Training on Data Science and Machine Learning

This course provides a structured and practical foundation in Data Science and Machine Learning, equipping professionals with the skills to transform data into actionable insights. It covers the end-to-end data science lifecycle—from data preparation and exploration to predictive modeling and deployment—while emphasizing business-driven decision-making and responsible use of data. The course balances conceptual understanding with hands-on application to support real-world analytical challenges across industries.

Course Duration

5 Days

Who Should Attend

• Data analysts and business intelligence professionals

• IT professionals transitioning into data science roles

• Engineers and statisticians working with data

• Managers and decision-makers seeking data-driven insights

• Researchers and technical consultants

Organisational Impact

• Enables data-driven decision-making by providing actionable insights from complex datasets.

• Supports predictive analytics, helping organizations forecast trends, risks, and opportunities.

• Enhances operational efficiency through automation of data analysis and model-driven processes.

• Strengthens competitive advantage by leveraging machine learning for innovation and optimization.

• Improves strategic planning by integrating advanced analytics into business workflows.

Personal Impact

• Equips participants with practical skills in data preprocessing, feature selection, model training, and deployment.

• Builds proficiency in machine learning tools and libraries such as Python, pandas, scikit-learn, and TensorFlow.

• Enhances career opportunities in data science, analytics, AI, and machine learning roles.

• Develops the ability to apply predictive models to real-world problems and business challenges.

• Increases confidence in working with large datasets and implementing advanced data science methodologies.

By the end of this course, participants will be able to:

• Understand the fundamental concepts of data science and machine learning.
• Learn how to preprocess and clean data for analysis.
• Develop skills to build, train, and evaluate machine learning models.
• Gain proficiency in using Python and its libraries for data science tasks.
• Apply machine learning algorithms to solve real-world problems.

Module 1: Introduction to Data Science and Python for Data Analysis

• Overview of data science concepts and business applications
• Python fundamentals for data analysis
• Working with Jupyter Notebooks
• Data manipulation using Pandas
• Data visualization with Matplotlib and Seaborn
• Case Study: Using data science to uncover performance trends in a business dataset
• Practical Exercise: Load, clean, and visualize a sample dataset using Python

Module 2: Data Preprocessing and Exploration

• Data cleaning and handling missing values
• Feature engineering and selection
• Data normalization and scaling techniques
• Exploratory Data Analysis (EDA)
• Handling categorical and time-series data
• Case Study: Preparing raw operational data for predictive modeling
• Practical Exercise: Perform EDA and feature preparation on a real-world dataset

Module 3: Supervised Learning – Regression and Classification

• Fundamentals of supervised learning
• Linear and logistic regression models
• Decision trees and random forests
• Support Vector Machines (SVM)
• Model evaluation metrics and validation
• Case Study: Predicting customer behavior using classification models
• Practical Exercise: Build and evaluate a regression and classification model

Module 4: Unsupervised Learning – Clustering and Dimensionality Reduction

• Principles of unsupervised learning
• K-Means and hierarchical clustering
• Principal Component Analysis (PCA)
• Anomaly detection techniques
• Applications in segmentation and pattern discovery
• Case Study: Customer segmentation using clustering techniques
• Practical Exercise: Apply clustering and PCA to identify hidden data patterns

Module 5: Deep Learning and Model Deployment

• Introduction to neural networks and deep learning concepts
• Using TensorFlow and Keras
• Building and training simple neural networks
• Overfitting, regularization, and hyperparameter tuning
• Model deployment using Flask and Docker
• Case Study: Deploying a machine learning model into a production environment
• Practical Exercise: Train a neural network and deploy a basic ML model