Introduction

In today's data-driven world, the ability to extract meaningful insights from vast amounts of data is a critical skill. Data Science and Big Data Analytics have become essential disciplines, empowering organizations to make informed decisions, optimize processes, and gain a competitive edge. This 5-day training program is designed to provide participants with a comprehensive introduction to the fundamental concepts and tools of Data Science and Big Data Analytics. Whether you're a professional looking to upskill or someone eager to explore the world of data, this program will equip you with the knowledge needed to navigate the data landscape effectively.

Objectives

The primary objectives of this 5-day training program are as follows:

• Introduction to Data Science: Provide participants with a solid understanding of the Data Science workflow and its significance in various industries.

• Data Analysis with Python: Familiarize participants with Python, a powerful programming language for data analysis, and equip them with the skills to clean, preprocess, and visualize data.

• Machine Learning Fundamentals: Introduce participants to the core concepts of machine learning, including supervised and unsupervised learning, model evaluation, and cross-validation.

• Advanced Machine Learning: Dive deeper into advanced machine learning topics such as clustering, dimensionality reduction, and ensemble learning.

• Big Data Technologies: Explore the world of Big Data, including Hadoop and Apache Spark, and learn how to process and analyze large datasets efficiently.

• Hands-on Experience: Although not explicitly included in this program outline, participants are encouraged to practice what they learn through hands-on exercises and independent projects outside of the training hours.

Target Audience

This training program is suitable for a diverse range of individuals, including:

• Professionals: Data analysts, business analysts, software developers, and IT professionals looking to transition into Data Science and Big Data roles or enhance their existing skills.

• Students: Undergraduates or postgraduates in computer science, data science, or related fields seeking a foundational understanding of Data Science and Big Data Analytics.

• Entrepreneurs and Managers: Business owners, entrepreneurs, and managers interested in leveraging data-driven insights to improve decision-making and business strategies.

• Anyone Curious About Data: Individuals with a general interest in data and analytics who want to explore the world of Data Science and Big Data Analytics.

Outline

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

• Overview of Data Science

• Data Science Workflow

• Introduction to Python for Data Analysis

• Data Cleaning and Preprocessing

• Basic Data Visualization

Day 2: Machine Learning Fundamentals

• Introduction to Machine Learning

• Supervised Learning: Linear Regression, Logistic Regression

• Supervised Learning: Decision Trees, Random Forests

• Model Evaluation and Cross-Validation

Day 3: Advanced Machine Learning and Big Data

• Unsupervised Learning: Clustering (K-Means, Hierarchical)

• Dimensionality Reduction (PCA)

• Introduction to Big Data and Hadoop

• Introduction to Apache Spark

Day 4: Big Data Analytics with Spark

• Spark Fundamentals and RDDs

• Spark DataFrames and Spark SQL

• Spark Machine Learning (MLlib)

• Data Analysis with Spark

Day 5: Conclusion and Recap

• Review of Key Concepts

• Discussion on Real-World Applications

• Q&A and Discussion with Participants

• Graduation and Certificate Distribution