In this growing era, the demand for data storage is increasing gradually. But the data can be in small quantities or in large quantities. The problem of storing the large data was not solved till 2010. But with the release of frameworks, like Hadoop and others, solved this problem up to an extent. But nowadays, the world is growing as a digital hub that gives the evolution to data processing. This is the place where data science plays an essential role. You might be thinking about what is data science and why it is necessary. Well, keep scrolling this blog. We have detailed all necessary information about data science below.
What is data science?
Data Science combines several algorithms, tools, and machine learning systems to recognize hidden models from the raw information. In simple words, data science definition in simple words can be defined as it is the study of big data that involves various ways to store, record, and analyze the data to extract relevant details. A Data Scientist analyzes the data from various viewpoints; sometimes, viewpoints are
unknown earlier. Various examples can answer what is data science? Some of these examples are:
● Prediction and identification of disease,
● Automating digital ads,
● Detection of frauds,
● Optimizing transportation & logistics routes in various real-time durations,
● Healthcare recommendations, etc.
What are the main components of data science?
Once you get the details about what is data science? There is a need to understand the components of data science. There are several elements of the Data Science life cycle, and these are:
● Data discovery
● Business requirements
● Data exploration
● Testing model
● Data processing
● Predictive modeling
● Communicating results
What is the domain of data science?
Data science plays an important role in decision-making. A self-driving car gathers live information using the sensors (such as cameras, radars, and lasers to generate a map). Depending on the collected data, it makes the decisions like when to speed high and lower, where to take turns, etc.
There is a need of data science in predictive analytics. Let’s take an example of weather forecasting. Data from aircraft, satellites, ships, radars can gather and analyze to create models. This model is forecasting the weather and supports predicting any upcoming natural calamities. This helps in taking relevant measurements and can save people’s precious lives.
Apart from this, there is a various domain of data science, such as:
● Travel: Predicting flight delays and dynamic pricing.
● Marketing: Cross-selling and upselling.
● Healthcare: Medication effectiveness and disease prediction.
● Social Media: Digital marketing and sentiment analysis.
● Sales: Demand forecasting and discount offers.
● Automation: Aircraft, drones, and self-driving cars.
● Credit and Insurance: Fraud and risk detection and claims predictions.
Define a data scientist
You might be confused about the definition of the data scientist. Here is the simple definition of it. Data scientists are the person who carries out the analyses from the huge collected data. Moreover, data scientists draw various useful information taken from scientific fields and applications that can be about mathematics or statistics.
What are the roles of a data scientist?
Data scientists solve difficult data problems using expertise in particular scientific disciplines. Scientists work with various components related to statistics, mathematics, computer science, and much more. They use various latest technologies to get the best and accurate solutions and reach suitable conclusions essential for an organization’s development and growth. Data Scientists show the details in a useful manner than the provided raw data accessible to them from unstructured and structured forms.
Apart from this, it is necessary that the data scientist must have knowledge about various tools. These tools are:
● R
● Apache Hadoop
● Python
● MapReduce
● NoSQL databases
● Apache Spark
● Cloud computing
● Apache Pig
● D3
● Tableau
● GitHub
● Python notebooks
Moreover, for algorithms, mathematics, modeling, data visualization, and statistics, data scientists usually have knowledge about packages and libraries. These libraries help them in various ways. Some of the renowned Python-based libraries are Scikit-learn, PyTorch, TensorFlow, Pandas, Matplotlib, and Numpy. But when the data scientists want to reproduce the reports and research, they must use the frameworks and notebooks like JupyterLab and Jupyter.
What are the skills a data scientist must-have?
● Programming skills (R, SAS, Python),
● Data visualization and Storytelling,
● Mathematical and statistical skills,
● SQL,
● Hadoop,
● Machine learning
What is the common difference between BI vs Data Science?
You might be wondering why we explain the difference between BI vs Data Science. Well, there are several learners who are confused with the concept of data science and Business Intelligence (BI). BI is used for analyzing the previous data that is useful for business development. In a similar way, data science helps analyze large data useful for business growth.
| Features | Business Intelligence | Data Science |
| Approach | Statistics and Visualization | Statistics, Graph Analysis, Machine Learning, Neuro-linguistic Programming (NLP) |
| Tools | Microsoft BI, Pentaho, R, QlikView | BigML, R, RapidMiner, Weka |
| Data Sources | Structured (Usually, SQL, that are mostly related to Data Warehouse) | Both Unstructured and Structured (text, logs, SQL, cloud data, NoSQL) |
| Focus | Present and Past | Future and Present |
The bottom line about what is data science
As we have mentioned above (about what is data science) that data Science combines several algorithms, tools, and machine learning systems to recognize hidden models from the raw information. Moreover, there are various domains where the use of data science plays an essential role. Therefore, it would be beneficial if you pursue your studies in this field. The data scientist’s job is not everyone’s cup of tea; therefore, do an in-depth study related to data science and polish your skills. A data scientist career also helps you get a handsome salary package because data scientists’ demand is increasing.
Well, we have included all the necessary details about what is data science? If you still have any queries regarding data science, comment us your query. We will be glad to help you with your queries as our main objective is to support the learner to improve their knowledge. So keep on learning and explore the dimension of data science.
