Data Visualization
Kieran Healy, 2019

Scientific Visualisation
Nicolai P. Rougier, 2021

Data Analyst Interview Questions

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Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go:

1. Computer vision
2. Natural language processing.

I outlined a roadmap for computer vision I believe many beginners will find helpful.

Artificial Intelligence

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.

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