Data visualization tools

Data visualization tools provide you with an easier way to create visual representations of large data sets. When dealing with data sets that include bunches of data points, automating the process of creating a visualization, at least in part, makes your job significantly easier.

The best data visualization tools on the market should have one important thing in common. It is their ease of use. The best tools can also handle huge sets of data. And the last but not least, they can output an array of different chart, graph, and map types.

There are hundreds, of applications, tools, and scripts available to create visualizations of large data sets. Many are very basic and have a lot of overlapping features.

- Tableau (and Tableau Public
Hundreds of data import options. Mapping capability. Free public version available. Lots of video tutorials to walk you through how to use Tableau.

- Infogram
Tiered pricing, including a free plan with basic features. Includes 35+ chart types and 550+ map types. Drag and drop editor. API for importing additional data sources.

- ChartBlocks
Free and reasonably priced paid plans are available. Easy to use wizard for importing the necessary data.

- Datawrapper
Specifically designed for newsroom data visualization. Free plan is a good fit for smaller sites. Tool includes a built-in color blindness checker.

- D3.js
A JavaScript library for manipulating documents using data. Very powerful and customizable. Huge number of chart types possible. A focus on web standards. Tools available to let non-programmers create visualizations. Free and open source.

- Looker Studio (Google Data Studio)
Free data visualization tool that is specifically for creating interactive charts for embedding online. Easily access a wide variety of data.

- FusionCharts
A JavaScript-based option for creating web and mobile dashboards. Huge number of chart and map format options. More features than most other visualization tools. Integrates with a number of different frameworks and programming languages.

- Chart.js
A simple but flexible JavaScript charting library. Free and open source. Responsive and cross-browser compatible output.

- Grafana
Open source, with free and paid options available. Large selection of data sources available. Variety of chart types available. Makes creating dynamic dashboards simple. Can work with mixed data feeds.

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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