Python AWS Training and Certification
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π Week Ends Assessment Test
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π Course + Internship Certification
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Python
Python Full Stack Training
- Introduction to Python
- Python Features & History
- Data Types & Variables
- Control Flow & Loops
- Functions & Modules
- Object-Oriented Programming (OOPs)
- Exception Handling
- File Handling
- Database Connectivity (SQLite, MySQL)
- Introduction to Django
- Project Setup & Virtual Environments
- MTV Architecture in Django
- Models & Database ORM
- Forms & Validation
- Templates & Static Files
- Authentication & Authorization
- REST API Development with Django REST Framework
- Introduction to Flask
- Routing & Views
- Jinja Templating
- Flask with SQLAlchemy
- Building REST APIs in Flask
- Flask Authentication
- HTML, CSS, and JavaScript Basics
- React/Angular Introduction
- State Management
- API Integration with Frontend
- Component Lifecycle & Hooks
- Routing & Navigation
- Integrating Backend with Frontend
- API Development & Documentation
- Authentication & Authorization
- Deployment with Docker & Kubernetes
- CI/CD Pipelines
- Real-time Project Development
- Python for Data Science
- Numpy, Pandas for Data Manipulation
- Data Cleaning & Preprocessing
- Data Visualization (Matplotlib, Seaborn)
- Statistics and Probability for Data Analysis
- Exploratory Data Analysis (EDA)
- Machine Learning Algorithms with Scikit-Learn
- Model Evaluation & Tuning
- Real-world ML Projects (House Price, Churn, Fraud Detection)
- SQL for Data Analytics
- Natural Language Processing (NLP)
- Time Series Forecasting
- Introduction to Deep Learning (Keras/TensorFlow)
- Portfolio Projects, Resume Building, GitHub Setup
- Deployment using Streamlit/Flask + Cloud
Data Analytics
Data Analytics Training
- Day 1: Course Introduction, What is Data Analytics? Types of Analytics
- Day 2: Excel Basics: Functions, Cell Formatting, Sorting & Filtering
- Day 3: Excel Charts: Bar, Line, Pie, Scatter
- Day 4: Excel: VLOOKUP, HLOOKUP, INDEX & MATCH
- Day 5: Pivot Tables & Pivot Charts
- Day 6: Excel Dashboard Project β Sales or HR Data (Hands-on)
- Day 7: Databases Overview, Introduction to SQL, SELECT, WHERE
- Day 8: GROUP BY, HAVING, ORDER BY, LIMIT, Aggregate Functions
- Day 9: JOINS: INNER, LEFT, RIGHT, FULL OUTER
- Day 10: Subqueries, Aliases, CASE WHEN
- Day 11: Window Functions: RANK, ROW_NUMBER, LEAD, LAG
- Day 12: Hands-on SQL Project: Employee/Student Database
- Day 13: Python Basics: Variables, Loops, Lists, Dictionaries
- Day 14: NumPy for Numerical Data (Arrays, Functions)
- Day 15: Pandas: Series, DataFrames, Import/Export (CSV, Excel)
- Day 16: Data Cleaning: Handling Nulls, Duplicates
- Day 17: Filtering, Sorting, GroupBy in Pandas
- Day 18: Project: Analyze a Sales or Weather Dataset using Pandas
- Day 19: Matplotlib: Line, Bar, Scatter, Pie
- Day 20: Seaborn: Countplot, Heatmap, Pairplot
- Day 21: Mini Project: EDA on E-commerce or COVID Dataset
- Day 22: Descriptive Statistics: Mean, Median, Mode, SD
- Day 23: Probability Basics, Distributions (Normal, Binomial)
- Day 24: Hypothesis Testing (t-test, chi-square), p-value
- Day 25: Correlation, Simple Linear Regression
- Day 26: Multiple Regression, Assumptions, Interpretation
- Day 27: Stats Project: Predict Sales or Housing Prices (EDA + Regression)
- Day 28: Intro to Power BI, Importing Data, Power Query Editor
- Day 29: Visualizations: Bar, Line, Map, Pie, KPI
- Day 30: DAX Functions, Calculated Columns & Measures
- Day 31: Power BI Filters, Slicers, Relationships
- Day 32: Power BI Project: HR/Marketing Dashboard
- Day 33: Tableau Basics: Connecting Data, Visuals, Calculations
- Day 34: Capstone Project (Team or Individual) β Real Dataset
- Day 35: Presenting the Project, Dashboard, Code, Documentation
- Day 36: Resume, LinkedIn, GitHub Portfolio, Interview Q&A
- Introduction to Machine Learning (Scikit-learn)
- Time Series Forecasting (Pandas + Power BI)
- Big Data Basics (Hadoop, Spark overview)
- Python Web Scraping with BeautifulSoup
- Connecting Python to SQL
Data Science
Data Science Training
- Day 1: Introduction to Data Science, Tools, and Career Paths
- Day 2: Python Setup, Jupyter Notebooks, IDEs
