Data Centric

 

In this data-centric era, the role of data and the structures used to support its use 

become increasingly essential to humankind and the world at large. 

Data science is a multi-billion dollar industry, and only poised to grow as we continue to 

identify its applications in the sciences, politics, business, and almost every other 

sector of our lives. 

Analysts estimate the industry will grow to over 500 billion by the end of this decade. 

As a data scientist, you can make $124,000 annually on average. 

Exciting, isn’t it? 

We generate and store trillions of gigabytes of data every day. 

With that being said, the need to analyze, manipulate, and, more importantly, manage 

this information has become a daunting task. 

And so, the importance of data science in today's world cannot be overstated. 

As we move towards a data-centric world, the value of data science is rising to new heights. 

In the rapidly evolving digital landscape, databases are vast data repositories. 

SQL, or Structured Query Language, is the key that unlocks these treasure troves, enabling 

us to query, update, and manipulate data efficiently. 

SQL plays a vital role in the data science workflow, enabling professionals to extract 

valuable insights from large, intricate datasets. 

SQL is powerful, globally relevant (in industry and academia), and widely used by most data 

scientists today. 

A very versatile language called Python is used to connect to various databases, execute 

SQL queries, and retrieve results in a format conducive to further data analysis. 

It offers a wide array of libraries that simplify the connecting process. 

This course aims to equip you with the knowledge and skills to harness the power of databases 

and SQL in your data science endeavors effectively. 

In module 1 of this course, you will learn simple SQL for data manipulation in a database 

table. 

In module 2, you will learn about data definition statements and how to create our database 

tables. 

In module 3, you learn about built-in functions, sub-queries, and working with multiple tables. 

In module 4, you explore Python for accessing databases using Jupyter Notebooks. 

Module 5 primarily focuses on a project based on real-world data designed to test what you 

have learned from the course. 

An additional module, 6, covers advanced concepts like Stored Procedures, Views, ACID Transactions, 

Inner & Outer JOINs. 

Additionally, integrated hands-on labs and evaluations allow you to apply the theoretical 

knowledge to real-world scenarios, enhancing your understanding and retention of the concepts. 

These are designed with the sole purpose of maximizing your learning efficacy. 

All this will make your journey from learning to mastering SQL for Data Science interactive, 

engaging, and, most importantly, effective. 

So, what are you waiting for? 

Let's embrace this exciting journey together!