Introduction to Data Science

Introduction:

Data science is a multidisciplinary field that combines statistical methods, computational techniques, and domain knowledge to extract valuable insights from data. As organizations increasingly rely on data to inform decision-making, understanding the fundamentals of data science has become essential for professionals across industries. This training program provides an overview of the core concepts, tools, and processes in data science. It introduces participants to the data science workflow, including data collection, cleaning, analysis, and visualization, to foster a foundational understanding of the field.

Program Objectives:

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

• Explore the principles and applications of data science.

• Recognize the steps involved in the data science workflow.

• Identify key tools and techniques used in data analysis.

• Comprehend the role of data visualization in presenting insights.

• Explore the ethical considerations in data science practices.

Target Audience:

• Professionals interested in leveraging data for decision-making.

• IT and analytics professionals exploring the field of data science.

Program Outline:

Unit 1:

Introduction to Data Science:

• Definition and scope of data science.

• Differences between data science, analytics, and AI.

• Area of applications of data science across industries.

• Key skills and roles in data science.

• The importance of data in modern decision-making.

Unit 2:

Data Collection and Preparation:

• Types of data: structured and unstructured.

• Sources of data and methods of collection.

• Techniques for cleaning and preprocessing data.

• Handling missing, inconsistent, or noisy data.

• Data storage and management principles.

Unit 3:

Exploratory Data Analysis (EDA):

• Objectives and importance of EDA in data science.

• Descriptive statistics and summary measures.

• Identifying patterns, trends, and relationships in data.

• Tools for visualizing and summarizing data.

• Common challenges in EDA and how to address them.

Unit 4:

Tools and Techniques in Data Science:

• Overview of programming languages used in data science.

• Introduction to libraries for data manipulation and analysis.

• Basics of machine learning algorithms and models.

• The role of statistical techniques in data science.

• Cloud-based platforms for data science projects.

Unit 5:

Data Visualization and Communication:

• Principles of effective data visualization.

• Tools for creating visualizations.

• Storytelling with data: presenting insights to stakeholders.

• Ethical considerations in data representation.