What are Data Analysis Tools and Techniques
                     What are Data 
Analysis Tools and 
Techniques?
SRI MOOKAMBIKA INFOSOLUTIONS PVT LTD
Data analysis tools - Snapshot
Statistical analysis tools
Features to watch:
1. An ecosystem of more than 10000 
packages and extensions for various 
types of data analysis
2. Statistical analysis, modeling, and 
hypothesis testing (e.g. analysis of 
variance, t test, etc.)
3. Active and communicative 
community of researches, 
statisticians, and scientists
Business intelligence tools
 Features to watch:
1. Visual drag-and-drop interface with 
an easy switch to advanced SQL 
mode
2. Powerful predictive analytics 
features and interactive charts and 
dashboards
3. Intelligent alarms that are triggered 
as soon as an anomaly occurs
General purpose programming language 
 Features to watch:
1. An open-source solution that has 
simple coding processes and syntax 
so it’s fairly easy to learn
2. Integration with other languages such 
as C/C++, Java, PHP, C#, etc.
3. Advanced analysis processes through 
machine learning and text mining
SQL Consoles
 Features to watch:
1. A unified visual tool for data modelling, 
SQL development, administration, 
backup, etc.
2. Instant access to database schema and 
objects via the Object Browser
3. SQL Editor that offers colour syntax 
highlighting, reuse of SQL snippets, and 
execution history
Data modeling tools
 Features to watch:
1. Automated data model generation to 
increase productivity in analytical 
processes
2. Single interface no matter the location 
or the type of the data
3. 7 different versions of the solution you 
can choose from and adjust based on 
your business needs
Standalone predictive analytics tools
 Features to watch:
1. Automatic forecasting for a large number 
of entities or products, including 
hierarchical forecasting
2. Scalability and modeling by combining 2 
or more models and creating an ensemble
3. An unlimited model repository that 
includes time series and casual methods 
such as ARIMA and ARIMAX
ETL tools
 Features to watch:
1. Collecting and transforming data 
through data preparation, integration, 
cloud pipeline designer
2. Data governance feature to build a data 
hub and resolve any issues in data 
quality
3. Sharing data through comprehensive 
deliveries via APIs
Unified data analytics engines
 Features to watch:
1. High performance: Large-scale data 
processing
2. A large ecosystem of data frames, 
streaming, machine learning, and graph 
computation
3. A collection of over 100 operators for 
transforming and operating on large 
scale data
Spreadsheet applications
 Features to watch:
1. Part of the Microsoft Office family, 
hence, it’s compatible with other 
Microsoft applications
2. Pivot tables and building complex 
equations through designated rows 
and columns
3. Perfect for smaller analysis processes 
through workbooks and quick sharing
Industry-specific analytics tools
 Features to watch:
1. 4 main experience features: 
customer, brand, employee, and 
product
2. Additional research services by their 
in-house experts
3. Advanced statistical analysis with 
their Stats iQ analysis tool
Data visualization tools & platforms
 Features to watch:
1. Interactive JavaScript engine for 
charts used in web and mobile 
projects
2. Designed mostly for a technical-
based audience (developers)
3. WebGL-powered boost module to 
render millions of data points 
directly in the browser
Data science platforms
 Features to watch:
1. A comprehensive data science and machine 
learning platform with more than 1500 
algorithms
2. Possible to integrate with Python and R as 
well as support for database connections (e.g. 
Oracle)
3. Advanced analytics features for descriptive 
and prescriptive analytics
Data analysis techniques - 
Snapshot
Text analysis
 Text Analysis is also referred to as 
data mining
Statistical analysis
 Statistical Analysis shows "What 
happen?" by using past data in the 
form of dashboards
Diagnostic analysis
 Diagnostic analysis shows “Why did 
it happen?" by finding the cause 
from the insight found in statistical 
analysis
Predictive analysis
Predictive Analysis shows "what is 
likely to happen" by using previous 
data
Prescriptive analysis
Prescriptive analysis combines the 
insight from all previous analysis to 
determine which action to take in a 
current problem or decision.
Data analysis process - 
Snapshot
Data requirement gathering
Data collection
Data cleaning
Data analysis
Data interpretation
Data visualization
Thank you
References:
1. Guru99.com
2.   Datapine.com
Looking for a professional data analysis 
services?
 Contact Us:
 SMI – Enterprise Data analysis services
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Madurai – 625001, TN, India
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