Data science has been a buzzword for many years now and is quite a big field in itself. It is a blend of computer science, statistics, various tools, algorithms, and machine learning principles. Data science starts with simple data reporting activities and goes up to advanced modeling using artificial intelligence.
Why Data Science?
Itâ€™s a digital age and the world has entered into an era of data. Years ago the available data was small in size and was in the form of structured data, simple BI tools could easily analyze it. Over the years as the data grew, the need for its storage also grew. Today the data which is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments is either semi-structured or unstructured. Simple business intelligence tools could not process this huge amount and variety of data. This is why the need for more complex and advanced analytical tools and algorithms for processing, came into the picture and hence Data Science has become so popular.
Who is a data scientist?
A Data Scientist is one who practices the art of Data Science. Data scientists crack complex data problems with their strong expertise in certain scientific disciplines along with several elements of mathematics, statistics, computer science, etc.
Role of a data scientist
For a data science project, the role cycle of a data scientist is as follows.
- Understanding the problem statement â€“ Understanding the problem statement is the foremost step of any data science project which can make-or-break the situation. During this time data scientists examine the objectives and expected requirements of the project. The data scientist needs to spend an ample amount of time on this step.
- Gathering Data â€“ Once a clear picture of the requirements is formed then comes the mining of needed data. The source of the data can be a company data warehouse, web scraping, financial records, etc.
- Data Cleaning â€“ As we have already mentioned that the data obtained can be semi-structured or unstructured therefore this is the most time-consuming process of the entire data science project. During this stage, the data scientist deals with outliers, missing data values, correcting the data types, and many other operations.
- Exploratory Data Analysis (EDA) â€“ It is at this stage data scientists analyze each feature or multiple features in the dataset and come up with some crucial insights with can help in the other steps of the project.
- Feature Engineering â€“ Feature engineering is an iterative process, going one by one through all the features and applying operations to improve the performance of the model. This step requires a lot of trial and error.
- Model Building â€“ Model building in itself is relatively a fast step but planning is important. Do you want a model with high accuracy or a model that can return the importance of features? You will need to think upon and select your strategy for model building and its evaluation.
- Deployment â€“ Once you have built and evaluated your model, it is finally time to deploy it in the real world. This step typically requires data scientists to work with data engineers or machine learning engineers.
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