Introduction to Data Science and Analytics

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Data Science is a multi-disciplinary field that makes use of mathematical methods, processes, systems, and algorithms to extract knowledge or business intelligence from structured or unstructured data.

Data is everywhere nowadays, from social media platforms to banking systems. And for businesses, it is quite essential to understand the data and gain business intelligence to improve the overall customer experience. The concepts, process, and methods with which the knowledge is extracted from the structured and unstructured data is called Data Science.

It is a field of study in which computers/systems gain the capability to learn itself through programming. Machine Learning makes use of past data to do predictive analysis and learn itself to predict future outcomes and take appropriate actions.

Stages of Data Analytics

1. Descriptive Analytics: Inferences from the past data. In this, we analyze what has happened in the past. For example, for each product, how many sales were made, how many employees joined in the last quarter, and what is their retention rate.

It helps business analysts to find where exactly the problem is.

Tools for descriptive analysis: SAS (It can be used on all stages of analytics), Tableau, SAP, Power BI, etc.

Microsoft Excel is also another tool that we can use for data handling, reporting, and creating business dashboards. In our data analytics journey, we will first start with MS Excel. After that, we will study SQL and then use Tableau for BI.

Apart from that, I will also be sharing VBA (Visual Basic for Applications) tutorials to write macros at the backend of Excel for tasks automation.

2. Predictive Analysis: In this Analytics stage, we answer business problems – what could happen in the future based on historical data. The historical data used for predictive analysis is obtained from the descriptive analysis.

For example, you apply for a credit card. The first business problem for the analytical engine is to decide whether you are eligible or not based on multiple parameters.

The second business problem for the analytical engine, in this case, is to decide the credit limit for the eligible person, which again depends on different variables like paying capacity, previous payment history, etc. As the value of these variables differs from person to person, the credit limit is predicted accordingly.

Without functional descriptive analysis, you can’t do proper predictive analysis.

3. Prescriptive Analysis: While descriptive analysis answers what has happened in the past, the predictive analysis explains what can happen in the future, and in the prescriptive analysis, the best outcome is chosen from the set of multiple possible choices.

For example, Google Maps uses prescriptive analysis to show you the optimal route from your source destination to the final destination.

Another example is YouTube, which uses predictive analysis to show recommendations. But, it can only show only one video for autoplay, and for that, it uses predictive analysis based on various factors like your watch history, search history, etc.

Prescriptive analytics is not possible without predictive analysis.

Road map for Data Science and Machine Learning course

Module 1: Introduction to Data Analysis and Visualization with Excel.

Module 2: MS SQL – RDBMS Concepts and SQL.

Module 3: Tableau – Introduction to Tableau, advance reports, interactive dashboards, etc.

Module 4: Python – Basics, Statistical analysis in Python, etc.

Module 5: Python – Machine Learning using Python with applications.

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So stay tuned for more articles and tutorials related to Data Science and Machine Learning. The frequency would be one post per week, or I will increase the number of posts as per my schedule. In case you want to be in touch to ask questions or any other reason, please feel free to connect with me on any of these social media platforms.

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