Describe How The Data Life Cycle Differs From Data Analysis.

Describe How The Data Life Cycle Differs From Data Analysis. – As an aspiring data scientist, you may be interested in understanding how the data science project lifecycle works so that it is easier for you to implement your individual projects in a similar pattern. Today, we will basically discuss the step-by-step process of implementing any data science project in a real-world scenario.

In simple words, a data science lifecycle is nothing but a repeatable set of steps you need to take to complete and deliver a project/product to your customer. While data science projects and the teams involved in deploying and developing the models are different, every data science lifecycle will be slightly different at every other company. However, most data science projects follow a somewhat similar process.

Describe How The Data Life Cycle Differs From Data Analysis.

To start and finish a data science-based project, we need to understand the different roles and responsibilities of the people involved in building and developing the project. Let’s take a look at the employees involved in a typical data science project:

Foundations: Data, Data, Everywhere Course (google)

Now that we have an idea of ​​who is involved in a typical business project, let’s understand what a data science project is and how we define the data science project lifecycle in a real-world scenario like a project identifier. fake news.

In a normal case, a Data Science project contains data as its main element. Without any data, we will not be able to make any analysis or predict any outcome, as we are looking at something unknown. So before we start any data science project that we get from customers or stakeholders, we must first understand the fundamental problem statement presented by them. Once we understand the business problem, we need to gather relevant data that will help us solve the use case. However, many questions arise for beginners such as:

So many questions, but the answers may vary from person to person. So, to address all these concerns at once, we have a predefined flow called Data Science Project Lifecycle. The process is quite simple, in which the company must first collect data, perform data cleaning, perform EDA to extract relevant features, prepare the data by performing feature engineering and feature sizing. In the second phase, the model is built and deployed after proper evaluation. This entire life cycle is not a one-man job, so you need the whole team to work together to get the job done, achieving the required amount of efficiency for the project.

The globally accepted framework for solving any kind of analytical problem is widely known as the Cross Industry Standard Process for Data Mining or abbreviated as the CRISP-DM framework.

Stages Of The Project Management Life Cycle

To build a successful business model it is very important that you first understand the business problem that the customer faces. Suppose he wants to predict the churn rate for his retail business. First you may want to understand your business, its requirements and what you really want to achieve with forecasting. In such cases, it is important to consult with experts in the field and ultimately understand the underlying issues that are present in the system. A business analyst is usually responsible for gathering the necessary details from the client and forwarding the data to the team of data scientists for further speculation. Even a small mistake in defining the problem and understanding the requirement can be very decisive for the project, so it must be done with maximum precision.

After asking the necessary questions to the company’s stakeholders or customers, we move on to the next process known as data collection.

Once we have clarity on the problem statement, we need to gather relevant data to break the problem down into small components.

A data science project begins by identifying various data sources, which may include web server logs, social media posts, data from digital libraries such as US Census datasets, accessed from web resources via APIs, web scraping, or information already present in an Excel spreadsheet. Data collection involves obtaining information from known internal and external sources that can help solve the business problem.

The Software Development Life Cycle (sdlc): 7 Phases And 5 Models

Typically, the data analyst team is responsible for data collection. They need to find appropriate ways to get data and collate it to get the desired results.

After collecting the data from the relevant sources, we need to move on to the preparation of the data. This step helps us better understand the data and prepares it for further evaluation.

Also, this phase is known as Data Cleansing or Data Organizing. It includes steps such as selecting relevant data, combining them by shuffling data sets, cleaning them, handling missing values ​​by removing them or inserting them with relevant data, handling incorrect data removing them, as well as checking and dealing with outliers. Using feature engineering, you can create new data and extract new features from existing ones. Format the data according to the desired structure and delete any unnecessary columns or functions. Data preparation is the most time-consuming process, accounting for up to 90% of the total project duration, and is the most important step in the entire life cycle.

Exploratory data analysis (EDA) is critical at this point, because summarizing clean data allows for the identification of structure, outliers, anomalies, and trends in the data. These insights can help identify the optimal feature set, an algorithm to use for model creation, and model building.

Product Life Cycle

In most cases of data analysis, data modeling is considered the core process. In this data modeling process, we take the prepared data as input and with that we try to prepare the desired output.

First, we tend to choose the appropriate type of model that would be applied to obtain results, whether the problem is a regression or classification problem or a clustering-based problem. Depending on the type of data received, we choose the appropriate machine learning algorithm that is most suitable for the model. Once this is done, we need to adjust the hyperparameters of the selected models to obtain a favorable result.

Finally, we tend to evaluate the model by testing accuracy and significance. In addition to this design, we must ensure that there is a correct balance between specificity and generality, i.e. the model created must be unbiased.

Before deploying the model, we must ensure that we have chosen the right solution after rigorous evaluation. It is then set to the desired channel and format. This is of course the last step in the life cycle of data science projects. Exercise extreme caution before performing each lifecycle step to avoid unwanted errors. For example, if you choose the wrong machine learning algorithm for data modeling, you will not achieve the desired accuracy and it will be difficult to get project approval from stakeholders. If your data is not properly cleaned, you will have to deal with missing values ​​or noise present in the data set later. Therefore, to ensure that the model is properly deployed and accepted in the real world as an ideal use case, you will need to do rigorous testing at every step.

Steps In The Data Life Cycle

All the steps mentioned above are equally applicable to both beginners and experienced data science professionals. As a beginner, your job is to learn the process first, so you should practice and deploy smaller projects like fake news detector, titanic dataset, etc. You can refer to portals like, to get the dataset and start working on it.

Fortunately for beginners, these portals have already deleted most of the data, so continuing with the next steps will be quite easy. However, in the real world, you need to get not just any data set, but data that can meet the requirements of your data science project. So initially your task is to first go through all the steps of data science life cycle very sincerely and after completing the process and deployment you will be ready to take the next step towards a career in this field. Python and R are the two most used languages ​​in data science use cases.

Nowadays, Julia is also becoming one of the favorite languages ​​for setting models. However, along with the clarity of the process, you should be comfortable coding through these languages. From understanding the process to being proficient in the programming language, you need to be proficient in everything.

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