Analyst in Action

Are you spending a big chunk of your time cleaning the data in excel? If yes then this blog is for you. All the cleaning and transforming of data can be achieved in Microsoft Power BI. Power BI offers a wide variety of options to clean your data. Power BI comes with a power query editor where you can play around with the data and I consider it as an ETL tool where you can transform your data according to the needs. We have discussed the data cleaning process in the last blog and in today's blog the focus will be on transforming data and the features that the power query editor offers. These steps are solely subjective according to the data type and to your needs. I am highlighting 6 steps to transforming the data. I am working on the sample superstore data. So let's get started!!!! Removing columns and rows-  We are aware that the datasets can have a lot of unwanted columns and rows. To get better insights you need to get rid of it. To do so you need to select the column. If y

Want to retain your customers then you are at the right place. It can be achieved by Cohort Analysis but the question is how this analysis helps in achieving it. Let's get started by understanding it. Cohort Analysis is used by the analyst to determine the user engagement of customers over time. To be more precise it helps to know whether our user engagement is increasing or decreasing over a period of time. It is done to know the level of user engagement and the customer churning rate in any organization.  In marketing, the cohort is referred to as the group which possesses similar characteristics or traits, and this analysis helps us to divide customers based on their behavior. To get a better understanding of your audience you need to perform such analysis on regular basis. It can be performed in almost every analytical tool mostly organizations prefer Google Analytics but I have done it in Tableau.  This analysis is done in Tableau. Let me walk through it and explain what it im

Have you heard of EDA? Let's, deep dive into Exploratory data analysis (EDA). It is the analysis used by a data scientist to spot the patterns, trends, and hypotheses to manipulate the data for getting your questions answered. It is a vital step to get the most out of any data and also it is critical because it provides a better picture of the relationship between different variables. EDA is mainly practiced to get a better understanding of your data before making any assumptions. It answers some basic questions related to data such as the confidence interval, standard deviation, and relationship that exist between different variables. EDA is a soul for any data analysis. According to John Tukey “ Exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there. ” Source There are mainly 4 types of EDA- Univariate non-graphical- It only deals with a single variable so it i

  Source SQL (Structured Query Language) plays a pivotal role in data analysis. The main purpose to create SQL is to help in managing data stored in Relational Database Management System (RDBMS). In elementary terms, it is the language that is used to interact with the database. You must be aware of the RDBMS which uses SQL extensively such as Microsoft SQL Server, Access, Oracle. SQL and SQL lite both follow the RDBMS model. Let's check out where these two differ. So let's understand what is SQLite ? SQLite act as a library for software. As compared to SQL it does not require any server to function. Generally, RDBMS requires a server to function. SQLite functions by combining the database to the application. The application help in interacting with the database. There is this term " self-contained " which is associated with SQLite which means it can operate on its own without the support of the operating system. This feature is the highlight of SQLite and makes it av

Do you want better control and flexibility in your business operations then this blog has a lot to offer you? All these can be achieved by a simple methodology called agile methodology. So what this uncanny term is? This methodology is used to define the development of software that is used in Business Intelligence and analytical tools. It is also called "Agile business intelligence methodology". Defining the development of software will allow any organization to work in a controlled and flexible manner. The umbrella of agile business has a lot of small sections but the main goal is to help businesses to adapt quickly to the market needs which is continuously evolving. The main advantage of such methodology is that in traditional methodology departments are focused on their tasks and goals and they won't inhibit the work of each other or there is no communication between the departments but this methodology allows that communication which makes the business more flexible

Source Want to improve your business performance? You heard it right it can be achieved by implementing Business Intelligence tools in your business operations and utilize  big data  in effective decision making. Business Intelligence is a process that can help executives and managers to make informed decisions by analyzing data and by developing actionable insights out of it. It is one of the major concepts which can help your business grow.  There are several Business Intelligence tools available it all depends on your needs. Tableau, Microsoft Power BI, SQL, QlikView, and many more. All of them perform in a similar way but there are minute differences between them when it comes to their functionality. SAS ( SAS BI ): Business Intelligence: It is a great business intelligence platform that also offers advanced predictive analytics which can help to predict future business trends. There are several APIs offered by SAS to customize your analysis and reporting. QlikSense ( Qlik BI ): It