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9 Months

Data Science & Strategic Analytics

From Raw Data to Business Intelligence

Transform into the data partner every leadership team craves—tell compelling stories with data, automate insight pipelines, and launch ML-powered decisions.

Command Python, SQL, and data tooling to cleanse, enrich, and activate information fast.

Explain statistical evidence with clarity so stakeholders trust every recommendation.

Ship dashboards and predictive models that move business metrics and answer “what happens next?”.

11 Modules
Live Mentorship
Real Projects
Industry Ready

Program Details

Duration
9 Months
Modules
11 Modules
Mentorship
Included
Real-world Projects
Yes
Apply Now →Download Brochure

Program Roadmap

1
Module 1

Python for Data Science

Master Python basics for data analysis.

  • ●Python Syntax and Core Concepts
  • ●Data Structures (List, Tuple, Set, Dictionary)
  • ●Control Flow and Functions
  • ●NumPy for Numerical Computing
  • ●Pandas for Data Manipulation
2
Module 2

Database Management & SQL

Learners can extract and manipulate data using SQL.

  • ●DBMS Intro, Relational databases, ER diagrams
  • ●SQL (SELECT, Joins, Subqueries, GROUP BY)
  • ●Constraints & Indexes
  • ●Intro to NoSQL (MongoDB basics)
3
Module 3

Mathematics for Data Science

Learners understand math behind models and metrics.

  • ●Linear Algebra (Vectors & matrices)
  • ●Calculus (Derivatives, Gradient)
  • ●Distance measures
4
Module 4

Statistics & Probability

Learners can validate insights statistically.

  • ●Descriptive statistics & Probability theory
  • ●Random variables & Distributions
  • ●Bayes’ theorem & Sampling techniques
  • ●Hypothesis testing (Z-test, T-test, Chi-square)
  • ●A/B testing
5
Module 5

Exploratory Data Analysis (EDA)

Master data exploration techniques.

  • ●Data cleaning strategies
  • ●Handling missing values
  • ●Outlier detection
  • ●Correlation analysis & Pattern identification
6
Module 6

Data Visualization & BI Tools

Learners can present business insights clearly.

  • ●Storytelling with data
  • ●Dashboards & reports
  • ●Power BI / Tableau
  • ●KPI & metric design
7
Module 7

Machine Learning Fundamentals

Build foundational ML models.

  • ●ML workflow & Preprocessing
  • ●Feature engineering
  • ●Linear Regression & Logistic Regression
  • ●KNN & Naive Bayes
  • ●Model evaluation metrics
8
Module 8

Advanced ML Models

End-to-end ML predictive model project.

  • ●Decision Trees & Random Forest
  • ●Gradient Boosting & XGBoost
  • ●Model tuning (Grid Search, Cross-validation)
9
Module 9

Big Data Analytics

Handle large-scale datasets.

  • ●Big Data concepts (5Vs)
  • ●Hadoop ecosystem
  • ●Apache Spark basics (DataFrames, SQL)
10
Module 10

Cloud & MLOps for Data Science

Deploy and manage ML models.

  • ●AWS basics (S3, EC2)
  • ●Docker basics
  • ●ML lifecycle & Version control (Git)
  • ●Intro to MLflow & DVC
11
Module 11

Case Studies & Capstone

Solve real-world business problems.

  • ●Customer churn analysis
  • ●Sales forecasting
  • ●Fraud detection
  • ●Capstone: End-to-end project (Business problem -> ML model -> Dashboard)
The Complete Journey

From Learning to Production

1 Month Placement Ready Program

ATS-Friendly Resume Building

Resume optimized for MNCs & startups, Keyword-based, HR-approved formats

Mock Interviews (Technical + HR)

Real interview environment, Personalized feedback & improvement plan

Industry Expert Mentorship

Guidance from professionals working in the same field, Insights on hiring trends & expectations

Portfolio Building

Project-based portfolio, GitHub + live project review

LinkedIn Profile Optimization

Recruiter-friendly headline & summary, Job-search & networking strategy

Placement Readiness Support

Interview question bank, Salary negotiation tips, Confidence & communication grooming