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Ideal Workplace Solutions
NITA AccreditedIntermediatePhysical + Virtual10 daysTSARC

Training on Time Series Analysis with R

Master time series analysis with R. Learn to analyze time-dependent data, forecast future trends, and make data-driven decisions.

Next intake

20 Jul 2026 · Nakuru

View all dates

Duration

10 days

Live instruction

Delivery

Physical + Virtual

Cohort based

Level

Intermediate

Working professionals

Certification

NITA reimbursable

For Kenyan cohorts

Language

English

All materials

Overview

About this programme

This course is designed to equip participants with the knowledge and practical skills needed to perform time series analysis using R. The course covers fundamental concepts and advanced techniques for analyzing time-dependent data, focusing on how to model, forecast, and interpret results effectively. By using R, a powerful statistical programming language, participants will gain hands-on experience in handling various time series data and applying state-of-the-art methods to derive meaningful insights for decision-making processes.

Course Duration

10 Days

Who Should Attend

  • Statisticians and data scientists interested in time series analysis.
  • Analysts in finance, economics, and other fields dealing with temporal data.
  • Researchers and academics who need to analyze time-dependent data.
  • Professionals in industries such as finance, healthcare, and environmental science where time series data is critical.
  • Graduate students in statistics, economics, and related fields.
Learning outcomes

What you'll walk away with

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

  • Understand the fundamental concepts of time series analysis.
  • Preprocess and visualize time series data in R.
  • Identify and apply appropriate time series models for different types of data.
  • Perform forecasting using various time series models.
  • Analyze and interpret the results of time series models.
  • Diagnose model performance and implement model improvement techniques.
  • Handle seasonality and trend components in time series data.
  • Use R for automating time series analysis and forecasting.
  • Work with advanced time series models such as ARIMA, GARCH, and others.
  • Apply time series analysis techniques to real-world problems across different industries.
Course modules

What we cover, module by module

Module 1: Introduction to Databases

  • Database concepts and fundamentals
  • Database management systems (DBMS)
  • Types of databases (relational, hierarchical, network, object-oriented)
  • Database applications and importance
  • Case Study: Implementing a database system for a retail business
  • Practical: Identify database requirements and design a simple data structure

Module 2: Database Design Concepts

  • Data modeling and normalization
  • Entity-relationship (ER) modeling
  • Functional dependencies and normal forms (1NF, 2NF, 3NF)
  • Database schema design
  • Case Study: Designing a database for a school management system
  • Practical: Create an ER diagram and normalize a dataset

Module 3: SQL Fundamentals

  • Introduction to SQL
  • Data definition language (DDL)
  • Data manipulation language (DML)
  • Data query language (DQL)
  • Data control language (DCL)
  • Case Study: Managing and querying organizational data using SQL
  • Practical: Write basic SQL queries for creating and manipulating data

Module 4: Advanced SQL

  • Joins (inner, outer, self)
  • Subqueries
  • Views
  • Indexes
  • Stored procedures and functions
  • Case Study: Combining multiple tables to generate business reports
  • Practical: Write advanced SQL queries using joins and subqueries

Module 5: Database Normalization

  • In-depth exploration of normalization
  • Higher normal forms (4NF, BCNF)
  • Denormalization techniques
  • Case Study: Optimizing database structure to reduce redundancy
  • Practical: Normalize and denormalize a dataset

Module 6: Database Performance Tuning

  • Indexing techniques
  • Query optimization
  • Database performance monitoring and troubleshooting
  • Database partitioning
  • Case Study: Improving database performance in a high-transaction system
  • Practical: Analyze and optimize slow-running queries

Module 7: Database Security and Integrity

  • Database security threats and vulnerabilities
  • Access control and authentication
  • Data encryption and backup
  • Database recovery and disaster recovery
  • Case Study: Securing sensitive organizational data from breaches
  • Practical: Develop a database security and backup plan

Module 8: Database Administration

  • Database installation and configuration
  • User management and administration
  • Database backup and recovery
  • Performance monitoring and tuning
  • Case Study: Managing database operations in an enterprise environment
  • Practical: Configure a database and perform administrative tasks

Module 9: Database Application Development

  • Database connectivity and programming
  • Database integration with applications
  • Database-driven web applications
  • Case Study: Building a database-backed web application
  • Practical: Connect a database to an application and perform CRUD operations

Module 10: Emerging Database Technologies

  • NoSQL databases
  • Big data and Hadoop
  • Cloud-based databases
  • Database trends and future directions
  • Case Study: Adopting cloud-based and NoSQL solutions for scalability
  • Practical: Explore and compare different database technologies
Impact

Where the change lands

Organizational Impact

  • Enhances predictive capabilities and strategic decision-making.

  • Increases efficiency through automated forecasting and real-time insights.

  • Supports better resource allocation, profitability, and competitive advantage.

Personal Impact

  • Develops advanced data science and predictive analytics skills.

  • Prepares participants for senior analytical, research, or data science roles.

  • Empowers individuals to lead and implement data-driven, predictive solutions.

Dates and locations

Upcoming intakes

Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.

Full calendar
FAQs

Common questions.

Still not sure? Send us a note and a facilitator will get back to you within a business day.

The goal is to equip you with the skills to use R for the entire time series analysis workflow, from data preparation and visualization to forecasting and reporting.

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For corporate teams

Training 10+ professionals?

We deliver Training on Time Series Analysis with R in-house at your offices, at a venue we arrange, or fully virtual. Customise the curriculum against your KPIs, and get a bespoke price for the cohort size you need.

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