📊 Data Science & Data Analytics
Complete Guide to Data Science, Data Analytics, Statistics, Python, SQL, Machine Learning, Data Visualization, Power BI, Tableau, Excel, Artificial Intelligence and Careers
Data Science combines data, statistics, programming, analytics and intelligent decision-making.
1. What is Data Science?
Data Science is an interdisciplinary field that combines mathematics, statistics, programming, computer science, artificial intelligence and domain knowledge to extract useful knowledge and insights from data.
Data Science is used to understand large and complex datasets, discover patterns, build predictive models and support better decision-making.
Major Components of Data Science
📊 Statistics
Helps understand distributions, relationships, uncertainty, correlation and statistical significance.
💻 Programming
Python, R and other languages are used for data processing, analysis and model development.
🗄️ Databases
SQL and database systems are used to store, retrieve and manage data.
🤖 Machine Learning
Algorithms learn patterns from data and make predictions or decisions.
📈 Visualization
Charts, graphs and dashboards make complex information easier to understand.
🧠 Domain Knowledge
Understanding the application area helps convert data into useful decisions.
2. What is Data Analytics?
Data Analytics is the process of examining, cleaning, transforming and interpreting data to identify useful patterns, trends and insights.
Data Analytics helps organisations answer questions such as:
- What happened?
- Why did it happen?
- What is happening now?
- What may happen in the future?
- What action should we take?
3. Data Science vs Data Analytics
| Feature | Data Science | Data Analytics |
|---|---|---|
| Purpose | Discover knowledge and build predictive/intelligent systems. | Analyse existing data to generate insights. |
| Main focus | Prediction, modelling, machine learning and advanced analysis. | Reporting, trends, dashboards and decision support. |
| Programming | Usually extensive. | May range from low to extensive depending on the role. |
| Tools | Python, R, SQL, ML frameworks and cloud platforms. | Excel, SQL, Power BI, Tableau, Python and similar tools. |
| Output | Models, predictions, algorithms and intelligent applications. | Reports, dashboards, insights and recommendations. |
4. Data Science Life Cycle
Step 1 – Problem Definition
Clearly identify the business, educational, scientific or research problem.
Step 2 – Data Collection
Collect relevant information from databases, surveys, websites, sensors, applications or other sources.
Step 3 – Data Cleaning
Handle missing values, duplicate records, inconsistent formats and incorrect data.
Step 4 – Exploratory Data Analysis
Use statistics and visualisation to understand patterns and relationships.
Step 5 – Feature Engineering
Create useful variables or transform existing variables for analysis and machine learning.
Step 6 – Model Building
Apply statistical or machine-learning techniques when prediction or classification is required.
Step 7 – Evaluation
Measure model or analytical performance using appropriate metrics.
Step 8 – Deployment
Use the final model, dashboard or analytical result in a real-world environment.
Step 9 – Monitoring
Monitor results and update the system when data or requirements change.
5. Types of Data Analytics
1. Descriptive Analytics
Answers: What happened?
Examples: sales reports, attendance reports, monthly revenue and student performance reports.
2. Diagnostic Analytics
Answers: Why did it happen?
Uses comparisons, correlations and investigation to identify causes and contributing factors.
3. Predictive Analytics
Answers: What may happen?
Uses statistics and machine learning to predict future outcomes.
4. Prescriptive Analytics
Answers: What should we do?
Suggests possible actions based on predictions, constraints and objectives.
6. Important Data Science & Data Analytics Tools
🐍 Python
Programming Data ScienceOne of the most widely used languages for data analysis, machine learning and automation.
R
Statistics AnalyticsPopular programming language for statistics, research and data visualisation.
SQL
Database QueryUsed to retrieve, filter, join, aggregate and manipulate database data.
Microsoft Excel
Spreadsheet AnalyticsUseful for calculations, pivot tables, charts, data cleaning and reporting.
Power BI
Dashboard Business IntelligenceUsed to build interactive dashboards, reports and business intelligence solutions.
Tableau
Visualization BIPopular platform for interactive data visualisation and dashboards.
Jupyter Notebook
Python ResearchInteractive environment for data analysis, visualisation and experiments.
Google Colab
Cloud PythonCloud-based environment for Python, data analysis and machine learning.
7. Python for Data Science
Python is one of the most important programming languages in the Data Science ecosystem because of its simple syntax and extensive collection of libraries.
Important Python Libraries
| Library | Application |
|---|---|
| NumPy | Numerical computing and arrays. |
| Pandas | Data cleaning, transformation and analysis. |
| Matplotlib | Data visualisation and charts. |
| Seaborn | Statistical data visualisation. |
| Scikit-learn | Machine learning. |
| TensorFlow | Machine learning and deep learning. |
| PyTorch | Deep learning and AI research. |
8. SQL for Data Analytics
SQL (Structured Query Language) is used to work with relational databases.
Important SQL Concepts
SELECT
Retrieve information from a database.
WHERE
Filter records according to conditions.
GROUP BY
Group records for analysis.
ORDER BY
Sort results.
JOIN
Combine information from multiple tables.
Aggregate Functions
SUM, AVG, COUNT, MIN and MAX are commonly used for analysis.
Example Analytical Question
9. Statistics in Data Science
Statistics provides the mathematical foundation for understanding data, uncertainty and relationships between variables.
Important Topics
- Mean, median and mode
- Range and variance
- Standard deviation
- Probability
- Probability distributions
- Correlation
- Regression
- Sampling
- Hypothesis testing
- Confidence intervals
- Statistical significance
- Bayesian methods
10. Data Visualization
Data visualisation converts numerical and categorical information into visual forms that make patterns easier to understand.
📊 Bar Chart
Compare categories.
📈 Line Chart
Show trends over time.
🥧 Pie Chart
Show proportions of a whole.
🔵 Scatter Plot
Study relationships between variables.
📦 Box Plot
Understand distributions and outliers.
🔥 Heatmap
Display values or correlations using intensity.
11. Power BI and Business Intelligence
Power BI is a business intelligence platform used to connect data sources, transform information, create visualisations and develop interactive reports and dashboards.
Power BI Workflow
- Connect to data.
- Clean and transform data.
- Create relationships between tables.
- Create calculations and measures.
- Build charts and dashboards.
- Publish and share reports.
Common Dashboard Applications
- Sales dashboard
- Student performance dashboard
- Financial dashboard
- HR dashboard
- Marketing dashboard
- Inventory dashboard
- Hospital dashboard
12. Tableau
Tableau is a data visualisation and business intelligence platform used to transform data into interactive charts, dashboards and reports.
Applications
- Business dashboards
- Sales analysis
- Financial reporting
- Customer analytics
- Marketing analysis
- Geographical analysis
- Performance monitoring
13. Machine Learning in Data Science
Machine Learning allows computers to learn patterns from data and make predictions or decisions without explicitly programming every rule.
Supervised Learning
Uses labelled data to learn relationships between inputs and known outputs.
Examples: classification and regression.
Unsupervised Learning
Finds patterns or structures in data without predefined labels.
Examples: clustering and dimensionality reduction.
Reinforcement Learning
An agent learns by interacting with an environment and receiving rewards or penalties.
14. Common Machine Learning Algorithms
| Algorithm | Common Use |
|---|---|
| Linear Regression | Predict numerical values. |
| Logistic Regression | Classification problems. |
| Decision Tree | Classification and regression. |
| Random Forest | Classification and regression. |
| K-Nearest Neighbours | Classification and similarity-based prediction. |
| Support Vector Machine | Classification and regression. |
| K-Means | Clustering. |
| Neural Networ
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