Data Analytics From Beginner To Advanced

Unlock the power of your data with our comprehensive course, Data Analytics with Excel, SQL, and Power BI.

Designed for beginners and professionals alike, this course will equip you with the essential skills to transform raw data into meaningful insights using three of the most widely used tools in the industry: Microsoft Excel, SQL, and Power BI.

What You’ll Learn

• Data Preparation and Cleaning: Master techniques to import, clean, and organize data efficiently in Excel, ensuring accuracy and reliability.

• Data Visualization with Excel: Learn to create compelling charts, graphs, and dashboards that effectively communicate your data’s story.

• SQL for Data Analysis: Gain a solid foundation in SQL to query databases, extract data, and perform analysis directly from structured datasets.

• Introduction to Power BI: Understand Power BI’s interface, features, and capabilities to begin your journey into powerful data analytics.

• Building Interactive Reports: Develop the skills to design and share interactive reports and dashboards in Power BI, enhancing data-driven decision-making.

• Data Modeling and DAX: Explore data modeling concepts and dive into Data Analysis Expressions (DAX) to perform advanced calculations and analysis.

Why Enroll

In today’s data-driven world, the ability to analyze, interpret, and visualize data is crucial across various industries. This course offers hands-on experience with real-world datasets, equipping you with practical, job-ready skills that can be immediately applied. Whether you’re starting a career in data analytics or looking to advance your existing role, this course is a valuable stepping stone.

Prerequisites

No prior experience in data analytics is required. A basic understanding of Microsoft Excel is helpful but not mandatory.

? Lesson Script: What is Data Analysis

1️⃣ Introduction (Hook)

"Data is everywhere — in our phones, our businesses, our hospitals, even in the decisions we make every day. But raw data by itself doesn’t mean much. What gives data its power is analysis."

2️⃣ Definition of Data Analysis

• Data analysis is the process of collecting, cleaning, transforming, and interpreting data in order to discover useful information, draw conclusions, and support decision-making.

• In simple words: it’s about turning raw data into meaningful insights.

3️⃣ Why Data Analysis Matters

• Organizations rely on it to:

  • Identify patterns and trends – e.g., sales going up during festive seasons.

  • Solve problems – e.g., why customers are leaving a service.

  • Make better decisions – e.g., whether to expand into a new market.

  • Predict outcomes – e.g., forecasting next month’s revenue.

4️⃣ Steps in Data Analysis

1. Define the problem/question – What are we trying to solve?

2. Collect data – From surveys, databases, sensors, etc.

3. Clean the data – Remove errors, duplicates, or missing values.

4. Analyze the data – Using statistical tools, visualization, or advanced models.

5. Interpret and communicate results – Translate numbers into insights and action.

5️⃣ Real-Life Example

"Imagine you run an online store. You collect data on customer purchases. By analyzing this data, you can discover that most buyers shop on weekends and prefer certain products. With this insight, you can adjust marketing campaigns and stock inventory accordingly. That’s the power of data analysis."

? Introduction

"In this lesson, we’re going to explore the four main types of data analysis that every data professional should know. These are: Descriptive analysis, Diagnostic analysis, Predictive analysis, and Prescriptive analysis.

Each one builds on the other. Descriptive tells us what happened, Diagnostic explains why it happened, Predictive forecasts what will happen, and Prescriptive guides us on what action to take. By the end of this lesson, you’ll be able to clearly explain and differentiate between the four types and understand when to apply each in real-world scenarios."*

1️⃣ Descriptive Analysis – “What happened?”

*"Descriptive analysis is the simplest form of data analysis. It looks at historical data and summarizes it into meaningful information.

For example, let’s say we are analyzing company sales. Descriptive analysis might tell us:

• ‘Total sales last month were $50,000.’

• ‘The North region contributed 40% of total sales.’

The goal here is not to explain why something happened, but to provide a snapshot of the past.

Techniques used in descriptive analysis include:

• Averages such as mean, median, and mode.

• Frequency counts, percentages, and distributions.

• Visualization techniques such as bar charts, line charts, and histograms.

So, in summary: descriptive analysis gives us the “what”."*

2️⃣ Diagnostic Analysis – “Why did it happen?”

*"The second type is diagnostic analysis. Here, we go deeper to identify the causes of events.

For example, if sales dropped last month, diagnostic analysis tries to answer questions like:

• ‘Why did sales drop?’

• ‘Was it because a competitor launched a cheaper product?’

• ‘Or because customers shifted to a different region?’

