Link to Coursera - BITS BSc Computer Science program: Link.

Note that the individual courses in this program are available under Coursera +.

Course - 2 : Data Visualization

Link to the course: Link.

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Refs:

ROUGH NOTES (!)
Updated: 20/07/2026

[Introducing Data Analysis]

Data Analysis: Unlocking Insights Through Data

What is Data Analysis?

The process of inspecting, cleaning, transforming, and modelling data to discover useful information.

Purpose:

• To support decision-making

• Find patterns

• Gain insights

Eg:

• Analyzing sales data to identify trends

• Studying user behaviour on a website

• Understanding customer feedback

Importance of Data Analysis

Why is Data Analysis important?

• Helps in making informed decisions

• Optimises processes and operations

• Uncovers trends and patterns that drive strategic actions

Eg: Companies like Amazon, Netflix, and Google use data analysis to personalize user experiences.

Types of Data

Structured Data (Organized data)

Eg: Sales records, student grades, etc. Usual formats: Databases and spreadsheets.

Unstructured Data (Unorganized data)

Eg: Emails, social media posts, etc. Usual formats: Text, Image, Video.

Semi-structured Data (Partially organized data)

Eg: Log files, metadata, etc. Usual formats: Json, xml.

Types of Data Analysis

Descriptive Analysis:

Summarises historical data.

Eg: Average sales per month.

Diagnostic Analysis:

Investigates reasons behind past outconmes.

Eg: Why did sales drop last quarter?

Predictive Analytics:

Uses historical data to forecast future outcomes.

Eg: Projecting next quarter’s sales.

Prescriptive Analytics:

Suggests actions based on predictions.

Eg: Recommending marketing strategies to boost sales.

Common Techniques in Data Analysis

Statistical Analysis: Mean, Median, Mode, Standard Deviation, etc.

Data Visualization: Charts, Graphs, Heatmaps, and more to visually represent data.

Correlation and Regression Analysis: Understanding relationships between variables.

Machine Learning Techniques: Clustering, Classification, and Prediction Models.

Tools for Data Analysis

Spreadsheet tools (eg: Excel, Google Sheets)

• Basic analysis, pivot tables, charts

Statistical tools (eg: R, Python with Pandas, SPSS)

• Advanced statistical analysis and modelling

Data Visualization Tools (eg: Tableau, Power BI)

• Creating interactive charts and dashboards

Database Management Systems (eg: SQL)

• Querying and managing large datasets

Getting Started with Data Analysis

Start Small: Begin with simple datasets and basic tools like Excel then proceed to visualisation tools or programming libraries.

Practice: Work on real-world projects or datasets.

Learn Continuously.

[Types of Data Analysis]

Types of Data Analysis

• Descriptive Analysis

• Diagnostic Analysis

• Predictive Analysis

• Prescriptive Analysis

• Exploratory Analysis

Descriptive Analysis

Describes and summarises past data to identify patterns, trends, and distributions.

Key techniques:

Statistical Summaries: Mean, median, mode, standard deviation

Visualisation Tools: Bar charts, histograms, pie charts, frequency distributions

Descriptive Statistics: Measures of spread (range, quartiles), shape (skewness, kurtosis)

Descriptive Analysis: Applications

Business: Analysing monthly sales data to determine which products performed well.

Healthcare: Summarising patient demographics to understand disease distribution.

Social Media: Evaluating engagement metrics like likes, shares and comments.

Eg: A retailer summarises quarterly sales data across different regions to identify top-performing locations.

Diagnostic Analysis

Delves into the data to discover the causes behind trends or anomalies.

Key techniques:

Data mining: Discovering patterns within large datasets

Root Cause Analysis: Identifying underlying reasons for observed results.

Correlation and Regression Analysis: Exploring relationships between variables.

Diagnostic Analysis: Applications

Healthcare: Investigating the causes of a spike in hospital admissions.

Marketing: Analysing reasons behind a sudden drop in website traffic.

Manufacturing: Diagnosing the causes of product defects.

Eg: An e-commerce platform analyses customer churn data to find out why users are abandoning their carts.

Predictive Analysis

Uses historical data and statistical models to predict future outcomes.

Key techniques:

Machine Learning: Algorithms like regression, decision trees, neural networks

Time Series Analysis: Forecasting trends based on time-indexed data

Predictive Modelling: Using models to simulate possible outcomes (eg, Monte Carlo simulations)

Predictive Analysis: Applications

Finance: Predicting stock prices or market trends.

Retail: Forecasting product demand for the upcoming season.

Healthcare: Predicting disease outbreaks or patient readmissions.

Eg: A financial institution uses predictive modelling to estimate credit risk and default probabilities.

Eg: An online streaming service predicts user preferences to recommend content.

Prescriptive Analysis

Provides recommendations on actions to take, based on predictive insights, to optimise outcomes.

(What happened -> What will happen -> How can we make it happen)

Key techniques:

Optimisation Models: Linear programming, optimisation algorithms to find the best course of action.

Simulation: Testing various scenarios to evaluate potential outcomes.

Decision Analysis: Tools like decision trees, and cost-benefit analysis.

Prescriptive Analytics: Applications

Supply Chain: Optimising inventory levels based on demand forecasts.

Healthcare: Personalised treatment plans based on patient data.

Marketing: Designing targeted advertising campaigns.

Eg: A logistics company uses prescriptive analytics to optimise delivery routes, reducing costs and time.

Eg: A retail chain adjusts pricing strategies based on predictive insights to maximise profits.

Exploratory Analysis

Uncover patterns, relationships, or insights in data without having a specific hypothesis in mind.

Key techniques:

Data Visualisation: Scatter plots, heatmaps, box plots to reveal trends and correlations.

Clustering Algorithms: K-means clustering to group similar data points.

Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to simplify data.

Exploratory Analysis: Applications

Product Development: Exploring user feedback to discover unmet needs.

Healthcare: Uncovering patterns in patient data that may suggest new research directions.

Social Media: Identifying emerging trends or viral content.

Eg: A company explores customer review data to identify common complaints and potential areas for product improvement.