Data Science

Professional Training Syllabus

  • Total Duration: 150 Hours

  • Training Format: Theory + Practical + Projects

  • Learning Level: Beginner to Advanced

  • Mode: Classroom / Online (Instructor-Led)

  • Tools & Technologies: Python, SQL, Excel, Power BI, Machine Learning Libraries

 

Program Overview

This Data Science training programme is designed to provide practical and industry-relevant knowledge required to work with data effectively. The syllabus covers data analysis, statistics, Python programming, machine learning, and real-world project implementation, ensuring learners gain both conceptual understanding and hands-on experience.

Course Objective

The goal of this Data Science course is to help learners understand how data is collected, analyzed, and used to solve real business problems. The course is designed to build strong practical skills in data handling, statistics, Python programming, and machine learning, even for those with no prior technical background.

This training focuses on working with real-world data, creating useful insights, and understanding patterns that support better decision-making. Learners will gain hands-on experience with industry-relevant tools and learn how data science is applied in fields such as business, finance, healthcare, and technology.

By the end of the course, students will be confident in analyzing data, building basic predictive models, and explaining data findings clearly, preparing them for entry-level and professional roles in the data science field.

Course Overview

Data Science is the practice of using data to understand problems, discover patterns, and support smarter decisions. This course provides a practical introduction to how data is collected, analyzed, and transformed into useful insights using modern tools and techniques.

The program covers the complete data science journey, starting from data understanding and cleaning, moving to analysis and visualization, and finally introducing machine learning concepts. Learners work with real-world datasets to understand how data science is applied in business, finance, healthcare, marketing, and technology sectors.

This training focuses on hands-on learning, clear explanations, and real industry scenarios. By combining Python programming, statistics, data analysis, and machine learning basics, the course helps learners build strong foundational skills and practical confidence required for professional data science roles.

What You Learn

  • Understand the basics of data science and how it is used in real industries

  • Work with different types of data and prepare data for analysis

  • Use Python to clean, organize, and analyze datasets

  • Apply statistics and probability to understand data patterns

  • Perform exploratory data analysis to find meaningful insights

  • Create visual reports and dashboards to present data clearly

  • Learn the fundamentals of machine learning and predictive models

  • Evaluate model performance and improve accuracy

  • Work on real-world projects using industry-relevant datasets

  • Communicate data insights in a clear and professional way

Learning Outcomes

After completing this Data Science training, learners will be able to:

  • Understand the complete data science workflow from raw data to insights

  • Clean, organize, and analyze real-world datasets using Python

  • Apply statistical concepts to interpret data accurately

  • Perform exploratory data analysis to identify trends and patterns

  • Build basic machine learning models for prediction and classification

  • Evaluate model performance and improve results using standard techniques

  • Create meaningful data visualizations and reports for business use

  • Translate data findings into clear, actionable insights

  • Work confidently on entry-level and professional data science tasks

  • Demonstrate practical skills through projects and case studies

MAHIRA EDGE Advantage

Mahira Edge offers a learning experience that is focused on real skills, practical exposure, and career readiness. Our training programs are designed to match current industry needs, helping learners gain knowledge that can be applied directly in real work environments.

Courses are delivered by experienced trainers who explain concepts in a simple and structured way. Learners receive hands-on practice with real datasets, live examples, and project-based learning to build confidence and practical understanding.

At Mahira Edge, we go beyond classroom training by providing continuous learning support, career guidance, and interview preparation. Our focus is on helping learners develop strong technical skills, problem-solving ability, and professional confidence required to succeed in today’s competitive job market.

Detailed Syllabus – Data Science

Module 1: Introduction to Data Science

  • Understanding data science and its applications

  • Role of a data scientist in business

  • Types of data and data sources

  • Data science lifecycle and workflow

Module 2: Python Programming for Data Science

  • Python fundamentals and syntax

  • Working with NumPy and Pandas

  • Data cleaning and data manipulation

  • Handling real-world datasets

Module 3: Statistics & Probability

  • Descriptive and inferential statistics

  • Mean, median, variance, and standard deviation

  • Probability concepts

  • Hypothesis testing and data interpretation

Module 4: Data Analysis & Exploration

  • Exploratory Data Analysis (EDA)

  • Data visualization techniques

  • Identifying trends and patterns

  • Business insight generation

Module 5: Data Visualization Tools

  • Charts and graphs for analysis

  • Visualization using Python libraries

  • Introduction to Power BI dashboards

  • Storytelling with data

Module 6: Machine Learning Fundamentals

  • Introduction to machine learning concepts

  • Supervised and unsupervised learning

  • Regression and classification models

  • Clustering techniques

Module 7: Model Evaluation & Improvement

  • Training and testing datasets

  • Performance metrics

  • Overfitting and underfitting

  • Model optimization basics

Module 8: Real-World Projects & Case Studies
  • End-to-end data science project
  • Industry-based case studies
  • Data interpretation and reporting
  • Presentation of insights
Module 9: Career & Interview Preparation

  • Data science interview concepts

  • Resume building and project explanation

  • Mock interviews

  • Career guidance and role mapping