Practical_data_analytics_and_AI_implementation_891x552

This course is available in 2025.

The exact date and time will be announced after registration. For more information please contact us at lift@rbs.lv or call us at 20318250

APPLY

Practical Data Analytics and AI Implementation for Beginners

Lecturer

Kārlis Zars

About the lecturer

Kārlis Zars is a passionate educator and technologist with a focus on practical tech education. He is the founder of App Pottery, an online program designed to teach coding and AI skills. Additionally, he is a course creator and instructor on Coursera, where he delivers engaging and practical technology courses. His work experience includes roles in robotic process automation and a strong dedication to empowering learners of all backgrounds.

Course Description

This course empowers you to harness the power of data and AI to drive business success. You’ll delve into data analytics techniques, master data visualization skills, and explore the potential of AI tools like ChatGPT. By the end of the course, you’ll be equipped to make data-driven decisions, optimize processes, and innovate your business.

Course Goals

Develop a strong foundation in data analysis techniques, including descriptive, predictive, and prescriptive analytics.
Learn how to leverage AI tools and techniques to automate tasks, improve decision-making, and gain a competitive edge.
Master data visualization techniques to create compelling and informative data visualizations.
Learn how to use data insights to drive strategic decision-making and improve business performance.
Gain practical experience in implementing data-driven solutions within organizations.

After the course participants will be able to:

Gain a solid foundation in key marketing analytics concepts and techniques.
Develop proficiency in using popular data analysis tools and software.
Learn how to leverage data to improve customer acquisition, retention, and satisfaction.
Acquire the ability to extract meaningful insights from data and translate them into actionable recommendations.
Learn how to use data to inform and optimize marketing strategies.

Course will be:

In Person

Prerequisites:

No prior specific experience or specific prerequisites are required to enroll in this course.

Teaching methods used:

Hands-on approach along with theoretical insights

Session 1: Foundations of Data Analytics (4h)

Introduction
– Welcome participants
– Overview of course objectives

Data Analytics Overview
– Types of Data Analytics:
– Descriptive Analytics
– Predictive Analytics
– Prescriptive Analytics
– Importance in Competitive Markets
– Benefits for SMEs (Small and Medium Enterprises) in Latvia
Group Discussion: Share experiences with data use in your company
Data Literacy
– Understanding Data Formats:
– Structured vs. Unstructured Data
– Data Sources:
– Internal (Sales, HR, Operations)
– External (Market Trends, Social Media)
– Ethical Considerations:
– GDPR Compliance
– Data Privacy Laws in Latvia
Individual Exercise: Identify data sources relevant to your role
Understanding Data Structures
– Basic Concepts:
– Databases and Spreadsheets
– Data Tables and Fields
– Data Quality Issues:
– Incomplete Data
– Inconsistent Data
– Importance of Clean Data
Interactive Quiz: Data quality scenarios and solutions
Session Wrap-Up
– Recap key learnings
– Preview of next session

Session 2: Essential Tools and Techniques (4h)

Introduction to Session 2
– Brief recap of Session 1
– Objectives for Session 2

Data Analysis Tools
– Introduction to User-Friendly Tools:
– Microsoft Excel
– Google Sheets
– Basic Data Manipulation:
– Sorting and Filtering
– Formulas and Functions
– Pivot Tables
Guided Exercise: Import and explore a sample dataset in Excel
Visualization Techniques
– Importance of Data Visualization
– Types of Charts and Graphs:
– Bar, Line, Pie Charts
– Scatter Plots, Histograms
– Best Practices:
– Choosing the Right Chart
– Visual Clarity and Design Principles
Hands-on Activity: Create charts using sample data
Presenting Data Effectively
– Storytelling with Data
– Tailoring Presentations to Your Audience
– Common Mistakes to Avoid
Group Exercise: Present findings to peers
Session Wrap-Up
– Recap key learnings
– Preview of next session

Session 3: Integrating AI into Data Processes (4h)

Introduction to Session 3
– Brief recap of Session 2
– Objectives for Session 3

AI Concepts Simplified
– What is Artificial Intelligence?
– AI in Everyday Life
– AI Trends Impacting SMEs
Interactive Discussion: Share experiences with AI
AI Tools for Everyday Use
– Accessible AI Tools:
– ChatGPT for data analysis
– Automated Reporting Tools
– Use Cases Relevant to SMEs:
– Customer Service Chatbots
– Sales Forecasting
– Inventory Management
Guided Demonstration: Use ChatGPT for data summarization
Implementing AI Solutions
– Criteria for Selecting AI Tools
– Integrating AI into Existing Processes
– Challenges and Considerations:
– Cost
– Training
– Data Security
Hands-on Activity: Identify an AI tool for a business problem
Session Wrap-Up
– Recap key learnings
– Preview of next session

Session 4: Real-world Applications and Planning (4h)

Introduction to Session 4
– Brief recap of Session 3
– Objectives for Session 4

Local AI solution
– How to run AI locally
– What are benefits of local LLMs
– Following the trends
Group Discussion
Developing a Roadmap
– Identifying Opportunities:
– Areas for Data Analytics Improvement
– Potential AI Applications
– Setting Realistic Goals:
– Short-term vs. Long-term Objectives
– Resource Assessment
Individual Exercise: Draft a data analytics and AI implementation plan
Creating an Action Plan
– Outline Key Steps:
– Data Collection Strategies
– Tool Selection
– Training Needs
– Overcoming Challenges:
– Budget Constraints
– Resistance to Change
– Measuring Success:
– Key Performance Indicators (KPIs)
– Continuous Improvement
Hands-on Activity: Finalize the implementation plan with peer feedback
Course Wrap-Up
– Review of key learnings
– Q&A Session
– Feedback Collection