Techniques used in diagnostic analysis include:

• Drill-down analysis, where we break data into smaller categories.

• Correlation analysis, which checks for relationships between variables.

• Hypothesis testing and root-cause analysis.

The key idea is: descriptive analysis shows you the problem, while diagnostic analysis explains the reason behind it."*

3️⃣ Predictive Analysis – “What will happen?”

*"Now that we know what happened and why, the next question is: ‘What is likely to happen in the future?’

This is where predictive analysis comes in. It uses historical data combined with statistical models and machine learning to forecast future outcomes.

For example:

• ‘Based on past sales patterns, we expect sales to grow by 10% next quarter.’

• ‘Employees who rated job satisfaction below 3 are 60% more likely to resign.’

Common techniques include:

• Regression analysis.

• Forecasting models like ARIMA or Prophet for time-series data.

• Machine learning algorithms for classification and clustering.

Predictive analysis gives businesses the ability to plan ahead by anticipating possible future scenarios."*

4️⃣ Prescriptive Analysis – “What should we do?”

*"Finally, prescriptive analysis takes things one step further. It doesn’t just tell us what might happen — it recommends the best course of action.

For example:

• ‘To maximize profit, reduce prices by 5% and increase digital marketing by 20%.’

• ‘To reduce employee attrition, offer flexible working arrangements to younger staff.’

Techniques used in prescriptive analysis include:

• Optimization models, such as linear programming.

• Simulation techniques like Monte Carlo.

• Decision trees and scenario modeling.

Prescriptive analysis is the most advanced type of data analysis because it turns insights into actionable strategies."*

✅ Recap

*"Let’s quickly recap the four types of data analysis:

• Descriptive: What happened? (past events)

• Diagnostic: Why did it happen? (causes)

• Predictive: What will happen? (future forecast)

• Prescriptive: What should we do? (recommended actions)

Together, they form a complete cycle of data-driven decision-making — from understanding the past to shaping the future."

1️⃣ Introduction

"Behind every smart business decision, there’s someone who has turned numbers into knowledge. That person is a Data Analyst."

2️⃣ Definition of a Data Analyst

• A data analyst is a professional who collects, processes, and analyzes data to help organizations make informed decisions.

• In short: They turn raw data into useful insights.

3️⃣ What a Data Analyst Does (Key Roles)

• Collects Data → from databases, surveys, APIs, or other sources.

• Cleans Data → removes errors, duplicates, or inconsistencies.

• Analyzes Data → uses statistics, visualization, and business intelligence tools.

• Communicates Insights → creates reports, dashboards, and recommendations.

4️⃣ Skills of a Data Analyst

• Technical skills: Excel, SQL, Python/R, Power BI/Tableau.

• Analytical skills: Statistics, problem-solving, critical thinking.

• Communication skills: Presenting findings in a simple way.

5️⃣ Example

"Imagine a retail company wants to know why sales dropped last quarter. The data analyst digs into sales data, customer behavior, and market trends — then explains that sales fell because a competitor lowered prices. With this insight, the company can respond effectively."

6️⃣ Closing / Transition

"So, a data analyst is not just someone who works with spreadsheets — they’re problem-solvers who help businesses make smarter, data-driven decisions. In the next lesson, we’ll look at the skills and tools every data analyst needs to succeed."

1️⃣ Introduction

"Every organization, no matter how successful, faces challenges. These challenges, when clearly defined, are what we call business problems."

2️⃣ Definition of a Business Problem

• A business problem is a challenge, issue, or gap that prevents an organization from achieving its goals effectively.

• In simple words: It’s the obstacle standing between where the business is today and where it wants to be.

3️⃣ Why Business Problems Matter

• Identifying business problems helps organizations:

  • Avoid wasted resources.

  • Focus on what really impacts performance.

  • Create targeted solutions.

  • Stay competitive.

4️⃣ Examples of Business Problems

• Sales decline – e.g., revenue dropping compared to last year.

• Customer churn – too many customers leaving a service.

• Operational inefficiency – processes taking too long or costing too much.

• Poor decision-making – relying on guesswork instead of data.

5️⃣ Data’s Role in Solving Business Problems

"This is where data analysis comes in. By analyzing data, we can uncover the root causes of business problems and design solutions based on evidence — not assumptions."

6️⃣ Closing / Transition

"So, a business problem is simply a challenge holding back growth or performance. In the next lesson, we’ll see how data analysts approach these problems step by step."

? Daily Activities of a Data Analyst

? Two Modes of Work (Framework Intro)

✅ Batch Projects (Deep Dives)

• Triggered by a new business question or issue.

