Are you one of the many who dreams of becoming a data scientist? If you are passionate about data science, I will tell you how does it really work. Being a data scientist lets us see how a day inner life goes while working on a data science project.

Well it is very important to understand the business problem first in meeting with the clients. I ask relevant questions to understand and defines objectives for the problem that needs to be tackled. Being a purist soul who asks a lot of questions is one of the many traits of a good data scientist. Now start cares up for data acquisition to gather and scrape data from multiple sources like SAP servers logs databases API and online repositories. Oh, it seems like finding the right data takes both time and effort. After the data is gathered comes data preparation this step involves data cleaning and data transformation. Data cleaning is the most time-consuming process as it involves handling many complex scenarios here. I usually deal with inconsistent data types, misspelled attributes, missing values, duplicate values, and whatnot.

Then to increase the transformation I usually modify the data based on defined mapping rules in a project. ETL tools like talent and Informatica are used to perform complex transformations that help the team to understand the data structure better than understanding what you actually can do with your data is very crucial. For that, I usually do exploratory data analysis with the help of EDA able to define and refines the selection of feature variables that will be used in moral development. However, if I escape this step, I might end up choosing the wrong variables, which will produce an inaccurate model.

Thus exploratory data analysis becomes the most important step, now I proceed to the core activity of a data science project such as data modeling. I repetitively apply various types of machine learning techniques like KNL, decision tree, knife phase to the data to identify the moral that best fits the business requirement. I usually train the models on the training data set and tests them to select the best performing model. Python can be used for modeling the data however; it can also be done using R and SAS well the trickiest part is not yet over. Visualization and communication must be performed to understand the data effectively.

Now, meets the clients again to communicate the business findings in a simple and effective manner to convince the stakeholders that we use tools like tableau, power bi, and QlikView that can help in creating powerful reports and dashboards. Then finally deploys and maintains the model that tests the selected model in a pre-production environment before deploying it in the production environment, which is the best practice. Right after successfully deploying it we uses reports and dashboards to get real-time analytics further. There is also a need to monitors and maintains the performance of the project well. That is how I complete the data science project. We have seen the daily routine of a data scientist is Koller fun has many interesting aspects and comes with its own share of challenges.

Now let us see how data science is changing the world. Data science techniques along with genomic data provide a deeper understanding of genetic issues in reaction to particular drugs and diseases logistic companies like DHL FedEx have discovered the best routes to ship the best suited time to deliver the best mode of transport to choose, thus leading to cost efficiency with data science. It is possible to not only predict employee attrition but to also understand key variables that influence employee turnover. In addition, the airline companies can now easily predict flight and notify the passengers beforehand to enhance their travel experience well. If you're wondering there are various rules offered to a data scientist like data analyst, machine learning engineer, deep learning,  data engineer, and of a course data scientist.

The median base salaries of the data scientists can range from 95 thousand dollars to 165 thousand dollars so that was about the data science are you ready to be a data scientist if yes then start today the world of data needs you. That is all from my side, thank you for reading the article, comment below the next topic that you want to learn.