90-Day Training Curriculum Design Plan
Prepared by:
[Your Name]
[Your Company Name]
Introduction
This 90-day Training Curriculum Design Plan, prepared by [YOUR COMPANY NAME], aims to provide a structured, hands-on learning experience for participants in the field of Data Analytics. The curriculum is designed to develop foundational, intermediate, and advanced data analysis skills through a series of modules and projects. By the end of the 90 days, participants will be equipped to apply data analytics techniques in real-world business scenarios.
Objectives
To provide participants with a foundational understanding of data analytics tools and concepts.
To develop skills in data visualization, statistical analysis, and predictive modeling.
To ensure participants can apply their learning in practical, business-driven projects.
To assess progress through regular feedback, quizzes, and final evaluations.
Target Audience
New Employees in data-related roles (e.g., data analysts, business analysts).
Current Employees looking to improve their data analytics skills for career development.
Professionals aiming to transition into data-driven roles.
Structure of the Plan
Phase 1: Foundation (Days 1–30)
Goals:
Build foundational knowledge in data analytics tools and concepts.
Introduce participants to key tools such as Excel, SQL, and Python.
Training Modules:
Module | Topics Covered |
|---|
Module 1: Introduction to Data Analytics | Overview of data analytics and its importance. Understanding data types and sources. The data analytics lifecycle.
|
Module 2: Data Collection and Management | Introduction to databases. Data cleaning and preparation techniques. Using SQL for data queries.
|
Activities:
Activity | Details |
|---|
Hands-on Exercises | Practice using Excel and SQL for data cleaning and basic analysis. |
Group Discussion | Analyze a sample data set and discuss real-world data challenges. |
Evaluation:
Evaluation Method | Details |
|---|
Quizzes | Assess understanding of data types, tools, and data collection methods. |
Trainer Feedback | Feedback is provided after each exercise for improvement. |
Phase 2: Skill Development (Days 31–60)
Goals:
Strengthen skills in data visualization and statistical analysis.
Introduce intermediate tools like Tableau, Power BI, and Python for data analysis.
Training Modules:
Module | Topics Covered |
|---|
Module 3: Data Visualization Techniques | Introduction to visualization tools (Tableau, Power BI). Best practices for creating impactful data visualizations.
|
Module 4: Statistical Analysis | Introduction to statistics for data analysis. Hypothesis testing, regression, and basic predictive modeling in Python.
|
Activities:
Activity | Details |
|---|
Visualization Project | Create a visualization using a business data set in Tableau or Power BI. |
Group Analysis | Perform statistical analysis on a real data set using Python. |
Evaluation:
Evaluation Method | Details |
|---|
Mid-program Assessment | A test on data visualization and statistical analysis techniques learned. |
Peer Feedback | Review and provide constructive feedback on group analysis projects. |
Phase 3: Advanced Integration (Days 61–90)
Goals:
Training Modules:
Module | Topics Covered |
|---|
Module 5: Advanced Analytics and Predictive Modeling | Regression, classification, and clustering models. Building and testing predictive models in Python.
|
Module 6: Business Intelligence and Decision-Making | |
Activities:
Activity | Details |
|---|
Capstone Project | Build a predictive model using a real-world data set and present findings. |
Presentation to Management | Present the capstone project results to a mock management team. |
Evaluation:
Evaluation Method | Details |
|---|
Final Assessment | Evaluation of capstone project, including model accuracy and presentation quality. |
Instructor and Peer Feedback | Final feedback from trainers and peers on the project deliverables. |
Schedule Overview
Week | Focus | Activities | Evaluation Method |
|---|
1–4 | Foundation | Core modules, hands-on exercises | Quizzes and feedback from trainers |
5–8 | Skill Development | Data visualization project, group analysis | Mid-program assessments and peer feedback |
9–12 | Advanced Integration | Capstone project, business intelligence tools | Final project assessments and presentations |
Resources Required
Software: Microsoft Excel, SQL, Python, Tableau, Power BI.
Instructor Support: Access to mentors or trainers for feedback and guidance.
Materials: Training guides, tutorial videos, and real business data sets.
Monitoring and Evaluation
Weekly Check-ins: Regular updates to monitor progress and address challenges.
Mid-Point Review: In-depth review after Phase 2 to assess progress and provide feedback.
Final Review: Evaluation based on the capstone project and presentation.
This structure ensures a clear and measurable progression through the 90-day curriculum, making sure that participants receive the support and practical experience necessary to excel in data analytics.
Plan Templates @ Template.net
