Data scientist

What Does a Data Scientist Do?
If you're passionate about data science and dream of becoming a data scientist, this article is for you. In this article, we'll explore what a day in the life of a data scientist looks like and how data science is changing the world.

Understanding the Business Problem
Before starting a new data science project, it's important to understand the business problem. A good data scientist asks relevant questions, defines objectives, and understands the problem that needs to be tackled.
Data Acquisition and Preparation
Data acquisition involves gathering and scraping data from multiple sources such as web servers, logs, databases, APIs, and online repositories. Once the data is collected, data preparation involves cleaning and transforming the data. This step is time-consuming and involves handling complex scenarios such as inconsistent data types, misspelled attributes, missing values, and duplicate values.
Exploratory Data Analysis
Exploratory data analysis helps to understand what can be done with the data. A data scientist uses EDA to define and refine the selection of feature variables that will be used in the model development. Skipping this step can result in choosing the wrong variables and producing an inaccurate model

Data Modeling
Data modeling involves applying diverse machine learning techniques to the data to identify the model that best fits the business requirement. A data scientist trains the models on the training data set and tests them to select the best-performing model. Python, R, and SAS are commonly used for modeling the data.
Visualization and Communication
Visualization and communication are essential to communicate the business findings in a simple and effective manner. A data scientist uses tools like Tableau, Power BI, and QlikView to create powerful reports and dashboards.
Deployment and Maintenance
The final step involves deploying and maintaining the model. A data scientist tests the selected model in a pre-production environment before deploying it in the production environment. After successfully deploying it, reports and dashboards are used to get real-time analytics. The project's performance is monitored and maintained.
Data Science and its Impact
Data science techniques, along with genomic data, provide a deeper understanding of genetic issues and reactions to particular drugs and diseases. Logistics companies can discover the best rules to ship, the best time to deliver, and the best mode of transport to choose, leading to cost efficiency. With data science, it's possible to not only predict employee attrition but also understand the key variables that influence employee turnover. Airline companies can now easily predict flight delays and notify passengers beforehand to enhance their travel experience.
Roles and Salaries
Various roles are offered to a data scientist, including data analyst, machine learning engineer, deep learning engineer, data engineer, and, of course, data scientist. The median base salaries of a data scientist can range from 95,000 to 165,000.
If you're ready to be a data scientist, start today. The world of data needs you.