3 Months (Weekends Only)

Data Sciences (Big Data)

This comprehensive course provides in-depth knowledge of data science with a focus on big data technologies. Led by seasoned professional software engineers, participants will gain hands-on experience, theoretical knowledge, and practical skills required to become proficient data scientists capable of handling and analyzing large datasets. The course includes practical exercises, data analysis projects, and a Live Project throughout the training plan.
  •  Understanding data science and its applications
  •  Role of data scientists in the industry
  •  Setting up the data science environment
  •  Introduction to Python for data science
  •  Data collection and acquisition techniques
  •  Data cleaning and preprocessing
  •  Handling missing data and outliers
  •  Hands-on: Data cleaning and preprocessing with Python
  •  EDA techniques and visualization
  •  Statistical analysis and summary statistics
  •  Data visualization libraries (e.g., Matplotlib, Seaborn)
  •  Hands-on: Data visualization and EDA with Python
  •  Introduction to machine learning concepts
  •  Supervised and unsupervised learning
  •  Model evaluation and validation
  •  Hands-on: Building machine learning models with Python
  •  Introduction to big data and its challenges
  •  Hadoop and the Hadoop Distributed File System (HDFS)
  •  Apache Spark for big data processing
  •  Hands-on: Setting up Hadoop and Spark clusters
  •  Spark RDDs and DataFrames
  •  Spark transformations and actions
  •  Spark SQL for data querying
  •  Hands-on: Data processing with Spark

  •  Spark MLlib for machine learning
  •  Building and evaluating machine learning models in Spark
  •  Scalable machine learning with Spark
  •  Hands-on: Machine learning with Spark
  •  Introduction to NoSQL databases (e.g., MongoDB)
  •  Data storage in distributed systems
  •  Data warehousing and data lakes
  •  Hands-on: Working with NoSQL databases
  •  Introduction to deep learning
  •  Neural network architectures
  •  Deep learning frameworks (e.g., TensorFlow, Keras)
  •  Hands-on: Deep learning with Python
  •  Stream processing and real-time analytics
  •  Apache Kafka for data streaming
  •  Real-time analytics with Spark Streaming
  •  Hands-on: Real-time data processing with Kafka and Spark
  •  Natural Language Processing (NLP)
  •  Image and text analysis
  •  Time series analysis and forecasting
  •  Hands-on: Advanced data science projects
  •  Project kick-off and problem definition
  •  Data acquisition and preprocessing for the Live Project
  •  Building machine learning models on large datasets
  •  Real-time data processing and analytics
  •  Project presentation and demonstration
  •  Graduation and certificate distribution


  •  Weekly practical exercises and projects
  •  Final project evaluation and presentation


Upon successful completion of the course and the Live Project, participants will receive a certificate in “Data Sciences (Big Data)” from “Industry Professionals.”


This course outline offers a structured and hands-on approach to data science with a focus on big data technologies, guided by experienced industry professionals, and emphasizes practical skills through exercises, data analysis projects, and a Live Project throughout the three-month program. Students will gain both theoretical knowledge and practical experience in data sciences and big data analytics.

Mamoona Riaz

Lead, AI & ML Engineer