• Example: “Why are Q2 sales declining?”

• Data analyst follows all 8 steps → produces detailed insights → delivers final report.

✅ Ongoing Monitoring (KPI Tracking)

• Continuous process of tracking business health.

• Analysts build dashboards that refresh automatically.

• Example: Monitor daily sales, customer churn, and website traffic.

• Ensures stakeholders spot issues early, before they become big problems.

? Together, these two modes make up the real daily work of analysts.
Now let’s look at the 8 steps that both modes rely on.

? Ongoing Monitoring (with Batch Project Trigger)

1. Define Objectives & KPIs (one-time, update when business changes)

• Choose key business metrics to track.

• Example: Daily sales, churn, website traffic.

2. Get Data / Data Collection (one-time setup + recurring)

• Connect dashboards to live data sources (SQL, APIs, CRM, cloud storage).

• Automate data extraction/refresh.

3. Data Cleaning & Preparation (one-time pipeline + recurring)

• Define rules for missing values, formats, duplicates.

• Run automated cleaning scripts during each refresh.

4. Build Automated Dashboards (one-time setup)

• Create KPI dashboards with drill-downs and alerts.

• Configure auto-refresh.

5. Daily Monitoring of Dashboards (recurring)

• Review dashboards for anomalies and deviations.

• Drill down if something looks off (region, product, customer segment).

6. Interpretation of Results (recurring)

• Translate deviations into possible business meaning.

• Example: “Traffic dropped because of server downtime.”

7. Communicate Insights & Recommend Actions (recurring)

• Send alerts, reports, or quick updates to stakeholders.

• Suggest immediate action if needed.

8. Trigger Batch Project (conditional)

• If a KPI deviation is severe, persistent, or unexplained, it moves beyond monitoring.

• Example: “Churn rate doubled and hasn’t gone back down.”

• Launch a Batch Project → deep dive using the 8-step process.

9. Refinement (recurring/periodic)

• Update KPIs, dashboards, alerts, and pipelines.

• Lessons from Batch Projects are fed back into monitoring.

? Batch Projects (Deep Dives)

Triggered by a new business question or issue.
Example: “Why are Q2 sales declining?”

The data analyst follows these 8 steps → produces detailed insights → delivers a final report.

1. Define Objectives, Questions & Metrics

• Meet with stakeholders to understand the problem.

• Translate goals into specific, measurable questions.

• Example: Why are sales dropping in Q2?

• Sub-questions: Which region? Which product line? Which customer group?

• Define success metrics (e.g., revenue recovery, churn reduction).

2. Data Collection & Extraction

• Identify sources (databases, spreadsheets, APIs, CRM, cloud).

• Extract relevant data (SQL, Python, BI connectors).

• Ensure correct time frame (focus on Q2 data vs Q1).

3. Data Cleaning & Preparation

• Handle missing values, duplicates, inconsistencies.

• Standardize formats (dates, categories, currencies).

• Merge datasets if needed.

• Create calculated fields (e.g., sales growth rates).

4. Data Exploration & Analysis

(a) Descriptive Analysis

• Summarize Q2 data: averages, totals, distributions.

• Simple charts (bar, line, histogram) to show drop.

• Answers: “What happened?”

(b) Exploratory Analysis

• Check trends, anomalies, correlations.

• Example: Region vs Product vs Customer group analysis.

• Use scatterplots, heatmaps, boxplots.

• Answers: “Where is the problem concentrated?”

(c) Deeper Analysis (if needed)

• Hypothesis testing: Is North region decline statistically significant?

• Regression: What factors drive sales decline?

• Cohort/Segmentation: Which customer group churned the most?

• Answers: “Why did it happen?” and “What factors matter most?”

5. Interpretation of Results

• Translate findings into insights.

• Example: “Sales dropped mainly in the North region, among customers aged 25–34, driven by reduced ad spend and longer delivery times.”

• Validate with business context.

6. Visualization & Reporting

• Build report/dashboard showing findings.

• Use storytelling: Problem → Evidence → Insights → Recommendation.

• Example: Side-by-side charts of Q1 vs Q2 sales by region.

7. Communicate Insights & Recommend Actions

• Present results to stakeholders.

• Answer questions.

• Recommend actions (increase marketing in North, improve logistics, target retention offers).

8. Monitoring & Continuous Improvement

• Suggest follow-up tracking to ensure fixes work.

• Example: “Track sales weekly in North region for next 3 months.”

• Feed results into new objectives for next cycle.