Data Analyst

Module 1: Introduction to Data Analytics & Business Acumen

• What is Data Analytics?
• Definition, importance, and types of data analytics (descriptive, diagnostic, predictive, prescriptive).
• Role and responsibilities of a Data Analyst.
• Impact of data analytics on business outcomes.

• Data-Driven Decision Making
• Understanding business problems and framing analytical questions.
• Translating business needs into data requirements.

• Fundamentals of Statistics and Probability
• Descriptive Statistics: Measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation, range), skewness, kurtosis.
• Inferential Statistics: Sampling, Central Limit Theorem, hypothesis testing (t-tests, ANOVA, Chi-square), correlation, regression.
• Probability Distributions: Normal, binomial, Poisson distributions.

Data Manipulation & Management (Excel & SQL)

• Microsoft Excel (Basic to Advanced)
• Fundamentals: Data types, cell references, basic formulas (SUM, AVERAGE, COUNT).
• Data Cleaning & Preparation: Text functions, conditional formatting, data validation, removing duplicates, text to columns.
• Data Analysis with Excel: Pivot Tables (creating, modifying, grouping, filtering), charts (various types, formatting), slicers, lookup functions (VLOOKUP, HLOOKUP, INDEX-MATCH).
• Advanced Features: What-if analysis (Goal Seek, Scenario Manager), array formulas, basic macros (VBA introduction).

• SQL (Structured Query Language)
• Fundamentals: Introduction to relational databases, SQL syntax.
• Data Retrieval: SELECT, FROM, WHERE, ORDER BY, GROUP BY, HAVING.
• Filtering & Sorting Data: Operators, wildcards.
• Functions: Aggregate functions (COUNT, SUM, AVG, MIN, MAX), string functions, date functions, numerical functions.
• Joining Tables: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN.
• Subqueries: Correlated and non-correlated subqueries.
• Data Manipulation Language (DML): INSERT, UPDATE, DELETE.
• Data Definition Language (DDL): CREATE TABLE, ALTER TABLE, DROP TABLE.
• Window Functions: ROW_NUMBER, RANK, LEAD, LAG.
• Common Table Expressions (CTEs).

Module 3: Data Visualization & Business Intelligence Tools

• Microsoft Power BI:
• Introduction to Power BI: Power BI Desktop, Power Query Editor.
• Data Connection & Transformation: Connecting to diverse data sources, data cleaning and shaping in Power Query.
• Data Modeling: Relationships, cardinality, cross-filter direction.
• DAX (Data Analysis Expressions): Calculated columns, measures, time intelligence functions.
• Visualizations & Dashboards: Creating various interactive visualizations, formatting, creating dynamic reports and dashboards.
• Power BI Service: Publishing reports, sharing, collaboration, gateways.

• Tableau
• Introduction to Tableau: Interface, connecting to various data sources.
• Creating Visualizations: Bar charts, line charts, scatter plots, pie charts, heatmaps, treemaps, geographic maps, dual-axis charts.
• Calculated Fields & Parameters: Creating new metrics and interactive elements.
• Dashboards & Stories: Designing interactive dashboards, adding filters and actions, creating data narratives.
• Data Blending & Joins in Tableau.

Module 4: Advanced Topics & Capstone Project

• Data Storytelling & Communication:
• Presenting insights effectively to non-technical stakeholders.
• Crafting compelling narratives from data.
• Designing effective dashboards and reports for decision-making.

• Data Cleaning and Imputation Techniques (Deeper Dive)
• Strategies for handling missing data.
• Outlier detection and treatment.

• Cloud Platforms for Data (Conceptual)
• Brief overview of data storage and processing services on AWS, Azure, or Google Cloud Platform (e.g., S3, BigQuery).

• Capstone Project
• Apply all learned skills to a real-world dataset.
• Define a problem, collect/clean data, analyze, visualize, and present findings.
• Develop a portfolio-ready project.

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