Data Science & Analytics
Overview:
Data is the new oil — and the ability to extract insights from it is one of the most sought-after skills in today’s digital economy. This comprehensive Data Science & Analytics program helps you master the full data lifecycle — from data collection and wrangling to advanced analytics, machine learning, and AI-driven decision-making. Through hands-on projects, real datasets, and expert mentorship, you’ll gain the analytical mindset and technical skills to turn raw data into actionable insights that drive business success.
Training Duration & Format
Total Duration: ~180 Hours (approx. 6 months)
Mode: Online / Classroom / Hybrid
Structure: Instructor-led sessions + hands-on labs + weekly assignments + final project
Training Highlights:
* Industry-aligned curriculum with practical, project-based learning
* Trainers from IT & analytics background with real-world experience
* Hands-on exposure to Python, SQL, Power BI, and AI frameworks
* Live projects and domain-specific case studies (finance, healthcare, retail, marketing)
* Job-readiness focus — resume building, interview preparation, and portfolio project review
Course Structure
Module 1: Foundation of Data Science
Understanding Data Science – Concepts, Roles, and Career Paths Basics of Data, Analytics, and AI Ecosystem Understanding structured & unstructured data Introduction to databases, data warehouses, and data pipelines Overview of Machine Learning, Deep Learning, and Data Engineering
Module 2: Programming for Data Science (Python)
Python fundamentals: syntax, loops, conditionals, functions Working with libraries: NumPy, Pandas, Matplotlib, Seaborn Data cleaning, missing value handling, and transformations Exploratory Data Analysis (EDA) Writing reusable, efficient, and optimized code for analytics
Module 3: Statistics and Probability for Data Science
Descriptive statistics and data distributions Measures of central tendency and dispersion Probability theory and hypothesis testing Correlation, covariance, regression concepts Real-world application of inferential statistics in analytics
Module 4: Database Management and SQL
Understanding relational databases Writing SQL queries: SELECT, WHERE, JOIN, GROUP BY, HAVING Aggregations, subqueries, and nested queries Integrating SQL with Python for analytics Basic introduction to NoSQL (MongoDB overview)
Module 5: Data Visualization and Reporting
Importance of visualization in analytics Tools: Power BI, Tableau, and Python visualization libraries Designing interactive dashboards and KPI reports Visual storytelling for decision-making Connecting live data sources and automating reports
Module 6: Machine Learning & Predictive Analytics
Introduction to Machine Learning concepts Types of ML: Supervised, Unsupervised, Reinforcement Algorithms: Linear & Logistic Regression, Decision Trees, Random Forest, SVM Model evaluation metrics – accuracy, precision, recall, F1-score Feature engineering and model optimization techniques Hands-on ML model development with Scikit-learn
Certification
Upon successful completion, participants receive the Certified Data Science & Analytics Professional credential — validating your expertise to employers and clients.