Data Science Course: Your Path to Becoming a Top Data Scientist
Who Is This Bootcamp For?
Data Science Course is tailored for:
Beginners: No prior experience needed.
Students and Working Professionals: Ideal for those with a basic knowledge of Python who wish to delve deeper into data science.
Course Overview
You will explore essential tools and technologies, including data analysis, machine learning, deep learning, and AI.
Key Learning Areas:
- Data Analysis with Python:
Learn to manipulate and analyze data using Python.
- Working with Different Types of Datasets:
Understand various data formats and structures.
- DIY Datasets with Web Scraping:
Create your own datasets by scraping data from the web.
- Dashboards with Tableau:
Visualize data and create interactive dashboards.
- Building ML Models from Scratch:
Develop machine learning models using popular libraries.
- NLP with NLTK and SpaCy:
Process and analyze natural language data.
- Neural Networks with TensorFlow:
Implement neural networks for deep learning applications.
- Working with Images using OpenCV:
Perform image processing and computer vision tasks.
- Computer Vision with CNN:
Use convolutional neural networks for advanced image recognition.
Course Content
01. Getting Started with Python
- Python Basics: Introduction to Python programming.
- Loops in Python: Understanding and using loops for iteration.
- Functions in Python: Creating and using functions for modular code.
- Strings in Python: Manipulating string data.
- Data Structures: Exploring lists, tuples, dictionaries, and sets.
- Working with Files: Reading from and writing to files.
02. OS with Python
- Getting Started with OS: Introduction to operating system interactions.
- Jupyter Notebook Setup: Setting up and using Jupyter Notebook for data analysis.
- OS with Python: Performing OS-level operations using Python.
03. Mastering Numpy Arrays
- Mastering Numpy Arrays: Efficient numerical computations using Numpy.
04. Data Analysis with Python
- Getting Started with Pandas: Data manipulation and analysis with Pandas.
- Statistics: Basic statistical concepts and methods.
- Data Preprocessing: Preparing data for analysis.
- Data Analysis: Performing comprehensive data analysis.
05. Tableau
- Introduction to Tableau: Getting started with Tableau for data visualization.
- Understanding Parameters: Using parameters to enhance interactivity.
- Basic Plots in Tableau: Creating basic visualizations.
- Fundamentals of Tableau: Mastering Tableau's core features.
- Designing Plots: Advanced plot design techniques.
06. Web Scraping
- Introduction to Scraping: Basics of web scraping.
- Using Selenium: Automating web browsing and data extraction.
- Project: YouTube Scraper: Building a YouTube data scraper.
- Project: Stock Images Infinite Scroll: Scraping stock images from websites.
- Image Dataset Creation: Creating custom image datasets.
07. Getting Started with AI
- Introduction to AI: Basics of artificial intelligence.
- Role of Data Science in AI: How data science powers AI applications.
08. Machine Learning
- Linear Regression: Understanding and implementing linear regression.
- Multiple Linear Regression: Extending linear regression to multiple variables.
- Polynomial Linear Regression: Modeling non-linear relationships.
- Support Vector Machine: Classifying data with SVM.
- Decision Tree: Building decision tree models.
- Random Forest: Ensemble learning with random forests.
- Classification Algorithms: Exploring various classification techniques.
- Clustering Algorithms: Grouping data with clustering methods.
- Feature Engineering: Enhancing model performance through feature engineering.
09. Deep Learning
- Perceptrons: Understanding the basics of neural networks.
- Multi-layer Perceptron Architecture: Building complex neural network architectures.
- Convolutional Neural Network (CNN): Advanced image recognition with CNN.
10. Image Processing
- Fundamentals of Image Formation: Basics of how images are formed.
- Image Processing Techniques: Various techniques for processing images.
- Image Processing with Live Webcam: Real-time image processing.
- Taking a Selfie Program with OpenCV: Building a selfie-taking application.
- Image Manipulation: Editing and manipulating images.
- DIY Instagram Filters: Creating custom image filters.
- Masking: Applying masks to images.
- Adding Logo on Live Video: Overlaying logos on video streams.
- Face Detection and Manipulation: Detecting and manipulating faces in images.
11. Natural Language Processing
- Getting Started with NLP: Basics of Natural Language Processing.
- Mastering Strings and ASCII Codes: Working with Text Data.
- Regular Expressions from Scratch: Using regular expressions for text processing.
- Getting Started with SpaCy: Advanced NLP with SpaCy.