Data Science with Python Training in Nepal

• Introduction to markdown

• Using Python as calculator

• Number data type
• Taking input from the users
• Type casting and type checking
• Type validation
• Number type with conditionals

• Modulus operator
• Boolean expressions
• Logical operators
• Conditional execution
• Chained conditionals
• Nested conditionals
• Recursion
• Stack diagrams for recursive functions
• Infinite recursion

• Multiple assignments
• Updating variables
• The while statement
• Break
• Debugging
• For loop

• Introduction
• Single line vs multiline string
• Indexing
• slicing
• len()
• Loop: for loop using range()
• Loop with conditionals
• continue vs break
• characters vs substrings
• Immutable data type
• String methods: .replace(),.lower(), .upper(),.title() .lstrip(), .rstrip(), .strip(), .split(), .join(), .isdigit(), isupper(), islower(), .format()

      List

• Introduction to list
• Indexing/Negative indexing
• Slicing
• looping & conditionals, len()
  • Different types of for loop ,while loop, for loop vs while loop
• list of list and nested loop
• Membership operators: in , not in
• Mutable vs Immutable data type with exmaple
• List methods: .insert(), .append(), .remove(), .pop(), .sort(), .extend(), .remove(), .sort()
• List Comprehension
• with if and else

Tuple

• Introduction to tuple
• Indexing , slicing, looping
• list vs tuple
• Typecasting list -> tuple and tuple ->list
• tuple unpacking

Set

• Introduction to set
• .remove() , .add(), .discard() in sets
• Type conversion
• Set operation in Python : union, intersection, difference
• Frozenset vs set

Dictionary

• Introduction
• disctionary methods: .get(), .update(), .keys(), .values(), .pop()
• Loop and dictionary comprehension
• Nested Dictionary

• Identity Operators

• Introduction and syntax: Why function is necessary
• Function definition and function call
• arguments/parameters in function
• return statement in function
• returning multiple value from function
• Handling multiple return values
• Default argument vs non default argument and why it is necessary
• global and local variables
• *args vs **kwargs
• Introduction to Recursion and Recursion tree
• pass keyword

• Class and Objects
• Class attribute and Object attribute
• Initilizing object attribute
• __init__()
• Self keyword and its importance
• Inheritance and its types
• Single Inheritance
• super() method
• Mulitple Inheritance
• Multi-level Inheritance
• Abstraction and Access specifiers
• Polymorphism
• + and len()
• Operator overloading using Dunders/magic method (user defined class)
• function overriding
• Encapsulation

• Understanding exceptions in Python
• Types of exceptions and their meaning
• Importance of exception handling
• Handling Exceptions
  • Using try-except blocks to handle exceptions
  • Catching specific exceptions
  • Handling multiple exceptions
  • Using the finally clauses
• Raising Exceptions

• open()
• modes:
  • read : ‘r’
    • read(), readline(), readlines()
  • write: ‘w’
  • append: ‘a’
  • create: ‘x’
  • Comparison of append and write modes
• File handling on CSV files.
• DictReader and DictWriter
• File handling with exception handling

• Lambda function/Annynomous Function:
  • map() , filter()
• os library
• random library
• math library

• Creating database
• Defining table structure with SQL statements and Specifying column names, data types, and constraints
• Inserting new records into tables using SQL INSERT statements
• Retrieving data from tables using SQL SELECT statements
• Modifying existing data in tables using SQL UPDATE statements
• Removing tables data  using SQL DELETE statements
• Filtering data using the WHERE clause in SQL SELECT statements

• Installing Git Bash
  • Overview of Git Bash
  • Installation
• Creating a GitHub Account
  • Sign up for a GitHub account
  • Set up profile
• Creating an Empty Repository
  • Create a new empty repository on GitHub
• Initializing a Git Repository Locally
  • Initialize a Git repository on your local machine using Git Bash
• Tracking Files
  • Add files to the staging area
  • Commit changes to the repository
• Configuration of Global User Information
  • Configure your global user.name and user.email for Git
• Branching (main)
  • Understand the concept of branches
  • Work with the default main branch
• Adding a Remote
  • Connect your local repository to the remote repository on GitHub
  • Configure the remote repository URL
• Pushing Changes
  • Push your local changes to the remote repository on GitHub
• Cloning
  • Clone a repository from GitHub to your local machine using Git Bash
• Creating a New Branch
  • Create a new branch for making changes
  • Switch between branches
• Pushing Changes to a Branch
  • Push your changes to the remote repository on a specific branch

• Introduction to Pandas
• DataFrame Data Structure
• Reading and writing csv files using DataFrame
• Manipulating DataFrame

• Introduction to Matplotlib and Seaborn and plotly
• Basic plotting using any of these library

• Web Scraping project + Databse
  OR
• Any desktop application: eg. Data Entry application

• 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
   

• Statistics (90% Theoretical Concept + 10% Practical)
• Introduction
• Data Description
• Population and Sample
• Variables and Variable Measurements Scales
• Data Distribution
• Central measure of Tendency (mean, median, mode)
• Measure of dispersion (Variance, standard deviation)
• Gaussian Normal Distribution
• P values
• Type 1 and Type 2 error
• 1-tailed and 2-tailed Test
• Statistical Test (z-test,t-test, chi-square test)
• Pearson Correlation Coefficient
• Spearman’s rank correlation
• Addition Rule and Multiplication rule
• Permutation and Combination
• Function of random variables
• Log-Normal Distribution
• Bernoulli Distribution
• Binomial Distribution
• Pareto Distribution
• Poisson distribution

• 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 Scipy
• Numerical Computaions
• Exploratory Data Analysis
• Model Generation

• Principles of Information Visualization
• Basic Charting
• Charting Fundamentals
• Applied Visualizations

• 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

• 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

Machine Learning Introduction

• ML core concepts
• Unsupervised and Supervised Learning
• Clustering, Classification, and Regression
• Supervised Vs Unsupervised

• Introduction to Linear Regression
• Regression and Best Fit Line
• Modeling and Evaluation in Python
• Introduction to Logistic Regression
• Classification & Sigmoid Curve Modeling and Evaluation
• Introduction to SVM
• Modeling and Evaluation of SVM in Python

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• K Means theory
• Modeling in Python

• Introduction to Flask
• URL and App routing
• Streamlit application – ML Model Deployment

• Exploratory Data Analysis (EDA) and Hypothesis Testing
• Regression : Predict Employee Salary using regression
• Text classification
• Topic Modeling or Customer Segmentation