Data Science with Python

What Will You Learn?

• Module 1: Introduction to Data Science (Duration - 1hr)
• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & its types
• Module 2: Introduction to Python (Duration - 1hr)
• What is Python?
• Why Python?
• Installing Python
• Python IDEs
• Jupyter Notebook Overview
• Hands-on-Exercise:
• Downloading and Installing Python
• Exploring Jupyter Notebook
• Module 3: Data Exploration & Cleaning (Duration - 2hrs)
• Python Libraries for Data Analysis
• NumPy
• Pandas
• Matplotlib
• Data Exploration with Pandas
• Data Cleaning Techniques
• Module 4: Statistical Analysis (Duration - 2hrs)
• Descriptive Statistics
• Measures of Central Tendency
• Measures of Dispersion
• Inferential Statistics
• Hypothesis Testing
• Confidence Intervals
• Module 5: Machine Learning (Duration - 4hrs)
• Supervised Learning
• Linear Regression
• Logistic Regression
• K-Nearest Neighbors
• Decision Trees
• Support Vector Machines
• Unsupervised Learning
• K-Means Clustering
• Principal Component Analysis (PCA)
• Module 6: Deep Learning (Duration - 3hrs)
• Artificial Neural Networks (ANNs)
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Natural Language Processing (NLP)
• Module 7: Tableau (Duration - 2hrs)
• Introduction to Tableau
• Data Visualization Techniques
• Creating Dashboards
• Module 8: SQL (Duration - 2hrs)
• Introduction to SQL
• Data Manipulation Language (DML)
• Data Query Language (DQL)
• Join Operations
• Module 9: Case Studies (Duration - 2hrs)
• Real-world case studies of Data Science applications
• Applying learned concepts to solve real-world problems
• Module 10: Project (Duration - 20hrs)
• Develop a Data Science project on a chosen topic
• Apply learned skills to solve a specific problem
• Present project findings and results
• Additional Resources:
• Online tutorials and documentation
• Data Science communities and forums
• Books and articles on Data Science topics
• Note: This syllabus is subject to change based on the latest industry trends and advancements in Data Science.