Data Management

// program overview

Data Management Training Program

Program Objective

Equip professionals with comprehensive skills across the data lifecycle—from engineering pipelines to data science modeling, and analytics dashboards—aligned with enterprise-grade tools and cloud platforms.

Training Tracks Overview

Data Engineering - Data ingestion, ETL, pipelines, warehousing targeting Data Engineers and Platform Engineers.

Data Science & AI/ML - Predictive modeling, machine learning, AI workflows for Data Scientists, ML Engineers, and Analysts.

Data Analytics & Viz - Business intelligence, reporting, data storytelling for Data Analysts, BI Developers, and Stakeholders.

Our comprehensive Data Management training program is designed to transform professionals into data experts across the entire data lifecycle. Through hands-on labs, real-world projects, and industry best practices, participants gain practical experience with leading data platforms and tools. The program combines theoretical knowledge with practical application, ensuring graduates are ready to tackle complex data challenges in modern enterprises.

  • Data Engineering
  • Data Science & AI/ML
  • Data Analytics & Viz
  • Capstone Projects
  • Tools & Platforms
  • Training Modes
  • Certification & KPIs

Track 1: Data Engineering Services

Module 1: Foundations of Data Engineering

  • Data lifecycle: ingestion → processing → storage
  • Structured vs unstructured data
  • Batch vs real-time processing

Module 2: ETL & ELT Pipelines

  • ETL tools: Apache NiFi, Talend, Informatica
  • ELT with cloud-native tools (AWS Glue, Azure Data Factory)
  • Hands-on: Build a pipeline from source to data lake

Module 3: Big Data Ecosystems

  • Hadoop, Spark, Hive overview
  • Kafka for real-time streaming
  • Data partitioning, parallel processing concepts

Module 4: Cloud Data Engineering

  • AWS (Glue, Redshift, S3, Lambda)
  • Azure (Data Factory, Synapse, Blob Storage)
  • GCP (BigQuery, Dataflow, Pub/Sub)

Module 5: Data Warehousing

  • Snowflake, BigQuery, Redshift, Synapse
  • Schema design (Star, Snowflake)
  • Time-travel, data sharing, and cost optimization

Module 6: Data Governance & Quality

  • Metadata management, data catalog (e.g., Collibra, Alation)
  • Data lineage, versioning, and data quality checks
  • Hands-on: Data quality scorecard

Track 2: Data Science & AI/ML

Module 1: Data Science Foundations

  • Statistics & probability refresher
  • Data wrangling with pandas, numpy
  • Exploratory Data Analysis (EDA) with Jupyter

Module 2: Machine Learning Algorithms

  • Supervised, Unsupervised, Reinforcement Learning
  • Algorithms: Linear Regression, SVM, Random Forest, XGBoost
  • Overfitting, bias-variance tradeoff

Module 3: AI & Deep Learning

  • Neural networks and backpropagation
  • CNNs for image, RNNs for sequence data
  • Frameworks: TensorFlow, PyTorch

Module 4: ML Engineering & MLOps

  • Model lifecycle (training → deployment → monitoring)
  • ML pipelines: MLflow, Kubeflow
  • CI/CD for ML models

Module 5: NLP & Generative AI (Optional Advanced)

  • Text preprocessing, sentiment analysis
  • Transformers and LLMs (e.g., BERT, GPT)
  • Prompt engineering basics

Track 3: Data Analytics & Visualization

Module 1: Fundamentals of Data Analysis

  • Data types and descriptive statistics
  • Data cleansing and transformation
  • SQL for data manipulation (Window functions, joins, CTEs)

Module 2: Business Intelligence Tools

  • Power BI: DAX, visualizations, slicers
  • Tableau: Data prep, calculated fields, dashboards
  • Looker / Google Data Studio

Module 3: Dashboard & Storytelling Techniques

  • KPI design and metric definitions
  • Interactive visualizations & UX principles
  • Telling a story with data: executive-ready dashboards

Module 4: Advanced Analytics

  • Predictive analytics in BI tools
  • Embedded analytics
  • Real-time dashboards (Kafka + Streamlit + Redis)

Capstone Projects by Track

Data Engineering

  • Build an ETL pipeline to load data into Snowflake

Data Science & AI/ML

  • Predict customer churn using Python & scikit-learn

Data Analytics & Viz

  • Executive dashboard using Power BI for sales insights

Tools Covered

Languages

  • Python, SQL, Java, Scala

Platforms

  • AWS, Azure, GCP

BI Tools

  • Power BI, Tableau, Looker

ML Frameworks

  • scikit-learn, TensorFlow, PyTorch

Orchestration

  • Apache Airflow, dbt

Data Storage

  • Snowflake, BigQuery, S3, Lakehouse

ETL/ELT Tools

  • Apache NiFi, Talend, Informatica
  • AWS Glue, Azure Data Factory

Big Data

  • Hadoop, Spark, Kafka, Hive

Training Modes

Mode Best For
Instructor-Led Onboarding, guided bootcamps
Self-Paced LMS Scalable team upskilling
Hands-on Labs Skill application
Microlearning Concept reinforcement
Blended Learning Deep learning + convenience

Certification & Evaluation

Assessment Methods

  • Pre-assessments and post-assessments
  • Hands-on project submissions
  • Quizzes per module
  • Internal certification badges

External Certification Alignment

  • Microsoft DP-203 (Data Engineering)
  • AWS Certified Data Analytics – Specialty
  • TensorFlow Developer Certificate

Add-on Services

  • Curriculum localization and industry tailoring
  • Integration with company LMS (Moodle, Cornerstone, etc.)
  • Train-the-Trainer packages
  • Analytics to track learner engagement and outcomes

KPIs for Success

  • Learner progress and completion rates
  • Hands-on lab scores and project pass rates
  • Model deployment success (for DS/ML)
  • Dashboard adoption and reuse (for Analytics)
  • Pipeline reliability and performance (for Data Engineering)
// Key Benefits

Data Management Training Benefits

End-to-End Data Skills

Master the complete data lifecycle from engineering to analytics

AI & ML Expertise

Build advanced machine learning and AI capabilities

Data Visualization

Create compelling dashboards and data storytelling

Cloud-Native Tools

Work with enterprise-grade cloud platforms and tools

Pipeline Engineering

Build robust, scalable data pipelines and workflows

// Success Story

Enterprise Data Lake & Analytics Platform

Challenge

A financial services company needed to build a comprehensive data lake and analytics platform to unify data from multiple sources and enable real-time insights.

Solution

Comprehensive data management training covering AWS data services, Spark processing, machine learning pipelines, and Power BI dashboards.

Outcome

Successfully implemented data lake processing 10TB+ daily, reduced reporting time by 80%, and enabled predictive analytics for risk management.