Course syllabus
Data Science with Python
Career Catalyst — 6-month placement-focused Python, Data, Analytics & AI program (240+ live hours)
Relaunch offer — first 2 batches only
24 weeks · ~10 hours/week · 240+ live hours. 100% online — live, interactive, and flexible. Learn from home or anywhere, grow from anywhere, and build your future without relocating. Recordings after every class; daily/weekly/module assessments and monthly grand test / interview.
Who it’s for: Students and career switchers targeting Data Analyst, Python Developer, Jr Data Scientist, ML / GenAI engineer (fresher) roles.
After you pay
What enrollment unlocks
Payment is processed on Feednet Solutions. Approved students get the learning home, recordings, and assessments there.
- Access to everyday class recording sessions
- Daily assessments + module-wise and weekly assessments
- Saturday assignments that raise your score
- Monthly grand test / monthly offline or online interview
- Learning-home (LMS) dashboard after enrollment
- Resume, portfolio & mock-interview support on the paid track
Curriculum
Modules
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Month 1 · Module 1 — Python Programming (Weeks 1–4)
- Goal: build strong coding & logical thinking skills
- Python basics, variables, data types
- Conditional statements, loops
- Functions & recursion
- List, tuple, set, dictionary; strings
- File handling & exception handling
- OOPs in Python & coding best practices
- Projects: student result system, employee payroll, ATM simulation, mini library tool
- Outcome: write clean Python code; crack Python coding rounds
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Month 2 · Module 2 — Excel for Data Analysis (Weeks 5–6)
- Advanced formulas (VLOOKUP / XLOOKUP, IF, COUNTIF)
- Pivot tables & charts
- Data cleaning techniques
- Dashboards & reporting; business use cases
- Project: sales performance dashboard
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Month 2 · Module 3 — SQL for Data Analysis (Weeks 7–8)
- Database concepts
- SELECT, WHERE, GROUP BY
- Joins (Inner, Left, Right, Outer)
- Subqueries & window functions
- Real-world query scenarios
- Projects: e-commerce DB analysis; employee performance SQL case study
- Outcome: ready for SQL interview rounds
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Month 3 · Module 4 — Data Analysis with Python (Weeks 9–10)
- Tools: NumPy, Pandas, Matplotlib, Seaborn, Regex
- Data cleaning & preprocessing
- Exploratory Data Analysis (EDA)
- Data visualization & feature engineering
- Handling missing data
- Project: Zomato / Netflix / IPL-style data analysis
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Month 4 · Module 5 — Statistics for Data Science (Weeks 11–12)
- Descriptive statistics, probability, distributions
- Hypothesis testing
- Correlation & regression; A/B testing
- Project: customer behavior statistical analysis report
- Outcome: strong analytical thinking for DS interviews
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Month 4 · Module 6 — Power BI (Weeks 13–14)
- Power BI interface & data modeling
- DAX basics
- Interactive dashboards & business storytelling
- Project: executive business insights dashboard
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Month 4 · Module 7 — Machine Learning (Weeks 15–16)
- ML workflow; supervised & unsupervised learning
- Regression & classification
- Decision trees & random forest; KNN, Naive Bayes
- Model evaluation
- Projects: five hands-on ML projects
- Outcome: end-to-end ML project confidence
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Month 5 · Module 8 — Deep Learning (Weeks 17–18)
- Tools: TensorFlow / Keras
- Neural networks & backpropagation
- ANN & CNN; activation functions
- Model optimization
- Project: handwritten digit recognition (MNIST)
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Month 5 · Module 9 — Natural Language Processing (Weeks 19–20)
- Text processing; TF-IDF & word embeddings
- Sentiment analysis & text classification
- Chatbot basics; BERT model (basics)
- Projects: movie review sentiment analyzer; resume screening NLP model
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Month 6 · Module 10 — Computer Vision (Weeks 21–22)
- Image processing basics; OpenCV
- Face detection; object detection & tracking
- CNN for vision tasks
- Projects: face-recognition attendance; object detection & counting in live video
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Month 6 · Module 11 — Generative AI (Weeks 23–24)
- LLM fundamentals; transformers
- Prompt engineering
- OpenAI / open-source models
- RAG concepts; AI agents overview
- Project: build a simple GenAI app using prompts and an LLM
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Final outcomes — job-ready track
- 15+ real-world projects; strong GitHub portfolio
- Resume & LinkedIn optimization; mock interviews & aptitude prep
- Target roles: Python Developer, Data Analyst, Jr Data Scientist, Business Analyst, AI/ML / GenAI Engineer (fresher tracks)