Specialization in “Learn SQL Basics for Data Science”

Tools used in this project:
Python SQL

Find out everything you need about the specialization “Learn SQL Basics for Data Science” by Coursera. This detailed review covers course content, projects, benefits of learning SQL, and more. Get an in-depth look at this specialization and determine if it is the right choice for you.

Data Science SQL

Introduction

In the information age, knowing how to analyze and interpret data has become crucial in many areas, especially in the corporate sector. As a Data Analyst, I have always tried to hone my skills and stay up-to-date on the latest trends and technologies. I recently completed a Coursera specialization offered by the University of California, Davis, titled “Learn SQL Basics for Data Science Specialization.” In this article, I would like to share my experience and illustrate how this specialization has enhanced my professional skills.

What is SQL, and why is it essential for Data Science

SQL (Structured Query Language) is a database management and manipulation programming language. It is an essential tool for Data Analysts and Data Scientists, as it allows them to extract, modify, update, and manipulate data stored in a database. SQL’s versatility makes it essential in multiple contexts, from Web applications to database management systems.

What I learned

The “Learn SQL Basics for Data Science” specialization offered by Coursera includes four courses ranging from understanding SQL to manipulating complex data to analyzing large volumes of data using Apache Spark and Delta Lake. The training proved extremely practical, offering numerous exercises and projects that allowed me to put the concepts learned into practice immediately. Although the lecturers showed great competence, and the teaching materials were clear and well-organized, I believe there is room for improvement.

During my specialization, I consolidated my knowledge of SQL fundamentals, learning how to formulate queries to extract and manipulate data, employ SQL-specific functions to analyze information and optimize query performance. I also delved into using Apache Spark for large-scale analysis and Delta Lake for building reliable data pipelines. An added value was the chance to work on concrete projects, which allowed me to apply skills in realistic contexts. These projects have embraced the analysis of large datasets, the definition of metrics for research, and the effective presentation of results.

In summary, the skills developed during the specialization significantly enhanced my ability to analyze and interpret data.

Benefits for a Data Analyst

The “Learn SQL Basics for Data Science” specialization offered by Coursera represents a significant opportunity to enhance the skills of a Data Analyst. This training provides the tools to address analytical challenges in an increasingly complex data environment. The acquisition of these skills, combined with a genuine passion for data analysis and an unwavering commitment to professional improvement, enables those who obtain this specialization to become an ideal candidate for any assignment or role requiring experts in Data Analysis.

The training course

The specialization consists of three courses, each structured in several weeks of learning:

Course 1: SQL for Data Science

Rating on Coursera: 4,6 out of 5 (number of ratings: 15,221)

In the current digital age, data collection has grown exponentially, making it essential to have professionals who can interact with data. This is the role of the data scientist, a figure who combines mathematical, computer, and trend analysis skills. According to Glassdoor, Data Science is considered one of the best jobs from a pay perspective, with thousands of job openings available.

The “SQL for Data Science” course is an excellent introduction to the basics of SQL and working with data. It requires no prior knowledge of SQL, making it accessible to anyone interested in entering the field of data science. The course gradually introduces how to write SQL queries, both simple and complex, to select data from tables. It also allows us to work with different data types, such as strings and numbers and provides methods for filtering and reducing the results.

One of the most appreciated features of the course is the opportunity to create new tables and transfer data into them. The course also covers essential topics such as data governance and profiling, providing students with the skills to use SQL professionally and model data for targeted analysis. Programming exercises based on real-world cases are another strength of the course, allowing students to apply the skills learned immediately. In conclusion, the “SQL for Data Science” course is an excellent starting point for anyone wanting to pursue a data science career. With a practical and accessible approach, this course provides an excellent foundation for learning SQL and data analysis.

Course 2: Data Wrangling, Analysis and AB Testing with SQL

Rating on Coursera: 3,3 out of 5 (number of ratings: 838)

The “Data Wrangling, Analysis and AB Testing with SQL” course is a significant step up from the introductory “SQL for Data Science” course, addressing more complex topics and more advanced applications of SQL. Although its low 3.3 rating may suggest some difficulty, it is essential to note that the complexity of the material covered could be a contributing factor.

The course focuses on four data science case studies, each requiring a thorough understanding of SQL (and Python) and its applications. Students learn to manage data, perform analysis, and conduct AB testing, all fundamental skills in data science.

However, some students have found that the treatment of these topics can sometimes be unclear. This may be due to the material’s inherently complex nature, which requires a high proficiency level in SQL and a solid statistical knowledge base. The instructor does her best to explain the concepts understandably, but some students may find the speed and depth of the course a bit challenging, especially if they have no prior training in statistics. Despite these challenges, the “Data Wrangling, Analysis and AB Testing with SQL” course remains a valuable resource for anyone wanting to deepen their SQL skills and apply them to real-world data science problems. It’s low rating on Coursera by students reflects more complexity than its quality; students who are ready to engage fully will find that the course offers a wide range of practical skills critical for any data scientist.

