Data Science with Deep Learning

MODULE 1 : PYTHON FOR DATA ANALYSIS & DATA SCIENCE

Introduction to Python for Data Analysis (1 hour)

Working with Libraries (1 hour)

Data Cleaning and Preprocessing (1 hour)

Exploratory Data Analysis (1 hour)

Module 2 : Data Analysis for another 5 hours with practical data set analysis

Recap of Python for Data Analysis (1 hour)

Brief review of Python basics and key libraries (NumPy, Pandas, Matplotlib).

Importing and Exploring Datasets (1 hour)

Data Cleaning and Pre-processing (1 hour)

Advanced Data Visualization (1 hour)

Statistical Analysis and Hypothesis Testing (1 hour)

Practical Data Analysis Project (1 hour)

DATASETs FOR DATA ANALYSIS

Iris Dataset:

Description: Measurements of sepal length, sepal width, petal length, and petal width for three species of iris flowers.

Domain: Botany

Use: Classification, basic statistical analysis, visualization.

Titanic Dataset:

Description: Passenger information on the Titanic, including survival status, class, gender, and age.

Domain: Transportation

Use: Survival analysis, categorical analysis, visualization.

Boston Housing Dataset:

Description: Housing prices and various factors affecting them in Boston suburbs.

Domain: Real Estate

Use: Regression analysis, correlation analysis, visualization.

Wine Dataset:

Description: Chemical analysis results of wines from three different cultivars.

Domain: Food and Beverage

Use: Classification, cluster analysis, visualization.

Diabetes Dataset:

Description: Various health metrics for diabetes patients.

Domain: Healthcare

Use: Regression analysis, correlation analysis, visualization.

Heart Disease UCI Dataset:

Description: Patient data related to heart disease.

Domain: Healthcare

Use: Classification, statistical analysis, visualization.

Breast Cancer Wisconsin (Diagnostic) Dataset:

Description: Biopsy results for breast cancer diagnosis.

Domain: Healthcare

Use: Classification, statistical analysis, visualization.

Penguins Dataset:

Description: Measurements and characteristics of penguin species.

Domain: Biology

Use: Classification, cluster analysis, visualization.

Advertising Dataset:

Description: Sales and advertising spending data.

Domain: Marketing

Use: Regression analysis, correlation analysis, visualization.

Student Exam Scores Dataset:

Description: Exam scores of students with attributes like study hours, attendance, etc.

Domain: Education

Use: Regression analysis, correlation analysis, visualization.

MODULE 3: MACHINE LEARNING

Supervised Learning Algorithms:

Decision Trees and Random Forest:

Dataset: Titanic Dataset

Objective: Predicting survival status based on features such as class, gender, and age.

Linear Regression:

Dataset: Boston Housing Dataset

Objective: Predicting housing prices based on various factors like crime rate, room numbers, etc.

K-Nearest Neighbors (KNN):

Dataset: Breast Cancer Wisconsin (Diagnostic) Dataset

Objective: Classifying tumors as malignant or benign based on various features.

Support Vector Machines (SVM):

Dataset: Heart Disease UCI Dataset

Objective: Predicting the presence or absence of heart disease based on patient data.

Logistic Regression:

Dataset: Student Exam Scores Dataset

Objective: Predicting the likelihood of a student passing an exam based on study hours and attendance.

MODULE 4 

Unsupervised Learning Algorithms:

K-Means Clustering:

Dataset: Wine Dataset

Objective: Grouping wines into clusters based on their chemical analysis results.

Hierarchical Clustering:

Dataset: Penguins Dataset

Objective: Discovering natural groupings of penguin species based on their measurements.

Principal Component Analysis (PCA):

Dataset: Iris Dataset

Objective: Reducing dimensionality and visualizing relationships among iris species.

Association Rule Mining (Apriori Algorithm):

Dataset: Advertising Dataset

Objective: Discovering associations between different advertising channels and sales.

Gaussian Mixture Model (GMM):

Dataset: Diabetes Dataset

Objective: Identifying patterns and clusters within health metrics.

MODULE 5 : DEEP LEARNING

COVID-19 Open Research Dataset (CORD-19):

Description: A collection of research papers, articles, and information related to COVID-19.

Use: Natural Language Processing (NLP), text mining, and information retrieval for understanding and analyzing research on the coronavirus.

Link: CORD-19 Dataset

Common Objects in Context (COCO):

Description: A large-scale object detection, segmentation, and captioning dataset.

Use: Computer Vision tasks such as image classification, object detection, and image segmentation.

Link: COCO Dataset

ImageNet:

Description: A large-scale image dataset with millions of labeled images across thousands of classes.

Use: Image classification, object detection, and transfer learning in deep learning projects.

Link: ImageNet Dataset

MODULE 6: AI MODELING

Natural Language Processing (NLP) for Sentiment Analysis:

Application: Analyzing Social Media Sentiments

Description: Build a sentiment analysis model using NLP techniques to analyze and understand the sentiment expressed in social media posts, customer reviews, or comments. This model can help businesses gauge public opinion, monitor brand perception, and identify areas for improvement.

Computer Vision for Object Detection:

Application: Autonomous Vehicles

Description: Develop a computer vision model for object detection to be used in autonomous vehicles. The model can identify and classify objects such as pedestrians, vehicles, traffic signs, and obstacles in real-time, allowing the vehicle to make informed decisions based on its surroundings.

Recommender System for E-Commerce:

Application: Personalized Product Recommendations

Description: Implement a recommender system using collaborative filtering or content-based filtering techniques for an e-commerce platform. This model can analyze user behavior, preferences, and purchase history to recommend products tailored to individual users, enhancing the user experience and increasing the likelihood of successful transactions.

TOTAL NOF OF HOURS L: 45 to 50 hours

Classes : MON-WED-FRI ( TUE to SAT Indian Calender)

TIME : 8AM to 9AM IST