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Master Data Science with Python For Beginners and Intermediate (with Projects & Certification)

Description

Data Science with Python – Complete Online Course for Beginners (with Projects & Certification)

Unlock the power of data and launch your tech career with our comprehensive Data Science with Python course — designed for absolute beginners and career switchers. Over the span of 3 months (36 live online sessions), you’ll learn everything from Python programming fundamentals to advanced data science, machine learning, and even deploying your own web applications.

Guided by industry professionals, this hands-on course takes a project-based learning approach to help you build real-world experience while mastering tools used by data scientists and analysts worldwide — including NumPy, Pandas, Matplotlib, scikit-learn, Streamlit, and FastAPI.

Whether you’re a student, job seeker, or professional looking to upskill, this course equips you with the skills, confidence, and portfolio to kickstart your journey in the world of Data Science & Artificial Intelligence.

Anish Thapaliya

A highly experienced Machine Learning Engineer with over 5 years of expertise in data science, deep learning, and AI. The instructor has led multiple real-world projects including AI agents, resume parsers, chatbots, and time-series forecasting systems, and has also mentored 100+ learners in advanced AI topics like Transformers, BERT, and GPT models. Skilled in delivering practical, hands-on learning with a strong focus on problem-solving, MLOps, and model deployment.

Benefits

Course Module

Lesson 1: Introduction to Python Programming

  • Welcome and build rapport with students
  • Introduction to the course (syllabus)
  • Future scope of Data science with Python
  • Onboarding to Learning platform (Google classroom platform)
  • What is python? What are the features of Python?
  • Obtaining and Installing Python3
  • Obtaining and Installing Integrated Development Environment (IDE) like VSCode
  • Virtual Environments and Package Management with pip/uv
  • Introduction to Google Colaboratory
  • Setup Jupyter notebook locally
  • “First Python program: Hello World”
  • Interactive Mode vs Script Mode

Class work: Write a python program to draw a triangle shape using print statements only.

  • Comments and Indentation
  • Operators vs Operands vs Expressions
  • Types of Operators and Operators Precedence

Classwork: Experiment python expressions in interactive mode

  • Variables 
  • Rules for Creating Variables
  • Naming Conventions: Camel Case, Pascal Case, Snake case
  • Python Keywords and Identifiers

Classwork: Write a program to swap 2 variables using temporary variable

  • Live Quiz
  • Assignments: Related to variables
  • Data Type vs Data Structure
  • Python Data Types Taxonomy
  • Example of Basic Data type 
  • Lab: Setting and Getting the Basic Data Type
  • Type casting / Type Conversion

Classwork: Develop basic calculator app for the user

  • String
  • List 
  • Tuple
  • Set 
  • Dictionary
  • If statement: How python program enters if blocks?
  • Simple if else statements
  • Lab: Simple if else statements
  • Nested if else statements
  • Lab: Nested if else statements
  • Shorthand if…..else statements/Ternary Operators
  • Lab: Shorthand if…..else statements/Ternary Operators
  • pass keyword
  • While Loop
  • Break and continue statement
  • Infinite Loop
  • For Loop
  • Loop over a sequence (string, list, tuple, set, dictionary)
  • Loop using range() function
  • Simple vs nested for loops

 

  • Anatomy of function
  • Creating function through def keyword
  • Arguments, Parameters, and Return keywords
  • Local variable vs global variable
  • Arbitrary positional arguments, *args
  • Keyword & Arbitrary keyword arguments, **kwargs
  • Default parameter value
  • pass keyword
  • Recall predefined functions
  • Recursion vs Tail Recursion
  • Introduction
  • Limitation of procedural programming
  • Key principles of object-oriented programming
  • Create a custom class and a object
  • Creating a class attributes
  • Creating a instance attributes
  • __new__ () vs __init__()
  • Creating methods in a class
    • Class methods
    • Instance methods
  • Encapsulation
  • Inheritance and Implementation Sharing
    • Single Inheritance
    • Multiple Inheritance
    • Multilevel Inheritance
    • Hierarchical Inheritance
  • Polymorphism and duck-typing
    • Function polymorphism
    • Class polymorphism
    • Inheritance class polymorphism
  • Abstract classes
  • Dunder or magic methods in python
  • Different modes of opening files 
  • Context managers
  • with keyword vs built-in open() function
  • Reading files: read() readline() readlines()
  • Closing files: with keyword vs built-in close() function
  • Reset file pointer: seek() method
  • Different types of file writing modes
  • os module for file operations
  • Text file vs binary file vs CSV file vs JSON fill
  • Introduction to Exception and Different types of common Exceptions
  • try except block
  • try except else block
  • try except finally block
  • raise an exception
  • Importance of Exception Handling in production system
  • Logging Practical Implementation In Python
  • Logging With A Real World Example
  • Introduction to Database
  •  File database vs server oriented database
  • What is SQL? SQL Data Types
  • CRUD operations (CREATE, SELECT, UPDATE, DELETE)