- Day 3: Variables, Data Types, Input/Output
- Day 4: Conditional Statements (if-else)
- Day 5: Loops (for, while)
- Day 6: Functions and Lambda Expressions
- Day 7: Practice Exercises + Mini Project
- Day 8: Lists, Tuples, Sets
- Day 9: Dictionaries and Nested Structures
- Day 10: String Operations
- Day 11: File Handling (Read/Write CSV, TXT, JSON)
- Day 12: Exception Handling
- Day 13: Python Modules and Packages
- Day 14: Practice Assignment + Quiz
- Day 15: Introduction to Numpy
- Day 16: Numpy Arrays, Indexing, Operations
- Day 17: Numpy Broadcasting and Functions
- Day 18: Pandas Series and DataFrames
- Day 19: Reading and Writing Data with Pandas
- Day 20: Data Selection and Filtering
- Day 21: Missing Values and Duplicates
- Day 22: Data Aggregation and GroupBy
- Day 23: Merging, Joining, and Concatenation
- Day 24: Handling Dates and Times
- Day 25: Sorting, Renaming, and Reset Index
- Day 26: Data Cleaning Project
- Day 27: Exploratory Data Analysis (EDA) Basics
- Day 28: Pandas Quiz + Practice Set
- Day 29: Introduction to Data Visualization
- Day 30: Matplotlib Basics (Line, Bar, Scatter)
- Day 31: Advanced Matplotlib Customization
- Day 32: Seaborn for Statistical Plots
- Day 33: Pairplots, Heatmaps, Boxplots
- Day 34: Plotly Introduction (Optional Advanced)
- Day 35: Visualization Project
- Day 36: Descriptive Statistics: Mean, Median, Mode
- Day 37: Variance, Standard Deviation, Range
- Day 38: Probability Theory: Basics & Rules
- Day 39: Conditional Probability, Bayes Theorem
- Day 40: Probability Distributions (Normal, Binomial)
- Day 41: Sampling Techniques
- Day 42: Central Limit Theorem
- Day 43: Hypothesis Testing (Z-test, t-test)
- Day 44: ANOVA and Chi-Square Tests
- Day 45: Statistical Analysis Project
- Day 46: Intro to ML, Types of ML, ML Pipeline
- Day 47: Data Preprocessing (Scaling, Encoding)
- Day 48: Train/Test Split, Cross-validation
- Day 49: Linear Regression
- Day 50: Polynomial Regression
- Day 51: Logistic Regression
- Day 52: K-Nearest Neighbors (KNN)
- Day 53: Support Vector Machine (SVM)
- Day 54: Decision Trees
- Day 55: Random Forest
- Day 56: Gradient Boosting & XGBoost
- Day 57: Unsupervised ML: Clustering (K-Means)
- Day 58: Hierarchical Clustering
- Day 59: Dimensionality Reduction (PCA)
- Day 60: Model Evaluation Metrics (Accuracy, Confusion Matrix, ROC-AUC)
- Day 61: Project 1: House Price Prediction
- Day 62: Project 2: Customer Churn Analysis
- Day 63: Project 3: Movie Recommendation System
- Day 64: Project 4: Email Spam Classifier
- Day 65: Project 5: Credit Card Fraud Detection
- Day 66: Introduction to Databases and SQL
- Day 67: SELECT, WHERE, ORDER BY
- Day 68: Aggregate Functions (COUNT, AVG, SUM)
- Day 69: GROUP BY, HAVING
- Day 70: JOINS (INNER, LEFT, RIGHT, FULL)
- Day 71: Subqueries, CTEs
- Day 72: SQL Project: Analyze Sales Data
- Day 73: Introduction to NLP
- Day 74: Text Preprocessing (Tokenization, Stopwords)
- Day 75: Sentiment Analysis
- Day 76: TF-IDF and Word Embeddings
- Day 77: NLP Project
- Day 78: Time Series Forecasting Intro
- Day 79: ARIMA, SARIMA Models
- Day 80: Time Series Project
- Day 81: Intro to Deep Learning, Perceptron
- Day 82: Neural Network Architecture
- Day 83: Activation Functions, Loss Functions
- Day 84: Feedforward and Backpropagation
- Day 85: Hands-on with Keras/TensorFlow
- Day 86: CNNs and Image Classification (Overview)
- Day 87: Deep Learning Mini Project
- Day 88: Creating a GitHub Portfolio
- Day 89: Resume Building for Data Scientists
- Day 90: Deploy Project (Streamlit/Flask + GitHub + Heroku)
Capstone Projects
Groceries Application
Build a lightning-fast delivery app with logistics and inventory features.
Food Application
Design a food delivery app with real-time order tracking and restaurant listings.
Banking Application
Create a secure and user-friendly digital payments platform.
Delivery Application
Develop an auction-based e-commerce system with reviews and bidding.
Airlines Application
Build a travel booking system for flights, hotels, and holiday packages.
LMS Application
Create an e-learning platform with course, video, and user dashboard modules.
Job-ready Profiles
Github
For freshers, GitHub plays a significant role in showcasing skills
Portfolio
For freshers, a portfolio demonstrates practical experience and personal projects effectivelyCertificate
