Certified Data Science with Python
32 Hrs Duration
10+
Modules
4+ Live project
Internship support
Interview Support
KOED Academy – Program Highlights
- 5 Industry-Aligned Courses
Master core and advanced concepts across Data Science, Product Management, and Financial Modelling. - 32+ Hours of Applied Learning
Gain hands-on experience through practical assignments, projects, and tool-based training. - 8 Professional Development Units (PDUs)
Earn PDUs to showcase your commitment to continuous learning and upskilling. - 4 Real-World Projects
Work on actual business challenges to apply concepts in real-life scenarios. - 1:1 Mentorship Sessions
Receive personalized guidance from industry mentors to support your learning and career goals5
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Our Students are Placed in
Curriculum
| Overview of Data Science |
| Applications Across Industries |
| Data Science vs. Related Fields: AI, ML, Data Analytics |
| Ethics and Data Privacy |
| Python Basics: Variables, Data Types, Control Structures |
| Data Structures: Lists, Tuples, Dictionaries, Sets |
| Loops- For, while |
| Object oriented programming |
| Functions and Modules |
| Libraries: NumPy, Pandas, Matplotlib, Seaborn |
| Version Control: Git and GitHub Basics |
| Descriptive Statistics |
| Probability Distributions |
| Inferential Statistics |
| Bayesian Thinking |
| Data Cleaning |
| Data Transformation |
| Feature Engineering |
| Data Integration and Reduction |
| Relational Databases: |
| NoSQL Databases: |
| Data Warehousing Concepts |
| ETL Processes |
| Principles of Effective Visualization |
| Tools: Tableau/ Power BI |
| Python Libraries: Matplotlib, Seaborn, Plotly |
| Dashboard Creation |
| Supervised Learning |
| Unsupervised Learning |
| Ensemble Learning |
| Model Evaluation |
| Overfitting and Underfitting |
| Neural Networks Basics |
| Convolutional Neural Networks (CNNs) |
| Recurrent Neural Networks (RNNs) |
| Frameworks: TensorFlow, Keras, PyTorch |
| Text Preprocessing |
| Sentiment Analysis |
| Topic Modeling |
| Language Models |
| Project Proposal and Planning |
| Data Collection and Analysis |
| Model Development and Evaluation |
| Presentation and Reporting |
Skills Covered
Creating And Managing Data
Analytics
Data Structure
Programming Constructs
Data Partitioning
Indexing and Slicing of Arrays
SQL
Data Mining
Job Roles
Data Scientist
Statistician
Data Engineers
Data Architect
Data and Analytics Manager
Data Analyst
Tools Covered
Python
Numpy
Jupitar
panda
Matplotlib
Sklearn
seaborn
Streamlit
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