Architect Evidence-Based Decisions. The Best Applied Data Science Course in Ahmedabad.

Banks, retailers, and product teams hire analysts who ship SQL queries, clean datasets in pandas, train scikit-learn models, and explain results on Power BI dashboards — not just theory slides. We train you on the applied stack Indian employers list for data analyst, business analyst, and junior data scientist roles.

Enterprise Enrollment Architecture
Duration: 60 Hours
₹25,500₹18,500
Note: Any type of tax would be extra. Global certification cost is excluded.

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Exclusive Program Benefits

  • After the course, Bascom Bridge will share 10–12 sample CVs to help build your resume.
  • Students receive a license for Bascom Bridge’s placement mobile app*.
  • Lifetime access* to the enrolled course for students.
  • If a student does not clear interviews, Bascom Bridge will provide retraining* until employment is secured.
  • Global certification training is included at no extra cost.
  • Discount on global certification fees* available.
  • Hands-on Jupyter labs with real datasets from retail, banking, and operations domains.
  • End-to-end capstone: ML model plus executive dashboard reviewed before placement season*.
*T&C apply. Valid per strict institutional guidelines upon active enrollment.
Applicable taxes will be added to each instalment.

Trusted by Government of India & Leading PSUs

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About Applied Data Science

Enterprise Tools Included

  • /Python 3.12+ with JupyterLab & Anaconda
  • /NumPy, Pandas, Matplotlib & Seaborn
  • /Scikit-learn for supervised & unsupervised ML
  • /SQL — PostgreSQL / MySQL for analytics queries
  • /Power BI for business intelligence storytelling

Cognitive Prerequisites

  • /Graduate in engineering, commerce, or science (statistics exposure helpful)
  • /Core Python or equivalent programming fundamentals
  • /Comfort with Excel pivot tables and basic charts
  • /Curiosity for data-driven decisions in business contexts

Salary Progression (₹)

  • Entry-Level (0-3 yrs)₹4.5L - ₹9.0L
  • Mid-Level (4-7 yrs)₹10.0L - ₹18.0L
  • Senior Level (8-12+ yrs)₹19.0L - ₹32.0L+
*Data benchmarked directly from AmbitionBox / Glassdoor India 2026 enterprise tech verified salaries.

The Enterprise Capstone Architecture

Customer Churn Intelligence for a Digital Services Provider. You will ingest transactional and support-ticket data via SQL, engineer features in pandas, train and tune a classification model in scikit-learn with proper validation, document precision/recall trade-offs, and publish a Power BI executive dashboard with segment-level churn risk — the portfolio artifact analytics hiring managers expect from applied data science graduates.

Deep-Dive Syllabus Grid

Module 1: Data Science Landscape & Analytics Mindset
How applied data science differs from pure research: CRISP-DM, stakeholder questions, KPIs, and ethical use of data in Indian enterprises. Setting up JupyterLab, project folders, and reproducible notebooks employers can audit in interviews.
Module 2: Python for Data — NumPy & Pandas Foundations
Array operations with NumPy, Series and DataFrames, indexing, joins, groupby, and handling missing values. Cleaning messy CSV exports from ERP, CRM, and payment gateways — the daily work of analytics teams in Ahmedabad IT services firms.
Module 3: Exploratory Data Analysis & Visualization
Univariate and bivariate analysis, correlation, outlier detection, and storytelling with Matplotlib and Seaborn. Building EDA notebooks that answer “what happened?” before any model is trained.
Module 4: Statistics for Applied Machine Learning
Descriptive stats, probability intuition, hypothesis testing, confidence intervals, and A/B test framing. Choosing the right metric when product managers ask whether a change “actually worked.”
Module 5: SQL for Data Extraction & Feature Engineering
SELECT, JOIN, GROUP BY, window functions, and subqueries on PostgreSQL/MySQL. Pulling features from warehouse tables the way analytics engineers support data science pipelines.
Module 6: Supervised Learning with Scikit-learn
Train/test splits, cross-validation, linear and logistic regression, decision trees, random forests, and gradient boosting basics. Interpreting feature importance and communicating model limits to non-technical stakeholders.
Module 7: Unsupervised Learning & Segmentation
K-means clustering, hierarchical clustering, PCA for dimensionality reduction, and customer segmentation use cases. Applications in marketing, credit risk buckets, and inventory grouping.
Module 8: Model Evaluation, Tuning & Imbalanced Data
Confusion matrix, precision/recall, ROC-AUC, F1, class weights, and basic hyperparameter tuning with GridSearchCV. Handling churn and fraud datasets where positives are rare — common in Indian fintech and telecom data.
Module 9: Business Intelligence with Power BI
Connecting to data sources, DAX essentials, interactive dashboards, and publishing insights for leadership reviews. Bridging notebook outputs to the dashboards operations teams use every Monday morning.
Module 10: Feature Pipelines, Documentation & MLOps Awareness
Sklearn Pipelines, joblib model persistence, README and model cards, version control for notebooks, and introduction to deployment patterns (batch scoring, API handoff). What hiring panels expect beyond a Kaggle notebook.
Module 11: Capstone Delivery & Interview Readiness
Presenting the end-to-end project: problem statement, data lineage, model choice, business impact estimate, and dashboard walkthrough. Mock technical interviews covering SQL, Python, and ML case studies used by analytics employers.

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Adani Logo
Arvind Logo
Asia MotorWorks Logo
Bharti Airtel Logo
Blue Star Logo
Charotar Institute Of Technology Changa Charusat
Cred Logo
Crest Data System Logo
Dalmia Group
Dhirubhai Ambani Institute Of Information And Comm
Einfochips Logo
Indus University
Jpmorgan Logo
JSW Group Logo
Larsen&Toubro Logo
MAXXIS Logo
McDonald'S Logo
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Nokia Logo
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