It’s not you, it’s the data!
In an ideal scenario, let’s suppose you managed to get your data, less messy than the original, and now you are ready for some exploratory data analysis.
You never work with 67 attributes in the data. There are always sections of data that you perform analysis or build your model on. Enters SQL.
SQL is a fourth-generation language; a domain-specific language designed to manage data stored in an RDMS (Relational Database Management System) In school, I learned that we can use SQL to handle structured data where variables of data relate to each other (which is core to Data Science).
Even though NoSQL and Hadoop have become a larger component of the data science ecosystem of a candidate to be well-versed with writing and executing complex queries.
As a data scientist, any job description would ask a proficiency with data querying — in versions of SQL (SQL Server, MySQL, NoSQL or others). This is non-negotiable and implicit for a role. Because SQL is specifically designed to help you access, communicate, and work on data, learning SQL will help you to better understand relational databases and boost your profile as a data scientist.
Machine learning gives you the power to think, analyze, and make decisions for a business. Machine learning models facilitate a business at taking better shots on profitable opportunities or avoiding unknown risks by identifying pockets of work.
To be a good data scientist, per my experience, you should have good hands-on knowledge of various supervised and unsupervised algorithms, their use cases, and the outcomes one can expect out of each model once deployed.
Machine learning for data science includes algorithms that are central to ML; Regression, K-nearest neighbors, Random Forests, Naive Bayes. With the increasing complexity of data, the science also finds its usability in PyTorch, TensorFlow, Keras.
My top three reasons to master machine learning for data science:
- Better and wider project prospective
- Growth opportunities
- Cross-functional exposure across industries
Visuals express ideas in a snackable manner.
Why do you need dashboard tools?
To represent information multiple times
To update information regularly and communicate it
When the base request will always be the same
Data Scientists are expected to drive better decision making by creating decision support tools from BI dashboards.
While the data team processes a vast amount of data, the following analysis and insights need to be translated into a format easy to comprehend. It’s a natural human tendency to understand pictures in form of charts and graphs more than raw data.
As a data scientist, you must be able to visualize data with the aid of data visualization tools such as Tableau, PowerBI, Looker, ggplot, d3.js, and Matplottlib. These tools will help you to convert complex results from your projects to a format that will be easy to understand and convey further.
The thing is, you cannot easily throw even basic Data Science jargons likes strong correlation or p values to a stakeholder with a Public Policy background. You need to show them visually what those terms represent and mean to the business.
Data visualization allows businesses to work with data first-hand. The people involved with the results of data analysis can quickly grasp insights to help them act on new business opportunities and stay ahead of the competition.
Stories of and with data — the new business acumen.
I cannot stress enough how important it is to understand the business you are solving problems for.
To be a data scientist, I believe it is as important to have a solid understanding of the industry you’re working in and know what business problems your company is trying to solve as Python coding or data querying. If you are not able to identify what problems are important to solve for the business, there is no way you can identify new ways the business should be leveraging its data.
Say, for instance, I am working in the Food & Beverage industry. If I do not have the idea of supply-chain, distribution channels, sales, and peripheral information, I cannot effectively and efficiently solve given a problem “What are the sales of xx products with xx retailer vs the competitor brands?”
Data scientists are expected to communicate with stakeholders to understand business objectives, design customized solutions, and perform tasks with discreet supervision. That is reason enough to believe why you need to know how businesses operate for you to direct your efforts in the right direction.