MODULE 1: DATA ANALYSIS FOUNDATION
Session 1: Data Analysis
Introduction
🕒 Time: 1
Hour
🎯 Objective: Understand
what Data Analysis is, its importance, and how it is used in decision-making.
🔹 Example:
- A retail store analyses customer purchases to
identify the best-selling products.
- Netflix analyses user watch history to recommend movies.
📌 Real-Life
Terminology Usage:
- "Based on the data trends, we should stock
more summer clothing in May."
- "The sales report shows a 20% increase in
demand for organic products."
Session 2: Data Preparation for
Analysis
🕒 Time: 1
Hour
🎯 Objective: Learn
how to clean, transform, and prepare data for analysis.
🔹 Example:
- Removing duplicate customer records in an
e-commerce database.
- Handling missing values in survey responses.
📌 Real-Life
Terminology Usage:
- "We need to remove incomplete entries
before analyzing survey results."
- "Let's convert all currency values to USD
for consistency."
Session 3: Common Data Problems
🕒 Time: 1
Hour
🎯 Objective: Identify
common data issues and how to fix them.
🔹 Example:
- An HR team finds that employee age data contains
negative values.
- A bank finds transaction timestamps missing for
some records.
📌 Real-Life
Terminology Usage:
- "Let's standardize date formats before
processing."
- "We found inconsistencies in the revenue
reports; let's clean the data first."
Session 4: Various Tools for
Data Analysis
🕒 Time: 1
Hour
🎯 Objective: Introduction
to tools like Excel, Power BI, Tableau, Python, and R.
🔹 Example:
- A logistics company uses Power BI to visualize
shipment delays.
- A financial analyst uses Excel PivotTables to
summarize sales data.
📌 Real-Life
Terminology Usage:
- "We use Power BI dashboards for real-time
sales tracking."
- "Excel’s VLOOKUP helps merge data from
different sheets."
Session 5: Evolution of
Analytics Domain
🕒 Time: 1
Hour
🎯 Objective: Understand
how data analytics has evolved from simple reports to AI-powered insights.
🔹 Example:
- Early analytics: Excel-based financial reports.
- Modern analytics: AI-driven stock market
predictions.
📌 Real-Life
Terminology Usage:
- "Earlier, we manually checked trends, but
now AI predicts them."
- "Data-driven decisions have revolutionized
customer experience."
MODULE 2: CLASSIFICATION OF
ANALYTICS
Session 6: Four Types of
Analytics
🕒 Time: 1
Hour
1️⃣ Descriptive Analytics
(What happened?)
🔹 Example: Monthly
sales reports show total revenue and growth.
📌 "Last
month's report showed a 15% increase in sales."
2️⃣ Diagnostic Analytics
(Why did it happen?)
🔹 Example: A
drop in sales was due to stock shortages.
📌 "Sales
dropped in July because of supply chain delays."
3️⃣ Predictive Analytics
(What will happen?)
🔹 Example: Predicting
next month’s customer demand.
📌 "We
expect a 10% rise in demand before Diwali."
4️⃣ Prescriptive Analytics
(What should be done?)
🔹 Example: AI
suggests reducing prices to boost sales.
📌 "Lowering
prices by 5% could increase revenue by 20%."
Day 2: CRIP-DM Model, Data
Analysis Techniques & Visualization
MODULE 3: CRIP-DM Model
Session 7: CRIP-DM Model
Overview
🕒 Time: 1
Hour
🎯 Objective: Understand
how businesses apply analytics systematically.
🔹 Example:
- A telecom company uses CRIP-DM for customer churn
prediction.
📌 Real-Life
Terminology Usage:
- "Before launching the new product, let's
analyze customer feedback using the CRIP-DM framework."
MODULE 4: UNIVARIATE DATA
ANALYSIS
Session 8: Summary Statistics
& Central Tendency
🕒 Time: 1
Hour
🎯 Objective: Learn
to compute Mean, Median, and Mode.
🔹 Example:
- Average monthly expenses of employees.
- Finding the most frequent product purchased in a
store.
📌 Real-Life
Terminology Usage:
- "The average score in the exam was
75%."
- "The median salary in our company is
₹50,000 per month."
MODULE 5: DATA ANALYSIS WITH
VISUAL CHARTS
Session 9: Visualization with
Charts
🕒 Time: 1.5
Hours
📊 Real-Life
Examples of Charts:
- Line Chart: Website traffic over
months.
- Column/Bar Chart: Product sales
comparison.
- Waterfall Chart: Revenue contribution
from different channels.
- Tree Map Chart: Product categories
contribution to total sales.
- Box Plot: Distribution of employee
salaries.
📌 Real-Life
Terminology Usage:
- "A bar chart clearly shows the top-selling
products."
- "A box plot helps us see salary disparities
in different departments."
MODULE 6: BI-VARIATE DATA
ANALYSIS
Session 10: Regression &
Correlation Analysis
🕒 Time: 1
Hour
🔹 Example:
- Analyzing how temperature affects ice cream sales.
- Relationship between advertising budget and
revenue.
📌 Real-Life
Terminology Usage:
- "A high correlation between marketing spend
and revenue shows a positive impact."
- "Regression analysis predicts next
quarter’s sales based on past trends."
MODULE 7: Advanced Power BI
with Probability & Prediction
Session 11: Power BI
Forecasting & Custom Predictions
🕒 Time: 1.5
Hours
🔹 Example:
- Power BI predicting next month’s revenue based on
past trends.
- Using Python scripts in Power BI for advanced
forecasting.
📌 Real-Life
Terminology Usage:
- "Power BI's AI-powered forecasting helps
predict seasonal sales trends."
- "Using Python in Power BI, we customized
demand forecasting for different regions."
Final Hands-On Exercise &
Q&A
🕒 Time: 2
Hours
🎯 Objective: Participants
apply concepts to real-world datasets and present insights.
🔹 Real-Life
Scenario:
- Analyzing customer feedback data for a product
launch strategy.
- Predicting employee attrition using historical HR
data.
📌 Final
Takeaways:
- "Data analysis is the foundation of smart
business decisions."
- "Understanding past trends helps predict
and improve future outcomes."
Conclusion & Certification
🕒 Time: 30
Mins
- Recap of key learnings.
- Distribution of certificates.
- Feedback & next steps.
🚀 Outcome:
- Participants gain hands-on experience in
data analytics.
- They understand real-life applications of
analytics in business & daily life.
- They learn to communicate insights
effectively using the right terminology.
📩 Want us to facilitate a
session for your team? Reach out at training@compassclock.in / +917845050100 😊
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