Tools: DBeaver

  • How Git Works
  • Installing Git on your machine
  • Git Setup (Login)
  • Creating and Cloning Repository
  • Useful Git Commands: 
    • git status, git branch, git fetch, git pull, git checkout, git add, git commit, git push, git stash, git rebase, git reset.
  • GitHub Actions for CI/CD
  • Getting Started: Printing a Variable’s Value
  • Using Breakpoints
  • Essential pdb Commands
  • Project: Scrape data, store in SQL, Analyze the data & plot the visualization, Push to GitHub
  • File Organization project

Lesson 2: Data Science With Python

  • What is Data Science?
  • Prerequisites for Successful Data Science Careers
  • Data Science Methodology
  • Types of Data Professionals (Data Scientist, Data Engineer,  Data Analyst, ML Engineer)
  • Python Frameworks for Data Science
  • Data Sources: Kaggle, UCI, AWS Registry, EARTH DATA, NOAA, DATA.GOV
  • Descriptive & Inferential Statistics
  • Bayesian Statistics
  • Probability Theory
  • Linear Algebra
  • Calculus and Optimization
  • NumPy and its importance
  • NumPy Array creation (1D, 2D, built-in array creation functions)
  • Random Numbers using NumPy
  • Attributes of NumPy Array
  • Array Indexing and Slicing
  • Subarrays as no-copy view
  • Reshaping of Arrays
  • Arrays Joining and concatenation
  • NumPy Universal Functions (Ufuncs)
  • Computation on NumPy Arrays: Broadcasting
  • Aggregations: Min, Max, and Everything in Between
  • Introduction to Pandas
  • Pandas installation
  • Pandas Series vs DataFrame
  • Different strategies for creating Pandas Series
  • Different strategies for creating Pandas DataFrame
  • DataFrame Operations: Viewing Data
  • DataFrame Operations: Data Selection
  • DataFrame Operations: Aggregations
  • DataFrame Operations: Handling Missing Data
  • Data Manipulation: replace(), astype(), drop(), sort_values(), groupby(), apply()
  • Data Join: Inner Join vs Left Outer Join, vs Right Outer Join vs Full Outer Join
  • Save Data: CSV/Excel format
  • What is SciPy?
  • Installation of SciPy
  • Statistical test using SciPy
  • Regression Analysis using SciPy
  • Importance of Data Visualization
  • Data Ingestion: Import any real world data for analysis and visualization
  • Prepare real world questions that needs to answered via Data
  • Scatter Plot
  • Line Plot
  • Bar Plot
  • Pie Chart
  • Area Chart
  • Box plot
  • Violin plot
  • Correlation Heatmap
  • Bubble plot and beyond
  • Artificial Intelligence vs Machine Learning vs Deep Learning
  • Traditional Programming vs Machine Learning
  • Supervised Learning vs Unsupervised Learning
  • Labeled Data vs Unlabeled Data
  • Regression vs Classification vs Clustering
  • Introducing scikit-learn
  • Linear Regression
  • Regression Evaluation Metrics
  • K-Nearest Neighbors (KNN)
  • Logistic Regression
  • Decision Tree 
  • Random Forest
  • Support Vector Machine (SVM)
  • Classification Evaluation Metrics
  • Clustering and Types of Clustering
  • K-Means Algorithm
  • Clustering Evaluation Techniques
  • Principal Component Analysis (PCA)
  • Parameter Estimation: Elbow method, Silhouette Index
  • Introduction to Market Basket Analysis
  • Association Rule Mining Algorithms: Apriori Algorithm
  • Implementing Association Rules in Python
  • Installing FastAPI
  • Create a simple API
  • Interactive API Docs
  • Different HTTP methods: GET, POST, PUT, DELETE
  • Comparison of FastAPI with Flask
  • Building Web App Using Streamlit
  • Example of ML App With Streamlit Web App
  • Data Science Agent, Natural Language to SQL (Text2SQL)
  • Different AI Tools: Gemini, DeepSeek, ChatGPT, Perplexity

AI Coding Assistants: Cursor, GitHub Copilot

    1. E2E Project: Steps By Step Project Implementation with Lifecycle of ML project followed by Deployment with Streamlit and FastAPI
    2. GitHub & LinkedIn: Push final project to GitHub and share it in linkedin
    3. Blog Post: Write at least one blog post with any of the learned concepts
  • Resume:
  • MLOps (Skills)
  • Deep Learning
  • Computer Vision
  • Time Series Forecasting
  • Speech Recognition (Text to Speech & Speech to Text)
  • Natural Language Processing
  • Generative AI and Large Language Models (LLMs)
  • Retrieval Augmented Generation (RAG) 
  • AI Agents and Agentic AI

Wrap up your learning journey with a course summary, certification guide, and tips to apply your skills in real-world projects and future career paths.

Course Details:

Course Price:

NRP 25000

Instructor

Anish Thapaliya

Number of Sessions

36 classes, 2 hours each, | Every Friday, Saturday, Sunday

Lessons

72 hours (3 months)

Mode of Delivery

Online

Language:

Nepali

Certifications

Digital, Physical

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