Data Science & Machine Learning Career Program
Learn how to turn real-world data into insights, predictive models and data-driven business decisions.
What you'll learn
Understand the data-science lifecycle and how to frame business questions.
Clean, explore and visualize datasets using Python.
Apply statistics and probability to practical data problems.
Build predictive models using machine-learning techniques.
Interpret model results and communicate insights to non-technical stakeholders.
Create dashboards and analytical reports.
Complete a portfolio-ready data-science project.
Career opportunities
This course includes
Live classes
Python and analytics labs
Statistics exercises
Data visualization
Machine-learning projects
Portfolio project
Career guidance
Certificate
Who it's for
- Students and graduates
- Analysts and reporting professionals
- Business professionals moving into data
- Aspiring data scientists
- Career switchers with basic quantitative interest
Curriculum
12 modules · Live classes · Hands-on practice · Practical assignments
- Data science lifecycle
- Business questions vs data questions
- Types of data
- Success metrics
Practical / Lab: Convert a business problem into measurable analytical questions.
- Python basics
- NumPy
- Pandas
- Data structures and transformations
Practical / Lab: Load, clean and transform a dataset.
- Missing data
- Duplicates
- Outliers
- Data types
- Feature preparation
Practical / Lab: Create a clean analysis-ready dataset.
- Descriptive statistics
- Distributions
- Correlation
- Segmentation
- Pattern discovery
Practical / Lab: Produce an exploratory analysis and explain key findings.
- Mean, median and variance
- Probability
- Sampling
- Confidence intervals
- Hypothesis testing
Practical / Lab: Interpret a statistical test in a business context.
- Chart selection
- Dashboard principles
- Narrative structure
- Avoiding misleading visuals
Practical / Lab: Build a decision-oriented dashboard.
- Relational data
- SELECT, filtering and joins
- Aggregations
- Subqueries
- Analytical queries
Practical / Lab: Answer business questions from a sample database.
- Regression
- Classification
- Feature selection
- Model evaluation
Practical / Lab: Build and evaluate a predictive model.
- Clustering
- Segmentation
- Dimensionality reduction
- Anomaly detection
Practical / Lab: Create and interpret customer or transaction segments.
- Feature importance
- Error analysis
- Trade-offs
- Communicating uncertainty
Practical / Lab: Turn model output into an actionable recommendation.
- Case-study writing
- Notebook organization
- Dashboard presentation
- Stakeholder communication
Practical / Lab: Package a project for a professional portfolio.
- Business problem
- Data preparation
- EDA
- Modeling
- Visualization
- Recommendations
Practical / Lab: Complete a full data-science case study from raw data to executive recommendation.
Requirements
- Basic computer skills are required.
- No prior data-science experience is required.
- Comfort with basic mathematics is helpful.
- Laptop and stable internet connection are required.
Program description
This program is designed around the actual workflow of a data professional: ask the right question, obtain and prepare data, analyze it, build a model when appropriate, validate the result and communicate what it means.
The program deliberately balances technical skills with analytical thinking. This matters because employer research continues to rank analytical thinking as a core skill while AI and big data remain among the fastest-growing skill areas.
Capstone / final project
Final project: analyze a realistic business dataset, uncover important patterns, build a predictive or segmentation model, create a clear visualization/dashboard and present recommendations to a non-technical stakeholder.
Recommended tools & platforms
Batches
New batches forming · limited seats
Mode
Live instructor-led training
Duration
12 weeks
24/7 support