Course 3: Distributed Computing with Spark SQL

Rating on Coursera: 4,5 out of 5 (number of ratings: 611)

The third course in the specialization is devoted to Big Data management. This course is designed for students with experience with SQL (and Python) who wish to further their journey into the world of data by learning how to use Apache Spark for distributed computing.

The course provides an in-depth understanding of this open-source standard for managing large data sets. Students will learn the basics of data analysis using SQL on Spark, laying the groundwork for understanding how to combine data with advanced research at a large scale and in production environments.

The course is divided into four modules. By the end of the course, students will have a clear understanding of Spark architecture, queries within Spark, everyday ways to optimize Spark SQL, and how to build reliable data pipelines.

The first module introduces Spark and the Databricks environment, explaining how Spark distributes computation and how Spark SQL works.

The second module covers key Spark concepts such as storage vs. compute, caching, partitions, and performance troubleshooting using the Spark user interface. This module also covers new features in Apache Spark 3.x such as Adaptive Query Execution.

The third module focuses on creating data pipelines, including connecting to databases, schemas and data types, file formats, and writing reliable data.

The last module covers data lakes, data warehouses, and lakehouses. Students will build production-grade data pipelines by combining Spark with the open-source Delta Lake project. By the end of this course, students will have honed their SQL and distributed computing skills, becoming more proficient in advanced analytics and preparing to transition to more advanced analytics as a Data scientist.

Course 4: SQL for Data Science Capstone Project

Rating on Coursera: 4,2 out of 5 (number of ratings: 198)

The fourth and final course in the specialization is the “SQL for Data Science Capstone Project.”

The field of data science is dynamic and growing, requiring solid knowledge and expertise in SQL to be successful. This course provides a solid foundation for applying SQL skills to analyze data and solve real-world business problems.

Whether you have completed the other three courses in the Specialization or are taking only this one, the final project represents an opportunity to apply the knowledge and skills you have acquired to practice SQL querying and solve problems with data.

All to enrich one’s portfolio. You will choose a data set and develop a design proposal. You will explore data and perform statistical analysis learned during this specialization. You will discover analytics for qualitative data and consider new metrics you apply to patterns that emerge during analysis.

You will put all the work together in the form of a presentation in which you will tell the story of your findings. Along the way, you will receive feedback through the peer review process. This community of other students will provide additional input to help you refine your approach to data analysis with SQL and present your findings to clients and executives. In summary, Course 4 is an excellent opportunity to practice the skills learned in previous courses by working on a data science project from start to finish.

Learn SQL Basics for Data Science Specialization

Conclusions

Coursera’s “Learn SQL Basics for Data Science” specialization was an enriching learning experience that provided me with valuable skills immediately applicable to my work as a Data Analyst. The practical approach of the course allowed me to immediately put the skills I learned into practice, making the learning immediately tangible. The skill level of the teachers should be highlighted.

However, it is essential to note that the shallow reviews for courses 3 and 4 could be due to the difficulty of course 2. This course, which deals with a complex topic, may have discouraged some students, leading them to drop out of the major. This is unfortunate because the difficulties encountered during Course 2 are offset by the skills acquired at the end of the course. In addition, basic proficiency in the Python programming language is required.

In any case, for those primarily interested in learning SQL applied to Data Science, Course 1 is extremely valuable and can be taken independently of subsequent courses. However, it is essential to note that taking only Course 1 will not allow one to obtain the specialization. In conclusion, I would recommend this specialization to anyone interested in improving their SQL and data analysis skills. Despite some difficulties, the skills acquired and the hands-on approach to learning make this course a highly worthwhile investment of time and resources.

Resources

Click on this link to view the notebook used for the entire course of study.
Instead, a final project case study can be viewed at this address.

Evaluation

  1. Course content: lectures’ quality, the topic’s relevance, and how well the course met your initial expectations.
  2. Clarity of instruction: how easy it was to understand the course material, whether the instructions were clear, and whether the teacher provided sufficient explanations.
  3. Practical applicability: if the skills acquired in the course will be helpful in my career or practical applications.
  4. Support and resources: additional learning resources, such as supplementary readings and support offered by tutors or fellow students.
  5. Difficulty level: the complexity of the course and how challenging it was for me to complete it; more stars indicate a more challenging course.
  6. Overall value: overall judgment of the quality of the course, considering both the cost and what I gained in terms of new skills acquired.

Course content

Clarity of instruction

Practical applicability

Resource support

Difficulty level

Overall Value

FAQ


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