Data Science In 5 Minutes | Data Science For Beginners | What Is Data Science? | Simplilearn

[0:00]Are you one of the many who dreams of becoming a data scientist? Keep watching this video if you're passionate about data science, because we will tell you how does it really work under the hood. MI is a data scientist. Let's see how a day in a life goes while she's working on a data science project. Well, it is very important to understand the business problem first. In our meeting with the clients, Emma asks relevant questions, understands and defines objectives for the problem that needs to be tackled. She's a curious soul who asks a lot of why's, one of the many traits of a good data scientist! Now, she gears up for data acquisition to gather and scrape data from multiple sources like web servers, logs, databases, APIs, 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, Emma deals with inconsistent data types, misspelled attributes, missing values, duplicate values and whatnot. Then in data transformation, she modifies the data based on defined mapping rules. In our project, ETL tools like Talend and Informatica are used to perform complex transformations that helps the team to understand the data structure better. Then understanding what you actually can do with your data is very crucial. For that, Emma does exploratory data analysis. With the help of EDA, she defines and refines the selection of feature variables that will be used in the model development. But what if Emma skips this step? She might end up choosing the wrong variables, which will produce an inaccurate model. Thus, exploratory data analysis becomes the most important step. Now, she proceeds to the core activity of a data science project, which is data modeling. She repetitively applies diverse machine learning techniques like KNN, decision tree, naive Bayes, to the data to identify the model that best fits the business requirement. She trains the models on the training data set and tests them to select the best performing model. Emma prefers Python 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. Emma meets the clients again to communicate the business findings in a simple and effective manner to convince the stakeholders. She uses tools like Tableau, Power BI and QlikView that can help her in creating powerful reports and dashboards. And then finally, she deploys and maintains the model. She 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, she uses reports and dashboards to get real-time analytics. Further, she also monitors and maintains the project's performance. Well, that's how Emma completes the data science project. We have seen the daily routine of a data scientist is a whole lot of fun, has a lot of interesting aspects and comes with its own share of challenges. Now, let's see how data science is changing the world. Data science techniques along with genomic data provides a deeper understanding of genetic issues and reaction to particular drugs and diseases. Logistics 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 the key variables that influence employee turnover. Also, the airline companies can now easily predict flight delay and notify the passengers beforehand to enhance their travel experience. Well, if you're wondering, there are various roles offered to a data scientist, like 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. 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's all from my side today. Thank you for watching. Comment below the next topic that you want to learn and subscribe to simply learn to get the latest updates on more such interesting videos. Thank you and keep learning.