Unlocking the potential of better data science workflow

Today everyone knows the possibilities of data science in the business environment. Many organizations are opening their doors to big data and unlocking its power with increasing value to a data scientist who knows how to manipulate actionable insights from gigabytes of data.

Here come the questions for data scientists, suppose they are starting new projects of data science so how do they streamline their workflow? Where have they placed your data and code? What equipment will they use and why?

In software engineering, there are some common answers to all questions. Well, each software company implements its own approach unique traits and quirks, in most of them the core processes are based on the same established principles, practices and tools.

Tips for better data science workflows

Data science is a difficult chapter it required more potentials for data scientists to make successful delivery of the project and make the project successful. Data scientists must have the ability of high skills, experience, patience, and knowledge on decision making. Here provides some addon which boosts the data scientist efficiency and capable of delivering a successful project before the deadline:

Distribute project into phases:

It is better to distribute the project in different stages; The reason for this is that if the data scientist starts working on the entire project at an early stage, there may be a chance of project failure or an unexpected result. The good strategies are to distribute into levels like:

Level1: Preliminary analysis: Preliminary analysis is beginning level where data is gathered, goals are defined and objective are clear.

Level2: Exploratory of data: During this phase, the data is cleaned, analyzed and evaluated. At this stage there is a need to clarify some questions and find solutions to the question. All confusion and doubt need to be clarified specifically in this section before proceeding further.

Level3: Data Visualization: Now the time has come to visualize the data. But before visualization, data needs to be stored in a database, spreadsheet etc., whatever is convenient for the user. Visualization of data becomes essential for the continuous flow of the project and an effective and persuasive way.

Level4: Knowledge Discovery: Finally, models are developed to explain the data. Algorithms can also be tested to come up with ideal results and possibilities.

Use suitable tools and software’s in data science project

In this data science world, speed and performance are given more importance. Tools and software are playing an important role in improving project performance. If you fall short in either of these areas, the entire project may compromise.

Make the workflow clear

Whether you are working on a small, large project or you are playing some part in the project, workflow should be clearly described. When creating a workflow make sure that it is clear, clear and clear to anyone who uses it

Select necessary resources for the project

Be aware of project involvement and try to keep your team small. This limits outside noise and ensures that you will not be paralyzed by excessive ideas and varied strategies. You want enough people to escape the tunnel, but not so much that you lose your focus.

The data science workflow

Let us now discuss data science workflow. Starting with business problems, where data scientists or organizations define business problems that can be solved. Typically, it has a specific metric that can be measured financially.

After defining business problems, the organization’s team works closely with the data science team to prioritize the problem and step into the project management workflow. Depending on one type of project, the focus may be on one process or another. Some may be complex and some may be easy to implement.

Now let’s discuss the 3 stages of data science workflow:

Preparation stage: The preparation phase is an initial phase where data is collected and cleaned. This phase takes longer than all others because almost all data is impure. In this step a necessary action should be taken to improve the quality and develop in to a actionable format so that machine can interpret and learn.

Experimentation stage: In the experimentation stage the hypotheses are prepared, data is transformed and visualize and finally model is prepared.

Distribution stage: In the distribution phase, reports are produced to provide results. Once the report is fully analyzed it is ready to be deployed to achieve business value.

Apart from this, the data science team also needs to focus on:

· Beginning of Workflow: Business Understanding / Domain Expert Communication, Data Understanding, Data Quality and Feature Engineering is Critical

· End of Workflow: Communication with project stakeholders, product delivery is important

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Conclusion:

When all the above points are included in the project, an improved data science workflow is more efficient, less expensive, and more returning than the average approach. By applying the above tips and suggestions you can make your project a success.

To make the best use of any software or application, the design needs to be as pluggable and expandable as possible. This pluggable and expandable design proves to make this approach fast, efficient, reusable, and scalable.