AI INTEGRATED COURSE

Data Science with Python Training

Data Science & ML with Python

Course Overview
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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.

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.

Benefits of Data Science & ML with Python

This course is ideal for IT professionals seeking to advance their careers in data analytics. The Data Science with Python course will help you build a strong understanding of Data Science and also enhance your analytics technique using Python. After completing this course, you'll learn the main concepts of Python programming and gain a better understanding of data analytics, machine learning, data visualization, web scraping, and natural language processing. This course will further provide you with the following benefits:

  • Gain expertise in Machine Learning.
  • In-depth understanding of data science processes.
  • Important concepts of Python programming.
  • Perform data analysis.
  • Extract data from different websites and many more.

Broadway has become the students' choice because of its finest teaching methods and experts who are focused on giving the students what they came for. We ensure that every trainee receives knowledge based on the latest updates, which will help them easily secure a job in the market. We encourage interested students to contact us at the earliest opportunity to enroll in our upcoming Data Science with Python training sessions.

Benefits of Data Science & ML with Python at Broadway Infosys

We are driven towards building skilled individuals in Data Science and further adding credentials to the student's portfolios. After undergoing concentrated training from a team of highly dedicated and committed experts, the students will have the opportunity to apply their acquired knowledge professionally. Learning data science with Python at Broadway gives you the following benefits:

  • Experienced mentors and trainers.
  • Internship and job placement opportunities for capable candidates.
  • Exposure to a real-world scenario.
  • Opportunity to expand network with like-minded professionals.
  • Affordable training costs and quality education.
  • Facilitation of globally recognized international-level certifications.
  • Wider access to training equipment and materials.
  • Scholarship offers for deserving and needy students.

Students who got hired learning this course

Hear from graduates who have completed our courses.

view more success stories
Successful student from Broadway Infosys Mr. Kiran Gautam
Mr. Kiran Gautam

Devops Engineer

CloudPundit Pvt. Ltd.

College/Faculty
Lumbini ICT Campus / Bsc CSIT
Successful student from Broadway Infosys Ms. ⁨Aanuk Mahat
Ms. ⁨Aanuk Mahat

Python and AI Developer

Global Staffing Support

College/Faculty
Trinity International College / BSc. CSIT
Successful student from Broadway Infosys Mr. Sachhyam Lal Shrestha
Mr. Sachhyam Lal Shrestha

Data Analyst

Credit Information Bureau Nepal

College/Faculty
Kathmandu University School of Management / BBA
Successful student from Broadway Infosys Mr. ⁨Avishek Majhi
Mr. ⁨Avishek Majhi

Associate Software Developer

Parsedom

College/Faculty
Orchid International College / BSc. CSIT

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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

  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

  • 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
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Upcoming Classes (11)
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