FEEDNET Artificial Intelligence Feednet Teach

Course syllabus

Data Science with Python

Turn data into insights. Build models. Solve real problems.

Duration 4 months Format 100% online · live · interactive · flexible Fee ₹19,999 (was ₹79,980 · 75% off)

Relaunch offer — first 2 batches only

4-month live online programme. Python, NumPy, Pandas, SQL, statistics, machine learning, and an end-to-end capstone. Learn from home or anywhere, with recordings after every class.

Who it’s for: Students and career switchers targeting Data Analyst, Python Developer, Jr Data Scientist, ML / GenAI engineer (fresher) roles.

Pay & enroll · ₹19,999 Download brochure Enquire WhatsApp

Highlights

What this program covers

  • 4-month live programme
  • Python, SQL, statistics, and machine learning
  • Retail, segmentation, pricing, and churn projects
  • Capstone: end-to-end data science solution

Opening slide

DATA SCIENCE

Data Science map: Big Data, Classification, Analyze, Statistics, Solving, Decision, and Knowledge.
  1. Big DataVolume, variety, velocity — the raw material.
  2. ClassificationGroup, label, and structure messy inputs.
  3. AnalyzeExplore patterns, outliers, and questions.
  4. StatisticsMeasure uncertainty — is the signal real?
  5. SolvingModels and code that answer the question.
  6. DecisionChoose an action with evidence, not guesswork.
  7. KnowledgeInsight you can teach, ship, and defend.

Whiteboard

Diagrams we draw in week one

Keep these three pictures. Python, SQL, stats, and ML are just sharper versions of the same loop.

01 — pick the right role

Analyst vs Scientist vs Engineer

Three roles: an analyst reports what happened; a data scientist models what will happen; a data engineer keeps reliable tables flowing.
  1. AnalystExcel / SQL → dashboard → a decision slide. Reports what happened.
  2. ScientistQuestion → stats / EDA → model + test → a defensible claim. This 4-month track.
  3. EngineerSources → pipeline → warehouse → reliable tables. See Azure Data Engineering.

Most “data science” job posts mix all three. We train the scientist path, and we show you where the engineer path splits off.

02 — the inner loop

Question to decision

Data Science loop: Question, Collect, Clean / EDA, Model / test, Decide with evidence.

If the signal is weak, you do not ship the model. Statistics exists to say that out loud.

03 — how the data arrives

Pipeline (DE sketch)

Production pipeline: Sources, ingest, lake, transform, warehouse, quality gate, BI, wrapped by an orchestrator.

You will consume tables from a pipeline even if you never build one. The Azure Data Engineering program is that sketch, in full.

  1. 01Python
  2. 02Pandas
  3. 03SQL
  4. 04Stats
  5. 05EDA
  6. 06ML
  7. 07Validate
  8. 08Capstone

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

  1. Module 1 — Python programming for data science
    • Python syntax, data types, conditions, loops, functions and modules
    • Data structures, reusable code and hands-on scripting
  2. Module 2 — NumPy and Pandas for data handling
    • NumPy arrays, vectorized operations and numerical computation
    • Pandas DataFrames: filtering, grouping, merging and data cleaning
  3. Module 3 — SQL and database fundamentals
    • SQL queries, joins, aggregation, subqueries, CTEs and window functions
    • Relational tables, keys and extracting data for analysis
  4. Module 4 — Statistics and probability
    • Descriptive statistics, distributions, probability and sampling
    • Correlation, hypothesis testing, confidence intervals and p-values
  5. Module 5 — Data collection and preprocessing
    • CSV, Excel, JSON, APIs and database connections
    • Missing values, outliers, encoding, scaling and data quality checks
  6. Module 6 — Exploratory data analysis
    • Univariate, bivariate and multivariate analysis
    • Pattern discovery, trend analysis, anomalies and business insights
  7. Module 7 — Data visualization and storytelling
    • Matplotlib, Seaborn and visualization best practices
    • Charts, dashboards and communicating analytical findings
  8. Module 8 — Machine learning fundamentals
    • Supervised vs. unsupervised learning and the model workflow
    • Train/test split, cross-validation, overfitting and baseline models
  9. Module 9 — Supervised machine learning
    • Regression and classification: linear, logistic, trees and random forest
    • Model evaluation: MAE, RMSE, accuracy, precision, recall and F1
  10. Module 10 — Unsupervised learning and feature engineering
    • Clustering, K-Means, dimensionality reduction and PCA
    • Feature creation, selection, encoding and scaling
  11. Module 11 — Model validation and improvement
    • Cross-validation, hyperparameter tuning and model comparison
    • Confusion matrix, ROC-AUC, error analysis and leakage prevention
  12. Module 12 — Introduction to deep learning and deployment
    • Neural network fundamentals and a TensorFlow/Keras introduction
    • Model saving, simple API deployment, integration and documentation
  13. Projects, capstone, and career guidance
    • Retail sales analysis and insights
    • Customer segmentation with Python
    • House price prediction / regression
    • Customer churn prediction
    • End-to-end data science case study
    • Capstone: define a problem, prepare data, explore insights, train and evaluate a model, then present findings and a working demonstration
    • Career and interview guidance

On completion

Certificate of completion

After you finish Data Science with Python, Feednet Teach issues a certificate like this in your name. The learner name is hidden on this sample.

Sample
Feednet Solutions Pvt Ltd Hyderabad, Telangana, India CIN: U72900TG2019PTC134896

Certificate of Completion

Data Science with Python

This certificate is proudly presented to

for successfully completing the Data Science with Python training program, demonstrating dedication and proficiency in data analysis, visualization, and machine learning fundamentals using Python.

Duration: 4 Months

Program Director PROGRAM DIRECTOR
Authorized Signatory AUTHORIZED SIGNATORY

Certificate ID: FSPL-DSP-SAMPLE · Issued on completion

Sample only. Your name, dates, and certificate ID appear after you complete Data Science with Python.
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