Data Science, AI & Emerging Technologies
Data Analytics & Visualization
Duration
2 Months
Price
Rs. 15,000.00
Course Description
Course Syllabus
1. Introduction to Data Analytics
- The data analytics lifecycle: collection, cleaning, analysis, visualization & communication
- Types of analytics: descriptive, diagnostic, predictive & prescriptive
- Key roles in data: analyst, engineer, scientist & business intelligence developer
- Real-world applications: healthcare, finance, retail, marketing & operations
- Case studies: how data-driven decisions transformed industry leaders
2. Data Literacy & Statistical Thinking
- Understanding data types: nominal, ordinal, interval & ratio
- Measures of central tendency: mean, median & mode
- Measures of spread: range, variance, standard deviation & IQR
- Probability basics: events, distributions & the normal curve
- Correlation vs causation: spotting spurious relationships in data
3. Data Collection & Sources
- Primary vs secondary data sources: surveys, APIs, databases & web scraping
- Structured vs unstructured data: tables, JSON, text, images & logs
- Data governance, privacy & ethics: GDPR principles & responsible use
- Open data sources: government datasets, Kaggle, World Bank & Google Dataset Search
- Introduction to databases: relational vs non-relational concepts
4. Microsoft Excel for Data Analysis
- Advanced formulas: VLOOKUP, XLOOKUP, INDEX/MATCH, SUMIFS & COUNTIFS
- Pivot Tables & Pivot Charts: summarising & slicing large datasets
- Data validation, conditional formatting & named ranges
- Power Query: importing, transforming & merging data from multiple sources
- What-if analysis: Goal Seek, Scenario Manager & Data Tables
5. SQL Fundamentals
- Relational database concepts: tables, keys, relationships & schemas
- SELECT, FROM, WHERE, ORDER BY & LIMIT: core query structure
- Filtering with AND, OR, NOT, IN, BETWEEN & LIKE operators
- Aggregate functions: COUNT, SUM, AVG, MIN & MAX
- GROUP BY & HAVING: segmenting and filtering grouped data
6. Intermediate SQL
- INNER JOIN, LEFT JOIN, RIGHT JOIN & FULL OUTER JOIN: combining tables
- Subqueries & correlated subqueries: queries within queries
- Common Table Expressions (CTEs): WITH clause for readable query logic
- UNION & INTERSECT: combining result sets across queries
- String, date & mathematical functions: manipulating data in SQL
7. Advanced SQL for Analytics
- Window functions- ROW_NUMBER, RANK, DENSE_RANK & NTILE
- Running totals & moving averages with SUM OVER & AVG OVER
- LAG & LEAD- comparing rows across time periods
- Query optimization- indexes, EXPLAIN plans & avoiding full table scans
- Working with databases- MySQL, PostgreSQL & Google BigQuery
8. Database Design & Data Modelling
- Entity-Relationship (ER) diagrams: designing normalised schemas
- Star schema & snowflake schema: data warehouse modelling
- Fact tables & dimension tables: the building blocks of BI
- Slowly changing dimensions (SCDs): handling historical data changes
- Introduction to cloud databases: AWS RDS, Google Cloud SQL & Azure SQL
9. Python Programming Fundamentals
- Python setup: Anaconda, Jupyter Notebooks & VS Code environment
- Variables, data types, lists, dictionaries, tuples & sets
- Control flow :if/else, for loops, while loops & list comprehensions
- Functions: defining, calling, arguments, return values & lambda functions
- File handling: reading & writing CSV, JSON & Excel files with Python
10. NumPy & Pandas for Data Wrangling
- NumPy arrays- creation, indexing, slicing, broadcasting & vectorised operations
- Pandas Series & DataFrames- the core data structures for analytics
- Loading data- pd.read_csv, read_excel, read_sql & read_json
- Data cleaning: handling missing values, duplicates, outliers & type conversions
- Data transformation: merge, join, groupby, pivot_table, melt & stack
11. Exploratory Data Analysis (EDA)
- EDA workflow: profiling, describing, visualising & summarising datasets
- Univariate analysis: distributions, histograms & box plots
- Bivariate analysis: scatter plots, correlation matrices & cross-tabulations
- Outlier detection: IQR method, Z-score & visual inspection
- Automated EDA tools: pandas-profiling & Sweetviz reports
12. Statistical Analysis with Python
- Hypothesis testing framework: null hypothesis, p-values & significance levels
- t-tests, chi-square tests & ANOVA: comparing groups & categories
- Confidence intervals & margin of error: quantifying uncertainty
- Correlation analysis: Pearson, Spearman & Kendall coefficients
- A/B testing design: sample size calculation, test execution & result interpretation
