  • Statistical interpretation
  • Business recommendations
  • Peer review of EDA projects

Practical: Complete an EDA project using a dataset from:

  • Sales
  • Banking
  • Healthcare
  • Education
  • Housing
  • E-commerce
  • Customer behavior
  • Review and provide feedback on a peer's EDA project

Topics

  • What is Machine Learning?
  • Traditional programming vs. Machine Learning
  • Supervised learning
  • Unsupervised learning
  • Regression
  • Classification
  • Clustering
  • Features and target
  • Training and testing
  • Overfitting and underfitting
  • Basic modeling workflow
  • Bias-variance tradeoff: introductory view
  • Parametric vs. non-parametric models
  • scikit-learn ecosystem overview

Practical: Identify the correct machine learning problem type for different scenarios. Additionally, explore the scikit-learn estimator API (fit, predict, score) using a simple toy dataset.

Topics

  • Regression problems
  • Simple linear regression
  • Multiple linear regression
  • Best-fit line
  • Predictions
  • Residuals
  • Model coefficients
  • Basic assumptions
  • Multicollinearity
  • Feature scaling for regression
  • Interpreting standardized coefficients

Practical: Build a model to predict:

  • House prices
  • Sales
  • Salary
  • Delivery time
  • Check multicollinearity using the Variance Inflation Factor (VIF)

Topics

  • MAE
  • MSE
  • RMSE
  • R²
  • Train-test split
  • Cross-validation introduction
  • Polynomial regression
  • Ridge regression
  • Lasso regression
  • Bias and variance
  • Elastic Net
  • Learning curves
  • Adjusted R²

Practical: Compare multiple regression models and identify overfitting. Additionally, plot learning curves for at least one model to diagnose bias and variance.

Topics

  • Classification problems
  • Binary and multiclass classification
  • Logistic function
  • Probability prediction
  • Decision threshold
  • Confusion matrix
  • Precision and recall
  • Multiclass strategies (One-vs-Rest, Softmax)
  • Class weighting for imbalanced data
  • Odds ratio interpretation

Practical: Build a classification model for:

  • Customer churn
  • Loan approval
  • Student performance
  • Spam detection
  • Apply class weighting to handle an imbalanced classification dataset

Topics

  • Decision tree structure
  • Splitting concept
  • Gini impurity: practical understanding
  • Tree depth
  • Overfitting
  • Random Forest
  • Feature importance
  • Advantages and limitations
  • Bagging vs. boosting: conceptual introduction
  • Gradient Boosting / XGBoost overview
  • Key hyperparameters (n_estimators, max_depth)

Practical:Train and compare Decision Tree and Random Forest models. Additionally, train a Gradient Boosting model and compare its performance with the Random Forest model.

Topics

  • K-Nearest Neighbors
  • Distance-based learning
  • Importance of feature scaling
  • Selecting K
  • Support Vector Machines
  • Hyperplanes and margins
  • Kernel concept
  • Strengths and limitations
  • Curse of dimensionality
  • Soft margin and regularization in SVM

Practical: Train a KNN or SVM classifier and compare its performance with Logistic Regression. Additionally, visualize the decision boundaries produced by the KNN and SVM models.

Topics

  • Accuracy
  • Precision
  • Recall
  • F1-score
  • Specificity
  • ROC-AUC
  • Confusion matrix
  • Class imbalance
  • Stratified splitting
  • Threshold adjustment
  • Precision-Recall curve
  • Macro vs. weighted averaging
  • Cohen's Kappa

Practical: Evaluate a classification model on an imbalanced dataset. Additionally, plot ROC and Precision-Recall curves to compare model performance.

Topics

  • Missing-value imputation
  • Numerical scaling
  • Categorical encoding
  • Feature selection
  • Feature creation
  • Preprocessing pipelines
  • Column transformation
  • Preventing data leakage
  • scikit-learn Pipeline and ColumnTransformer
  • Custom transformers
  • Saving and loading models (joblib / pickle)

Practical: Build a complete preprocessing and modeling pipeline. Additionally, save the trained pipeline to disk using joblib and reload it to confirm it works correctly.

Topics

  • Parameters vs. hyperparameters
  • Cross-validation
  • Grid search
  • Randomized search
  • Model selection
  • Hyperparameter search spaces
  • Error analysis
  • Model improvement
  • Bayesian optimization: conceptual introduction
  • Nested cross-validation: brief overview
  • AutoML: conceptual introduction

Practical: Tune a Random Forest, SVM, or another selected model. Additionally, compare the runtime and results of Grid Search and Randomized Search on the same model.

Topics

  • Unsupervised learning
  • K-Means clustering
  • Centroids
  • Elbow method
  • Silhouette score
  • PCA: conceptual introduction
  • Model interpretation
  • Complete ML workflow
  • Hierarchical clustering: conceptual introduction
  • DBSCAN: conceptual introduction
  • Anomaly detection basics

Practical: Perform customer segmentation or another clustering task. Additionally, compare the K-Means results with a hierarchical clustering solution on the same dataset.

Topics

  • What is Generative AI?
  • What are Large Language Models?
  • Traditional ML vs. LLMs
  • AI-assisted coding
  • AI-assisted SQL
  • AI-assisted EDA
  • AI-assisted documentation
  • Hallucinations
  • Data privacy
  • Responsible AI use
  • Embeddings and vector search: conceptual introduction
  • Retrieval-Augmented Generation (RAG): conceptual overview
  • Common LLM tools and APIs

Practical: Use an LLM to assist with:

  • SQL query generation
  • Data-cleaning suggestions
  • EDA questions
  • Code explanation
  • Report drafting
  • Use an LLM API to draft commentary on summary statistics

Topics

  • Writing clear prompts
  • Providing context
  • Defining constraints
  • Specifying output format
  • Few-shot examples
  • Iterative prompt improvement
  • Asking for assumptions
  • Debugging generated code
  • Verifying AI output
  • Chain-of-thought prompting
  • Role-based prompting
  • Reusable prompt templates

Practical: Create prompts for:

  • Data analysis
  • SQL
  • Visualization
  • Statistical interpretation
  • Machine learning
  • Documentation
  • Build a prompt template for automated report generation

Topics

  • Natural-language data analysis
  • AI-generated analytical questions
  • AI-assisted visualization
  • AI-generated reports
  • Human verification
  • Reproducibility
  • Privacy and confidential data
  • Limitations of AI-generated conclusions
  • Introduction to AI agents in Data Science
  • AI coding assistants in Data Science workflows
  • Evaluating AI-generated code for correctness
  • Bias in AI-generated insights

Practical: Complete an AI-assisted data analysis workflow and verify every important result. Additionally, use an AI coding assistant to speed up one part of the workflow, then review and correct its output.

Topics

  • From notebook to application
  • Interactive data applications
  • Dashboard vs. prediction application
  • Application structure
  • User inputs
  • Displaying data and results
  • Basic Streamlit concepts
  • Wireframing an application
  • Choosing between Streamlit, Gradio, and Dash
  • Deployment options: conceptual overview

Practical: Create a simple interactive dashboard. Additionally, sketch a simple wireframe of the planned capstone application before building it.

Topics

  • Streamlit layout
  • Widgets
  • Filters
  • Tables
  • Charts
  • User input
  • Loading datasets
  • Displaying model predictions
  • Basic error handling
  • Caching with st.cache_data
  • Session state basics
  • Multi-page apps

Practical: Build a dashboard or prediction interface. Additionally, add caching to the application to improve data-loading performance.

CLASS DURATION
3 Months
SEND INQUIRY
Quick Inquiry
All fields are mandatory.