Two days offline Technical Workshop
Edufabrica Offline Workshop Series with E Cell, IIT GUWAHATI 2025
Workshop Info
Workshop Fees
Book your seat now for just ₹1,050/-(Rs ₹3,000)—limited-time offer!
Workshop Topic
Data science using python
Workshop Dates
29th-30th March 2025
Workshop Venue
Indian Institute of Technology Guwahati Campus
Provide Certificate
DATA SCIENCE USING PYTHON
OFFLINE WORKSHOP COURSE CONTENT
FOUNDATIONS OF DATA SCIENCE AND PYTHON
Session 1: Introduction to Data Science
What is Data Science?
– Key concepts and terminology
– Data Science lifecycle and methodologies
– Overview of tools and technologies used in Data Science
FOUNDATIONS OF DATA SCIENCE AND PYTHON
Session 2: Python Basics
– Introduction to Python and its environment (Anaconda,
Jupyter Notebook)
– Data types, variables, and basic operations
– Control structures (if statements, loops)
– Functions and modular programming
FOUNDATIONS OF DATA SCIENCE AND PYTHON
Session 3: Data Handling with Pandas
– Introduction to Pandas: Series and DataFrames
– Importing and exporting data (CSV, Excel)
– Data exploration and inspection
– Data cleaning techniques (handling missing values, duplicates)
– Data transformation (filtering, sorting, grouping)
FOUNDATIONS OF DATA SCIENCE AND PYTHON
Session 4: Data Visualization Basics
– Importance of data visualization
– Introduction to Matplotlib and Seaborn
– Creating basic plots: line plots, bar charts, histograms
– Customizing visualizations: titles, labels, legends
INTERMEDIATE TOPICS AND MACHINE LEARNING
Session 5: Exploratory Data Analysis (EDA)
– Techniques for EDA
– Descriptive statistics and data summaries
– Identifying patterns, correlations, and outliers
– Visualizing relationships between variables
INTERMEDIATE TOPICS AND MACHINE LEARNING
Session 6: Introduction to Machine Learning
– Overview of Machine Learning concepts
– Types of Machine Learning: Supervised vs. Unsupervised
– Introduction to Scikit-Learn
– Setting up a machine learning environment
INTERMEDIATE TOPICS AND MACHINE LEARNING
Session 7: Supervised Learning Techniques
– Linear Regression: Theory, implementation, and evaluation
– Classification algorithms: Logistic Regression, Decision Trees
– Model evaluation metrics: Accuracy, Precision, Recall, F1 Score
– Overfitting and underfitting concepts
INTERMEDIATE TOPICS AND MACHINE LEARNING
Session 8: Unsupervised Learning Techniques
– Clustering: K-Means and Hierarchical Clustering
– Dimensionality Reduction: PCA (Principal Component Analysis)
ADVANCED TOPICS AND PROJECT WORK (OPTIONAL
Session 9: Model Deployment Basics (1 hour)
– Introduction to model deployment concepts
– Overview of Flask for creating simple web applications
– Basics of RESTful APIs
ADVANCED TOPICS AND PROJECT WORK
Session 10: Advanced Data Visualization
– Interactive visualizations with Plotly
– Advanced techniques in Seaborn
– Dashboard creation using Dash or Streamlit