TTDS6000: Data Science Overview | Technologies, Tools & Modern Roles in the Data-Driven Enterprise

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About this Course

Delve into the world of data science and big data with our engaging one-day course, “Data Science & Big Data Overview | Tools, Tech & Modern Roles in the Data-Driven Enterprise.” Guided by our expert instructor, you’ll explore how data science is revolutionizing business, connecting the dots between data and strategic decision-making. Gain key insights into transforming raw data into meaningful insights through data collection, preparation, and visualization techniques. Learn about the essential elements of data science, the dynamics of big data technologies, and the evolving roles that are shaping data-driven organizations today. You’ll also review the foundations of machine learning, big data technologies, and the practical application of data science tools, preparing you to tackle real-world business challenges effectively, while exploring the latest trends in AI and its application in data science for maximum productivity.

This course serves as an ideal primer, offering a valuable kickstart for individuals new to data science, whether they aim to apply foundational concepts in their current role, engage in informed discussions, or pursue advanced training to launch their data science career. You’ll exit the class with a solid understanding of these technologies to a conversant level, able to participate in strategic decisions surrounding data science skills. A highlight of the course will be our demo of a custom Retrieval Augmented Generation (RAG) based GPT solution, demonstrating how advanced AI techniques align with various data-oriented business roles and processes, providing insights into the latest technologies driving modern data strategies.

You’ll exit this course with the skills required to effectively communicate data-driven insights, contribute to strategic business decisions, and stay ahead in the evolving landscape of data science and big data technologies.

Audience Profile

This introductory-level course is geared for tech professionals who wish to integrate data-driven strategies into their business operations. Ideal roles include business analysts, project managers, and department heads who aim to understand the impact of data science in decision-making and strategy formulation. The course is also suited to IT professionals and aspiring data analysts looking to familiarize themselves with the foundational concepts and tools of data science, setting the stage for further specialized training.

At Course Completion

This course combines engaging instructor-led presentations and useful demonstrations and group discussions. Throughout the course you will explore:

The essentials of data collection and cleansing, recognizing their role as the foundation for insightful analytics.
The dynamics of data visualization, and how well-crafted visuals aid in decision-making and idea communication.
A high-level overview of machine learning, exploring its ability to identify patterns and forecast trends for informed business strategies.
The fundamentals of big data technologies, understanding their capacity to manage and analyze extensive information for business leverage.
The significance of data-driven insights in steering strategic, informed actions and nurturing a data-aware business culture.
The framework of data teams, appreciating the collaborative synergy among data scientists, analysts, and engineers in driving data initiatives.
Key programming languages and tools used in data science, acknowledging their role in effective project execution and team communication.

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals.

Students will explore:

· The Hadoop Ecosystem: HDFS; Resource Navigators, MapReduce, Spark, Distributions

· Big Data, NOSQL, and ETL

· ETL: Exchange, Transform, Load

· Handling Data & a Survey of Useful tools

· Enterprise Integration Patterns and Message Busses

· Developing in Hadoop Ecosystem: R, Python, Java, Scala, Pig, and BPMN

· Artificial Intelligence and Business Systems

· Who’s on the Team? Evolving Roles and Functions in Data Science

· Growing your Infrastructure

Outline

Please note that we will work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation.

1.Understanding Data Science in Business

Overview of Data Science and its Evolution
Importance of Data Science in Business
Key Terminologies in Data Science
Data Science vs. Traditional Business Intelligence
Demo

2. Data Collection and Preparation

Learn about the sources of data and methods to collect and prepare data for analysis.
Sources of Data in Business
Data Collection Techniques
Data Cleaning and Pre-processing
Demo

3. Introduction to Analytics and Statistical Methods

Explore basics of analytics and statistical methods used in data science.
Descriptive vs. Inferential Statistics
Common Statistical Methods and Their Business Applications
Basics of Data Visualization
Demo

4. Introduction to Machine Learning for Business

Understand the fundamentals of machine learning and its application in business decisions.
Overview of Machine Learning
Supervised vs. Unsupervised Learning
Common Machine Learning Algorithms in Business
Demo

5. Data Visualization and Interpretation

Learn how to visualize and interpret data to derive actionable business insights.
Importance of Data Visualization
Common Data Visualization Tools (Tableau, PowerBI, Qlik, Looker etc.)
Demo

6. Big Data, Hadoop, and Business Decisions

Understand the concept of big data, the Hadoop ecosystem, and how it can be leveraged for strategic business decisions.
Introduction to Big Data
Overview of the Hadoop Ecosystem and its Components
Big Data Technologies and Tools (Hadoop, Spark, Hive, Cassandra, etc.)
Integrating Big Data into Business Strategy
Big Data for Predictive Analysis
Challenges and Ethical Considerations in Big Data
Demo

7. Common Tools, Languages & AI in Data Science

Gain insights into the common tools and languages used in data science, the role of AI, and modern data science roles.
Common Data Science Tools and Languages (Python, R, SQL, and Java)
Introduction to AI in Data Science
New AI Tools in Data Science
Modern Roles in Data Science (Data Analyst, Data Engineer, Data Scientist)
Demo

8. Implementing Data Science in Business

Gain insights on how to successfully implement data science projects in a business environment.
Building a Data-Driven Culture
Roles and Responsibilities in a Data Science Team
Project Management for Data Science

9. Data Science in Action

Q&A session & Recap
Group discussion on the application of data science in participants' respective businesses
Additional resources and guides for further self-paced learning

Prerequisites

Basic Understanding of Data Concepts: Participants should have a basic understanding of data and its importance in business contexts, including familiarity with common data types and simple data manipulation.
Fundamental Analytical Skills: A basic proficiency in analytical thinking and problem-solving, enabling participants to follow along with data analysis concepts and techniques presented during the course.
Familiarity with Business Operations: An understanding of general business operations and processes, as this will help in relating data science concepts to practical business applications and decision-making scenarios.

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