Types of data analysis, Roles and Tasks


Overview of data analysis

The world becomes more data-driven, storytelling through data analysis is becoming a vital component and aspect of large and small businesses. It is the reason that organizations continue to hire data analysts.

The process of data analysis focuses on the tasks of cleaning, modeling, and visualizing data, the concept of data analysis and its importance to business should not be understated. To analyze data, core components of analytics are divided into the following categories:

  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive
  • Cognitive

Descriptive analytics

Descriptive analytics helps to answer questions about what has happened based on historical data. Descriptive analytics techniques summarize large datasets to describe outcomes to stakeholders.

Diagnostic analytics

Diagnostic analytics helps to answer questions about why events happened. Diagnostic analytics techniques supplement basic descriptive analytics, and they use the findings from descriptive analytics to discover the cause of these events.

Predictive analytics

Predictive analytics help answer questions about what will happen in the future. Predictive analytics techniques use historical data to identify trends and determine if they're likely to recur. Predictive analytical tools provide valuable insight into what might happen in the future. Techniques include a variety of statistical and machine learning techniques such as neural networks, decision trees, and regression.

Prescriptive analytics

Prescriptive analytics help answer questions about which actions should be taken to achieve a goal or target. By using insights from predictive analytics, organizations can make data-driven decisions. 

Cognitive analytics

Cognitive analytics attempt to draw inferences from existing data and patterns, derive conclusions based on existing knowledge bases, and then add these findings back into the knowledge base for future inferences, a self-learning feedback loop. Cognitive analytics help you learn what might happen if circumstances change and determine how you might handle these situations.


Roles in data

  • Business analyst : Business analysts work with organizations to help them improve their processes and systems

  • Data analyst : The data analyst serves as a gatekeeper for an organization's data so stakeholders can understand data and use it to make strategic business decisions

  • Data engineer : Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. Data engineers are often responsible for building algorithms to help give easier access to raw data, but to do this, they need to understand company's or client's objectives.

  • Data scientist : A data scientist's role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.

  • Database administrator : Database administrators (DBAs) use specialized software to store and organize data. The role may include capacity planning, installation, configuration, database design, migration, performance monitoring, security, troubleshooting, as well as backup and data recovery.


Tasks of data analyst


1. Prepare

2. Model

3. Visualize

4. Analyze

5. Manage

Conclusion 

In this article I have explored different types of analytics and role in data and task performed by data analyst.


. 

Comments

Post a Comment

Popular posts from this blog

Use DAX iterator functions in Power BI Desktop models

Data Analysis Expressions (DAX) include a set of functions known as  iterator functions . Iterator functions enumerate all rows of a given table and evaluate a given expression for each row. They provide you with flexibility and control over how your model calculations will summarize data. Some DAX Functions are Complex summarization 1. SUMX e.g. : 1.  Revenue = SUMX (Sales, Sales[Sales Amount]) 2. Revenue = SUMX (Sales, Sales[Order Quantity] * Sales[Unit Price] * ( 1 - Sales[Unit Price Discount Pct.])) 3. Discount = SUMX ( Sales, Sales[Order Quantity] *( RELATED ('Product'[List Price]) - Sales[Unit Price] ) ) 2. AVERAGEX e.g :1. Revenue Avg = AVERAGEX ( Sales, Sales[Order Quantity] * Sales[Unit Price] * ( 1 - Sales[Unit Price Discount Pct.]) ) 2. Revenue Avg Order = AVERAGEX ( VALUES ('Sales Order'[Sales Order]), [Revenue] ) 3. RANKX The  RANKX  DAX function is a special iterator function you can use to calculate ranks. Its syntax is as follows: RANKX (<...
Read more

Different charts in Power BI

Image
  An overview of Chart Types in Power BI As everyone might be aware, Power BI is one of the SaaS offerings from Microsoft in the field of BI and Analytics. It provides a very intuitive way for non-technical users to do analyses and create reports on their own without having to rely on a technical team. One of the main purposes of Microsoft behind Power BI is to promote self-service BI and it is one of the BI tools in the market with a comparatively lower learning curve as there are many similarities compared to Microsoft Excel.  In this blog, I am going to give an overview of the most widely used chart types in Power BI.   Let's look at some of the most frequently used chart types in Power BI Bar and column charts Stacked Bar and Stacked Column Chart Clustered Bar and Clustered Column Chart Line Chart Area Chart Stacked Area Chart Line and Stacked Column Chart/ Combo Chart Line and Clustered Column Chart/ Combo Chart Ribbon Chart Waterfall Chart Funnel Chart Scatter Chart...
Read more

Modify DAX filter context in Power BI Desktop models

Summary CALCULATE  DAX function to modify filter context in the formulas. The syntax for the CALCULATE function is as follows: CALCULATE (<expression>, [[<filter1>], <filter2>]…) I learned about filter context. And also learned why and how to work with the CALCULATE function to modify filter context by passing in filters. These filters can add or overwrite the filter context, and they can modify filter context when we are passing in special functions like REMOVEFILTERS or KEEPFILTERS. Additionally, I learned that the CALCULATE function can transition row context to filter context, which can be required when we are using measure expressions in calculated columns or iterator functions.
Read more