Post #1697

Complete Syllabus for Data Analytics interview: SQL: 1. Basic - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING - Basic JOINS (INNER, LEFT, RIGHT, FULL) - Creating and using simple databases and tables 2. Intermediate - Aggregate functions (COUNT, SUM, AVG, MAX, MIN) - Subqueries and nested queries - Common Table Expressions (WITH clause) - CASE statements for conditional logic in queries 3. Advanced - Advanced JOIN techniques (self-join, non-equi join) - Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag) - optimization with indexing - Data manipulation (INSERT, UPDATE, DELETE) Python: 1. Basic - Syntax, variables, data types (integers, floats, strings, booleans) - Control structures (if-else, for and while loops) - Basic data structures (lists, dictionaries, sets, tuples) - Functions, lambda functions, error handling (try-except) - Modules and packages 2. Pandas & Numpy - Creating and manipulating DataFrames and Series - Indexing, selecting, and filtering data - Handling missing data (fillna, dropna) - Data aggregation with groupby, summarizing data - Merging, joining, and concatenating datasets 3. Basic Visualization - Basic plotting with Matplotlib (line plots, bar plots, histograms) - Visualization with Seaborn (scatter plots, box plots, pair plots) - Customizing plots (sizes, labels, legends, color palettes) - Introduction to interactive visualizations (e.g., Plotly) Excel: 1. Basic - Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.) - Introduction to charts and basic data visualization - Data sorting and filtering - Conditional formatting 2. Intermediate - Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF) - PivotTables and PivotCharts for summarizing data - Data validation tools - What-if analysis tools (Data Tables, Goal Seek) 3. Advanced - Array formulas and advanced functions - Data Model & Power Pivot - Advanced Filter - Slicers and Timelines in Pivot Tables - Dynamic charts and interactive dashboards Power BI: 1. Data Modeling - Importing data from various sources - Creating and managing relationships between different datasets - Data modeling basics (star schema, snowflake schema) 2. Data Transformation - Using Power Query for data cleaning and transformation - Advanced data shaping techniques - Calculated columns and measures using DAX 3. Data Visualization and Reporting - Creating interactive reports and dashboards - Visualizations (bar, line, pie charts, maps) - Publishing and sharing reports, scheduling data refreshes Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.