Analytics | ML

Data Analytics:

Analytics is a discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making.

It is the process of examining data sets by applying the qualitative analysis, statistical methods and ML algorithms in order to draw conclusions about the information they contain.

Extract actionable insights from business data to improvise the business.
Domain specific data analytics is called Business analytics

Advanced Analytics:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/advanced-analytics

Elements in Data analytics:
Datawarehouse
EDA - Statistical and analytical
Data mining - patterns and correlations
Machine Learning
Analytical Programming skills - R, Python, SAS
Reporting with visualization
SQL coding
Spreadsheet knowledge

Statistical Concepts:
Mean
Median
Mode
Variance
Covariance
Standard deviation
Correlation
Regression
Rsquare
Information Value
etc.. etc..

Analytical tools:
R/Python Programming. - Open source analytical tools
SAS
Tableau Public
Apache Spark
Excel
RapidMiner
KNIME
QlikView

Machine learning:
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

Machine learning (ML) is a category of algorithm, has ability to predict outcomes without being explicitly programmed.

ML is a concept or technique used to develop own algorithm or make use of predefined algorithm.

ML is predominantly used for data analytics for predictions and an approach to AI.

ML Methods:
Supervised Learning
Unsupervised Learning
Reinforcement Learning

Learning process:
Develop algorithms based on machine learning techniques
Use analytical tools/programming by applying statistical methods based on algorithms
Analytical program will produce optimized parameter or value, called model

Prediction process:
Apply model on test data

Applications of ML:

Why Business Analytics?

It should come as no surprise that business intelligence and data analytics is one of the fastest growing markets in the 2017 enterprise software landscape. Today’s businesses are growing increasingly digital and are capable of accurately measuring every aspect of their operations, from marketing to human resources analytics, in real-time.

However, data in its raw form is usually useless, and the driving force behind any data-driven organization is insights: conclusions drawn from the data, which can suggest a new course of action. To reach these insights, organizations must use business analytics tools and techniques to connect data from multiple sources, analyze the data, and communicate the results in ways that decision-makers can understand.

Typically, commerical organizations use business analytics in order to:

• Analyze data from multiple sources
• Use advanced analytics and statistics to find hidden patterns in large datasets
• Deseminate information to relevant stakeholders through interactive dashboards and reports
• Monitor KPIs and react to changing trends in real-time
• Justify and revise decisions based on up-to-date information

If your business is looking to achieve one or more of these goals, business analytics is the way to go. The level of investment in tools, technology and manpower should vary according to your needs – in some cases increasing your proficiency in Excel might suffice, while in others you might want to look at specialized solutions from business intelligence software vendors.

Use the resources available here to understand whether your business needs business analytics, build a business case for investing in analytics, and determining the complexity level of your data to decide on which method to use.

Business Analytics use cases:
Text analytics
HR analytics
Customer analytics
Health care analytics