If you are a person that is intrigued with data science, Here are the things you need to know.
First, What is data science? Data science combines multiple fields, including statistics, scientific methods, artificial intelligence (AI), and data analysis, to extract value from data. Why is this important? can help you to detect fraud using advanced machine learning algorithms and empowers better business decision-making through interpreting, modeling, and deployment.
Understanding data science can be a little overwhelming. So to avoid confusion, there are certain processes we should follow
PROCESS ONE: ASKING QUESTIONS
Of course, to solve some problems we should always analyze and prepare questions. And make sure this is an actionable business question. Asking the real business question helps address these issues. It helps align the stakeholders and the analysts to the main problem so that there is clarity between them. Here are some examples: What is the sales process right now? How to attract users? Who are the customers?
PROCESS TWO: COLLECTING DATA
Once the problem is identified, you will need the data to give you the information you need to reverse the problem with a solution. Part of this process involves thinking about what data is needed and finding ways to get to that data, whether that be by querying an internal database or purchasing an external data set. Think of this process as organizing and rearranging data, deleting what is no longer needed, replacing what was lost, and standardizing the format across all collected data.
PROCESS THREE: EXPLORE DATA
During exploration, raw data is typically reviewed using a combination of manual workflows and automated data exploration techniques to visually examine data sets, look for similarities, patterns, and outliers, and identify relationships between various variables. This is sometimes called exploratory data analysis, which is a statistical technique used to analyze raw data sets for their general characteristics. This is also known as the most time-consuming part as it involves filling in missing data.
PROCESS FOUR: MODEL THE DATA
In this step, you need to develop ideas that can help identify hidden patterns and insights. You need to find more interesting patterns in the data, such as why sales of a particular product or service are increasing or decreasing. You need to analyze or pay attention to this type of data more thoroughly. This is one of the most important steps in the data science process. Data modeling employs standardized schemes and formal techniques. This provides a common, consistent, and predictable way of defining and managing data resources across an organization, or even beyond. Ideally, data models are living documents that evolve along with changing business needs. They play an important role in supporting business processes and planning IT architecture and strategy. Data models can be shared with vendors, partners, and/or industry peers.
PROCESS FIVE: COMMUNICATE THE RESULTS
As the last step, it is important to convey your knowledge and ideas to the sales manager and make him understand their meaning. It will help you if you communicate properly to solve the problem that has been raised. Correct communication leads to action. On the contrary, improper contact can lead to inaction. You should combine the data you have collected and your knowledge with the knowledge of the sales manager so that he can better understand it. You can start by explaining why a product underperformed and why certain demographics weren't interested in the sales pitch. After you present the problem, you can proceed to resolve it. You need to create a strong narrative with clarity and strong objectives.
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