13. Data Visualization Principles
- The grammar of graphics: data, aesthetics, geometry & scales
- Choosing the right chart: bar, line, scatter, pie, heatmap & treemap
- Color theory for data: sequential, diverging & categorical palettes
- Pre-attentive attributes: using size, color & position to guide attention
- Common visualization mistakes: chart junk, misleading axes & dual axes
14. Python Visualization Libraries
- Matplotlib- figure anatomy, subplots, styling & saving publication-quality charts
- Seaborn- statistical plots, themes, heatmaps & pair plots
- Plotly & Plotly Express- interactive charts, animations & choropleth maps
- Folium- geospatial visualisation & interactive maps
- Dashboard frameworks- Dash & Streamlit for deploying interactive data apps
15. Power BI for Business Intelligence
- Power BI Desktop: data import, Power Query Editor & data modelling
- DAX fundamentals: calculated columns, measures & time intelligence functions
- Building interactive reports: slicers, filters, drill-through & bookmarks
- Power BI Service: publishing, sharing, workspaces & row-level security
- Power BI best practices: report design, performance optimisation & governance
16. Tableau for Visual Analytics
- Tableau Desktop interface: dimensions, measures, shelves & marks card
- Calculated fields, table calculations & Level of Detail (LOD) expressions
- Building dashboards: layout containers, device layouts & actions
- Tableau Story Points: guided narrative presentations with data
- Tableau Public & Tableau Server: publishing & sharing visualizations
17. Data Storytelling & Communication
- The narrative arc: situation, complication, resolution & recommendation
- Structuring an executive summary: headline insight, evidence & next steps
- Slide design for data presentations: avoiding death by bullet point
- Presenting to technical vs non-technical stakeholders- adapting language
- Handling questions & pushback- defending your analysis with confidence
18. Introduction to Machine Learning
- Supervised vs unsupervised learning- classification, regression & clustering
- Linear & logistic regression: building and evaluating predictive models
- Decision trees & random forests: ensemble methods for classification
- Model evaluation metrics: accuracy, precision, recall, F1 & RMSE
- scikit-learn workflow: train/test split, pipelines, cross-validation & hyperparameter tuning
19. Time Series Analysis
- Time series components: trend, seasonality, cyclicity & noise
- Moving averages & exponential smoothing: simple forecasting techniques
- ARIMA & SARIMA models: statistical forecasting with Python
- Prophet: Facebook's open-source forecasting library for business data
- Visualizing time series: decomposition plots, autocorrelation & forecast bands
20. AI & Automation for Analysts
- ChatGPT & Claude for data analysis: prompt engineering for SQL, Python & insights
- GitHub Copilot- AI-assisted code writing for faster data pipelines
- AI-powered BI tools- Microsoft Copilot in Power BI & Tableau Pulse
- Automating reports- Python scripts, Task Scheduler & email delivery
- Ethical AI in analytics- bias detection, fairness & explainability
21. Cloud & Big Data Fundamentals
- Cloud analytics platforms: Google BigQuery, AWS Athena & Azure Synapse
- Introduction to Apache Spark: distributed data processing concepts
- Data lakehouse architecture: Delta Lake, Apache Iceberg & Databricks
- ETL vs ELT pipelines: building data workflows with dbt & Airflow
- Real-time analytics: streaming data with Kafka & Google Pub/Sub
22. Portfolio Development & Career Readiness
- Building a data portfolio: structuring case studies with problem, process & insight
- GitHub for analysts: uploading notebooks, datasets & README documentation
- Kaggle competitions: participating, submitting & building a public profile
- Preparing for data analyst interviews: SQL tests, case studies & take-home projects
- LinkedIn optimization, resume writing & networking in the data community
23. Capstone Projects
- Retail sales analysis: SQL + Python + Power BI dashboard with trend & product insights
- Customer churn prediction: EDA, feature engineering, logistic regression & recommendations
- COVID-19 global data story: Pandas, Plotly & Streamlit interactive web app
- Financial dashboard: stock price time series, forecasting & Tableau visualisation
- HR analytics capstone: attrition analysis, workforce insights & executive presentation
Quick Overview
- Category
- Data Science, AI & Emerging Technologies
- Duration
- 2 Months
- Price
- Rs. 15,000.00
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