Data science is a “term for unifying analytics, data analysis, machine learning and related approaches” in order to “understand and interpret real events” with data. This uses methods and hypotheses from a wide range of fields in the fields of mathematics, economics, computer science and information science.
We have trained and qualified a team who practice technique gained over the past 10 years of experience with projects and data science initiatives which take in hand are performed more elaborately only by this developed technique. In reality, designing your data project isn’t as hard as it seems but you need to learn the data science process itself first. Becoming data-powered is mainly about understanding the basic steps and phases of a data analytics project and pursuing them from the processing of raw data to the creation of a machine understanding model and finally to the operation.
We evaluate the requirements, extract the working direction in compliance with our methods and then begin to put together blocks one by one.
We Define the Goal :
Understanding the business or activity that the development is part of is key to ensuring its success and the first stage of any sound data science initiative. You must have specific operational criteria to inspire the multiple players required to get from concept to development. We go out and speak to the people of organisations whose business we strive to boost with data before even talking about the data. Then, we sit down to identify timeline and primary success metrics in specific terms. I know, preparation and procedures sound tedious, but actually they’re a critical first step to kick-starting the job!
This move may seem trivial in a big enterprise project, playing around with a data set or an API. It is not. It is not enough just to access a cool open data collection. We define a specific aim of what we want to do with knowledge to give us a motivation, direction and purpose: a particular question that we have to address, a product to create, etc.
We Get the Data :
Once we are clear with requirements, we search for data and this is the second stage of any data analysis. Mixing and integration of the maximum number of different data sources makes analysis more effective so as far as possible we see and search for data.
Here are some of the data sources:
Connect to a database: we sit with your data and an IT team looking for available data, or we might ask you to start digging your private database to see what information you’ve been collecting.
Use APIs: We use the APIs to dig into the data that is there with you, whether it’s the thousands of emails or the information that your sales team has put in the CRM or the submitted support ticket, etc. Don’t worry, we’ve got the perfect team members to do this for you.
Search for open data: the World Wide Web is full of data, from census information to the total number of animals to the number of employees or the number of vehicles.
Using census data, you can calculate the average revenue per address or applications such as openstreetmap can tell the number of coffee shops on a given street and you can use all that data for further processing.
We Clean the Data :
This is the dreaded data prep step that normally takes up to 80 percent of the time spent on a data project.
Once We have your data, it’s time to get to work on it in the third phase. We start digging to see what you have and how we can link everything together to meet your original goal. We carefully start taking notes on our first analyses and ask you, the IT team or other groups to understand what all your variables mean.
The next step is to clean up your data. You may have noticed that even though you have a country feature, for example, we have different spellings, or even missing data. It’s time to look at each column to ensure that your information is uniform and clean.
Alert! Alert! ! This is probably the longest and most annoying step of our data analytics project. Data scientists report that data cleaning can take up to 80 per cent of the time spent working on a project. This is going to be a little painful, but as long as we have focus on the final goal, we will be delivering it.
Finally, one critical aspect in data planning that should not be ignored is to ensure compliance with the data protection regulations for your data and project. The privacy and protection of personally identifiable data is becoming a priority for consumers, organisations and the public, and it will be a priority for us also from the beginning itself.
We will centralise all the data activities, sources and databases in one location or method to promote transparency in order to operate privacy conforming projects. Information sets and programs that contain confidential and/or sensitive data and that need to be handled differently should then be labelled specifically.
We Enrich Data :
Now that you have clean data, now is the time to take full advantage of it. We will begin the data enrichment process of your project by joining all your sources and group logs to restrict your data to essential characteristics. One example is the creation of time-based features such as: extraction of data elements (month, hour, weekday, year week, etc.). One example is enriching data.
A further way to improve data is to add a dataset — mostly to find columns from one dataset or tab. This is a vital aspect in all research, but when you have a multitude in sources it can easily become a nightmare. Luckily, our approach helps us to combine the data through easy analysis, data collection or attachment based on unique, sophisticated parameters.
we will be carefull when gathering, processing, and manipulating the data not to inject unwanted bias or other undesirable patterns into it. Nonetheless, the data used in the development of machine learning models and AI algorithms is always a reflection of the real world, and may therefore be highly biased but we make sure that with our approach we avoid any bias towards anything. One thing that makes us worry about data and AI the most is that the system can’t detect bias. Consequently, when you train your model on biased results, it will perceive recurrent bias as a reproductive decision and not anything to fix.
we will be carefull when gathering, processing, and manipulating the data not to inject unwanted bias or other undesirable patterns into it. Indeed, the data used in machine learning models and AI algorithms also reflects the real world and can therefore be significantly skewed by other communities or individuals. One thing that makes us worry about data and AI the most is that the system can’t detect bias. But working with our methodologies and implementation techniques we will make sure the data is completely unbiased towards anything.
We Visualise the data & Find insights :
Now we have a stunning data set, and now is a perfect time to test it with graphics. Visualisations are the easiest way to analyse and communicate conclusions while working with vast volumes of data, and they are the next step of the data analytics project.
The tricky part here is to be able to dig into graphics at any time and answer any question that someone might have about a given insight. we will still dive through the diagrams and answer every query that someone may have about a specific perspective. The processing of data is very handy here: We are the guy or Gal who did all the dirty work, and we are sure of the data like a hand palm!
If this is the final step, it is necessary to use APIs and plugins so that we can push data to the end user. Graphs are also another way of enriching a data set and developing more interesting features. For example, by putting data points on a map, we might notice that specific geographic zones are more telling than specific countries or cities.
We Develop Machine learning Module:
Here the real fun starts, Machine learning algorithms will help you take a step forward in understanding emerging patterns and predicting them.
Through working with cluster algorithms, you will build models in order to discover data patterns that can not be separated in graphs or statistics. These build groups of related events (or clusters) and communicate more or less clearly what is key to these outcomes.
We will go further and forecast future patterns with algorithms under supervision. They identify features that influenced previous patterns by analysing previous information and using them to create forecasts. The final step will lead to the creation of new products and processes rather than simply gaining information.
Even though your organisation’s personal data journey is not yet complete, it is crucial to understand the process so that all the stakeholders can understand what happens in the end.
Finally, the predictive model must not stand on the shelf to get real value from your project; it must be operationalized. Operationalization literally means applying an organisational machine learning model. Operationalization is necessary if your business and the benefits of your data science activities are to be realised.
We Iterate data :
The key goal of any business project is to show its success as soon as possible so that the work can be justified. The same applies to databases. You will easily obtain your first results by gaining time on data purification and enrichment to the completion of the project. This is the final step of data processing and is crucial to the entire process of data life.
The predictive power of a model lies in its ability to generalise. How do we explain a model depends on its ability to generalise unseen future data.
Interpreting data refers to the presentation of your data to a non-technical layman. We deliver the results to answer the business questions we asked when we first started the project, together with the actionable insights that we found through the data science process.
We will include you from step one to end so that you are in loop of complete work methodology. We work on artificial intelligence, IT product development, computer vision and smart engineering, assist businesses in AI development, user intelligence, workflow management and cost-effective processes. Our innovative technology, excellent customer service, ongoing innovation in talent growth and our own R&D center guarantee this.
We have great data scientists and our mathematical models have the potential to raise revenue, save operational overheads, optimize inventory consumption and provide business intelligence insights.