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Data Science

Welcome to the frontier of knowledge: Data Science. In our courses, we embark on a journey through the vast landscape of data, where insights are waiting to be unearthed and decisions are guided by the power of analytics. Whether you’re a curious novice or a seasoned professional, our curriculum is crafted to equip you with the tools and techniques to navigate this intricate realm with confidence. From foundational principles to advanced methodologies, we offer a comprehensive exploration of data science, blending theory with practical applications. Join us as we decode the language of data, uncover patterns, and transform information into intelligence. Get ready to embark on a transformative learning experience, where the possibilities are as boundless as the data itself

Data Science Syllabus

  • Overview of Data Science
    • Importance and Applications
    • Data Science Process
  • Tools and Technologies
    • Setting Up Development Environment
    • Version Control with Git and GitHub
  • Introduction to Python
    • Basic Syntax and Data Types
    • Control Structures (if, for, while)
    • Functions and Modules
  • Data Handling with Python
    • NumPy for Numerical Computations
    • Pandas for Data Manipulation
    • Matplotlib and Seaborn for Data Visualization

1. Introduction to Python

  • Basics: High-level, interpreted language.
  • Data Types: Integers, Floats, Strings, Lists, Tuples, Sets, Dictionaries.
  • Variables: Assignment and naming conventions.
  • Operators: Arithmetic, Comparison, Logical, Assignment.
  • Input/Output: input() for input, print() for output.

2. Conditional Statements

  • If-Else: if, elif, else for decision-making.

3. Loops

  • While: Repeats while a condition is true.
  • For: Iterates over sequences (lists, strings).
  • Nested Loops: Loops inside loops.

4. Collections

  • List: Ordered, mutable.
  • Tuple: Ordered, immutable.
  • Set: Unordered, unique.
  • Dictionary: Key-value pairs.

5. Functions

  • Basics: def to define functions, return to return values.
  • Lambda: Anonymous functions.
  • Built-ins: map(), filter(), reduce(), zip().

6. OOP (Object-Oriented Programming)

  • Class & Objects: Class defines objects with attributes/methods.
  • Inheritance: Extending classes.
  • Constructor: __init__() initializes objects.

7. File Handling

  • Read/Write: Open files, read/write data.
  • With: Automatic file closing using with.

8. Exception Handling

  • Try-Except: Handling errors, finally for cleanup.

9. Advanced Python

  • Libraries: Numpy (arrays), Pandas (dataframes), Matplotlib/Seaborn (visualization), EDA (data analysis).

 

Introduction to Databases and MySQL

1.1 Data and Databases

  • Data Types: Structured, semi-structured, unstructured data.
  • Databases: Organized collection of data in tables; benefits include efficient management, integrity, and centralized access.
  • RDBMS: Relational databases store data in tables with relationships (using keys); provide integrity, easy querying, and structure.

2. MySQL Commands and Data Types

2.1 DDL & DML Commands

  • Database & Table Management:
    • CREATE DATABASE, DROP DATABASE, CREATE TABLE, DROP TABLE, ALTER TABLE (modify structure).
    • SHOW DATABASES, SHOW TABLES (list databases and tables).
  • Data Manipulation:
    • INSERT, UPDATE, DELETE to manage data in tables.
    • TRUNCATE TABLE (remove all rows).

2.2 Data Types and Constraints

  • Data Types: Numeric (INT, FLOAT), Date/Time (DATE, DATETIME), String (VARCHAR, TEXT).
  • Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, DEFAULT, AUTO_INCREMENT, CHECK.

3. Data Retrieval and Advanced Queries

3.1 Querying Data

  • SELECT: Retrieve data from tables, with WHERE, ORDER BY, GROUP BY, HAVING, DISTINCT clauses.
  • Joins: Combine data from multiple tables using INNER JOIN, LEFT JOIN, RIGHT JOIN, CROSS JOIN.
  • Aggregates: Functions like SUM(), COUNT(), MIN(), AVG().

3.2 Subqueries, Views & Window Functions

  • Subqueries: Nested queries within SELECT, INSERT, UPDATE, DELETE.
  • Views: Virtual tables for simplified complex queries (CREATE VIEW).
  • Window Functions: ROW_NUMBER(), RANK(), DENSE_RANK() for ranking results.

4. Procedures, Triggers, and Data Control

4.1 Procedures and Triggers

  • Stored Procedures: Store reusable queries with input parameters (CREATE PROCEDURE).
  • Triggers: Automatically execute queries in response to data changes (CREATE TRIGGER).

4.2 Transaction Control & Data Security

  • TCL: Manage transactions with COMMIT, ROLLBACK, SAVEPOINT.
  • DCL: Manage user access with GRANT and REVOKE.

5. Importing/Exporting Data

  • CSV Operations:
    • Import: LOAD DATA INFILE to load CSV data.
    • Export: SELECT INTO OUTFILE to export query results to CSV.

1. Getting Started with Excel

  • Overview: Explore Excel’s capabilities.
  • Navigation: Key elements of spreadsheets and managing workbooks.

2. Data Entry & Manipulation

  • Data Entry: Efficient input of text, numbers, and formulas.
  • Editing: Modify content, undo/redo, and use Find & Replace.
  • Copy/Paste: Basic and advanced techniques.

