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Introduction to 
Data Analytics

Data analytics is defined as a series of techniques aimed at extracting relevant and valuable information from extensive and diverse sets of data gathered from different sources and varying in size. It is also a broad term that encompasses many diverse types of data analysis. The process of analyzing raw datasets to extract a conclusion based on the information they collect is known as data analytics. Applications operating will be used on machine learning algorithms, simulation, and automated systems by data analytics processes and techniques. It helps the organizations to understand their clients better and analyzes their promotional campaigns, personalized content, creates content strategies, and upgrades their products.

There is a difference between data analytics, data analysis, and data science. Data analytics deals with a piece of information, dashboards, and reporting. Data analysis focuses on processes and functions. Data science includes data analysis and also the elements of data cleaning and preparation for further investigations.

Big data analytics is the process of uncovering trends, patterns, and correlations in large amounts of raw data that helps to make data-informed decisions.

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Use of Big Data in Data Analytics :

• Volume ( Size of data )

• Velocity ( The Speed at which Data is generated )

• Variety ( Different type of Data )

• Veracity ( Data accuracy )

• Value ( Useful Data )

• Validity ( Data quality Governance )

• Variability ( Dynamics, Evolving Behavior in Data Source )

• Venue ( Distributed Data from Multiple Platform )

• Vocabulary ( Data Models, Semantics that describe data Structure )

• Vagueness ( Confusion over Meaning of BigData and Tools used )

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There are 4 types of data analytics. Descriptive analytics shows the happenings over time. For example, it shows whether the current monthly sales are better than the last one. The second will be predictive analytics which focuses on the events that are expected to occur in the immediate future. It also seeks answers to questions like, what happened to last month's sales during the winter season? . Prescriptive analytics specifies a plan of action. If the chance of hot summer is higher after calculating the average of 5 weather models, raincoats must be considered to increase the production compared to the umbrella. Diagnostic analytics clearly shows the reason for the event to occur. This requires hypothesizing. It also involves a much diverse dataset. Questions for example, “Did the increment in selling price affect sales?" will be used to examine the data by answering the questions.

The first step of process data analytics will be determining the criteria for grouping the data by dividing by a range of different criteria such as age, population, or sex. The second step will be collecting the data through multiple sources such as computers, and sources from the community. The third step will be organizing the data to examine. Spreadsheets or other types of software that enable statistical data can be used for data organization. The last step will be cleaning the data to make sure there is no mistake and help to fix or eliminate mistakes before sending it to the data analyst for analysis purposes. It also will be reviewed to make sure it is complete.

In the business sector, data analysts study the information and clean it from noise. They also assess the quality of data and its sources before developing the scenarios for automation and machine learning. Their last role will be overseeing the proceedings. Data analytics is important in the business sector because it helps to optimize the company’s performance. By implementing it, companies can reduce costs in storing a large amount of data.

There are a few differences between the data scientist and data analyst. Data scientist deals with various data operations while data analyst deals with data cleaning, transforming and generating inferences based on the data. Data scientists are involved in multiple underlying structured and unstructured data procedures while data analysts are involved in limited small structured data and static inferences. The data scientist knows mathematics, statistics & machine learning algorithms while the data analyst has problem-solving skills and basic knowledge of statistics. Data scientists know use SAS, Phyton, R, TensorFlow, Hadoop and Spark while data analysts are proficient in Excel, SQL, R in some cases and Tableau.

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Microsoft Power BI is not coding but just a device for connecting. Microsoft Power BI also :

• Connect to all the data you care about through 1000 pre-built APIs for information sources like Excel workbook, SQL server and Dataverse.

• Predict future performance machine learning capabilities.

• Analyze, combine, and model advanced datasets.

• Easily and firmly distribute information and insights outside your organization with embedded and extended analytics.

• Build apps to customize information exploration for your groups or your customers.

• Set alerts to simply track performance goals and determine anomalies.

• With Microsoft Power BI all of your information is automatically offered within the app with no extra development prices or maintenance needed.

There are three sections which are report, data side and modeling side. The first step you should do to start your Microsoft Power BI project is to import the data source to the Power BI desktop. Then, start reporting and interpret your data. And the last step is the modeling part which can create relationships between tables to tables such as (one to many, many to one, one to one and many to many). 

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Lastly, Microsoft Power BI is user-friendly and it is different from Microsoft Excel because the usage of Microsoft Power BI is more to do with automated tools. Plus, you don't need to do another Excel to add any current data that you want to add to your previous project. It is an automatic ad with just one click. Microsoft Power BI is also a good platform to transform the data into a computer form and also has its program language named-data analysis expression that will calculate all the data to visualize.