What will you learn in Applied Data Science with Python Specialization Course
Use Python and libraries like Pandas, Matplotlib, Scikit-learn, NLTK, and NetworkX for advanced data analysis.
Perform inferential statistical analysis and evaluate sampling accuracy.
Create effective data visualizations and interpret multivariate patterns.
Build predictive models using supervised and unsupervised machine learning.
Transform text data and analyze social networks using real-world datasets.
Program Overview
Module 1: Introduction to Data Science in Python
⏳ 4 weeks
• Topics: Python essentials (lambdas, CSV handling), Pandas for data manipulation and cleaning, basic statistics and t-tests.
• Hands-on: Explore DataFrame operations like groupby, merging, and pivot tables using real datasets.
Module 2: Applied Plotting, Charting & Data Representation in Python
⏳ 4 weeks
• Topics: Design principles of good visualizations, chart selection, Matplotlib functions for varied use cases.
• Hands-on: Create visualizations using Matplotlib that communicate data insights cleanly.
Module 3: Applied Machine Learning in Python
⏳ 4 weeks
• Topics: Difference between ML and statistics, clustering, predictive model building, feature engineering.
• Hands-on: Train models like decision trees and clustering algorithms, evaluate and compare performance.
Module 4: Applied Text Mining in Python
⏳ 4 weeks
• Topics: Text parsing, NLP fundamentals, topic modeling, usage of NLTK for text processing.
• Hands-on: Write code to clean text, classify documents, and extract topic insights from collections.
Module 5: Applied Social Network Analysis in Python
⏳ 4 weeks
• Topics: Network representation using NetworkX, node centrality, connectivity measures, network dynamics.
• Hands-on: Analyze network graphs, compute centrality metrics, and model network evolution.
Get certificate
Job Outlook
Widely applicable across roles like data scientist, analyst, ML engineer, or research data specialist.
Python proficiency and domain experience are essential for data-driven roles across industries.
Salaries range from ₹8–20 LPA (India) and $80–$150K (US), depending on experience and specialization.
Builds a portfolio-ready foundation for freelance and remote analytics work.
Specification: Applied Data Science with Python Specialization Course
|
FAQs
- Prior Python knowledge recommended; not for absolute beginners.
- Focuses on applying Python to data manipulation, visualization, and machine learning.
- Hands-on projects using Pandas, Matplotlib, Scikit-learn, NLTK, and NetworkX.
- Includes exercises on real-world datasets for practical learning.
- Prepares learners for professional data science tasks and portfolio projects.
- Covers inferential statistical analysis and sampling evaluation.
- Teaches data visualization with Matplotlib and Seaborn.
- Focuses on multivariate data patterns and insights.
- Includes hands-on exercises for cleaning and analyzing datasets.
- Prepares learners to communicate data-driven insights effectively.
- Builds skills for Data Scientist, ML Engineer, or Data Analyst roles.
- Covers predictive modeling with supervised and unsupervised learning.
- Includes text mining and social network analysis for domain versatility.
- Supports portfolio development with project-based learning.
- Enhances employability across tech, finance, healthcare, and research sectors.
- Five modules, approximately 4 weeks each.
- Covers Python essentials, plotting, machine learning, text mining, and social network analysis.
- Self-paced format allows flexible learning schedules.
- Includes hands-on exercises and a capstone project.
- Suitable for learners aiming for comprehensive applied data science training.
- Learn NLP techniques for text data using NLTK.
- Analyze network graphs with NetworkX, computing centrality and connectivity metrics.
- Build and evaluate predictive models using Python libraries.
- Apply methods on real-world datasets for hands-on experience.
- Skills are directly transferable to professional data science projects and analytics roles.
