Data Science with Python Training in Nepal
AI INTEGRATED COURSE

Data Science with Python Training in Nepal

Data Science

Course Overview

Broadway provides training in Python for data science and is a frontrunner in IT. The goal is to make students aware of simple spreadsheet concepts while improving their data communication skills. Data science is progressively becoming an aspiring professional course with high career value, and the skill set to work in data science is necessary. Broadway Infosys focuses on data acquisition and collection, understanding relevance, processing, extracting insights, displaying results, and visualizing findings through guidance and support by our senior professionals.

Why does learning Python matter? Through the rapid development of programs and prototypes, Python reduces the turnaround time of the whole development process. If you want to explore further, advanced programming languages like Java and C are excellent alternatives. Suppose you ever need assistance in practicing and getting better at data science. In that case, You can check out Broadway's Data Science with Python training that focuses on preparing you to start working as soon as possible.
 

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

“
Course so goated, thinking of taking a new one.
Testimonial from Mr. Anup Wasti
Mr. Anup Wasti
Data Science with Python Training
“
Broadway Infosys is very cooperative and helpful. I learned many good things from them.
Testimonial from Ms. Shikshya Poudel
Ms. Shikshya Poudel
Data Science with Python 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

  • 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
CLASS DURATION
3 Months
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