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:

Apply advanced data analytics techniques to real-world projects.
Develop proficiency in predictive modeling and business intelligence.

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	Introduction to business intelligence tools. Using analytics to inform business strategy.

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.

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