3. Formatting for Clarity

  • Formatting: Adjust fonts, alignment, and cell styles.
  • Conditional Formatting: Highlight data based on rules.
  • Customizing: Personalize views and freeze panes.

4. Formulas & Calculations

  • Basic Operations: Addition, subtraction, multiplication, division.
  • Formula Writing: Create simple formulas, apply BODMAS rules.

5. Advanced Data Manipulation

  • Fill & Flash Fill: Auto-fill patterns and data.
  • Logical Functions: Use IF statements and nested functions.

6. Working with Functions

  • Statistical Functions: SUMIF, COUNTIF, AVERAGEIF.
  • Text Functions: TRIM, PROPER, UPPER, LOWER.
  • Date/Time Functions: TODAY, NOW, DAY, MONTH, YEAR.
  • Lookup Functions: VLOOKUP, INDEX/MATCH.

7. Sorting & Filtering

  • Sorting: Organize data alphabetically or numerically.
  • Filtering: Focus on specific data subsets.

8. PivotTables

  • Introduction: Use PivotTables for data summarization and analysis.

9. Data Visualization

      • Charts: Create and customize basic charts (column, pie, bar).
      • Customizing: Enhance charts with titles, labels, and legends.

1. Getting Started with Power BI

  • Overview: Key capabilities and applications.
  • Installation: Setting up Power BI Desktop.
  • Why Power BI?: Advantages over other BI tools.

2. Visualizing Data

  • Basic Charts: Column, pie, donut, funnel.
  • Maps: Visualize geographic data.
  • Tables: Formatting and conditional formatting.
  • Aggregation: SUM, AVG, MIN, MAX.

3. Expanding Visualizations

  • Additional Charts: Line, area, scatter, waterfall.
  • Cards & Filters: Add interactive cards and filters.
  • Slicers: Dynamic data filtering.

4. Enhancing Reports

  • Objects: Insert images, text, shapes, buttons.

5. Power BI Service & Exporting

  • Service: Microsoft account and sharing features.
  • Exporting: Export to PPT, PDF, PBIX.

6. Power Query & Data Transformation

    • Text, Date, Number Functions: Clean and format data.
    • Append & Merge: Combine and merge files.
    • Conditional Columns: Create columns based on conditions.

1. Introduction to Machine Learning

  • Key Concepts: Overview and applications.
  • Supervised vs Unsupervised: Basic differences.
  • Data Preparation: Preprocessing for ML.
  • Python Libraries: Essential libraries for ML.

2. Supervised Learning

Regression

  • Linear, Polynomial, SVR, Decision Tree, Random Forest: Various regression techniques.

Classification

  • Logistic Regression, KNN, SVM, Naive Bayes, Decision Trees, Random Forest & Gradient Boosting: Common classification methods.

3. Model Evaluation

  • A/B Testing: Performance comparison.

4. Unsupervised Learning

  • Clustering: K-Means, Hierarchical Clustering.
  • Association: Apriori Algorithm.

5. Reinforcement Learning

    • Concepts: Exploration vs. exploitation (UCB, Thompson Sampling).
  • Introduction to NLP
    • Text Preprocessing (Tokenization, Lemmatization)
    • Feature Extraction (TF-IDF, Word Embeddings)
  • NLP Techniques
    • Sentiment Analysis
    • Named Entity Recognition (NER)
    • Topic Modeling (LDA)
  • Advanced NLP
    • Transformers and BERT
    • Building Chatbots

1. Introduction to Deep Learning

  • Overview: What is Deep Learning, applications, and neural networks (ANNs).

2. Neural Networks & Training

  • Network Basics: Perceptrons, feedforward networks, and activation functions (Sigmoid, ReLU).
  • Training: Forward & backpropagation, optimization (Gradient Descent, Adam), loss functions (MSE, Cross-Entropy).

3. Key Architectures

  • CNNs: Convolution, pooling, and architectures like LeNet and ResNet for image classification.
  • RNNs & LSTMs: Sequential data analysis, time series, and GRUs as alternatives.

4. Generative Models

  • Autoencoders & GANs: Data compression, unsupervised learning, and generating new data.

5. Advanced Topics

  • Transfer Learning: Reusing pre-trained models for new tasks.
  • Attention & Self-Supervised Learning: Transformers for NLP and unsupervised learning without labels.

6. Tools & Applications

  • Frameworks: TensorFlow/Keras, PyTorch for building models.
  • Applications: Computer Vision, NLP, and Reinforcement Learning (Deep Q-Learning).

7. Model Evaluation & Deployment

  • Evaluation: Metrics, regularization (dropout), and handling overfitting.
  • Deployment: Saving models and cloud deployment.
  • Front-End Development
    • HTML and CSS Basics
    • JavaScript and Front-End Frameworks (React/Vue)
  • Back-End Development
    • Building RESTful APIs with Django/Flask
    • Database Integration (SQL/MongoDB)
  • Deployment and Cloud
    • Docker and Kubernetes Basics
    • AWS Cloud Services for Deployment
    • CI/CD Pipelines
  • Project Planning
    • Defining Requirements and Scope
    • Designing the Architecture
  • Implementation
    • Data Collection and Preparation
    • Model Training and Evaluation
  • Deployment and Presentation
    • Building a Web Interface
    • Final Deployment and Reporting
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