Review - Is Data Science Fundamentals with Python and SQL Specialization on Coursera Worth It?

The demand for people who can analyze big data and extract meaningful information to drive decisions for companies is increasing every day. They need them to improve the quality of their services and deliver the best user experience for their customers. These actions are the responsibility and the role of the data scientists. Data scientists need to know many skills to perform the data analysis and make decisions such as the skills to collect data from different sources like the web and also extract and filter the data from the databases and clean them before performing analysis and even learn machine learning and deep learning to perform much more complex actions and make predictions but not all the data scientists know the artificial intelligence and not a mandatory skills. Still, it will be good if you have that in your belt.

Many people think they need years of study in the university with a degree to have these skills. Still, with a computer and an internet connection, you can take some online courses and learn all of that in your place. 

With thousands of available courses, I’ve chosen one of the best to start with Data Science Fundamentals with Python and SQL Specialization offered from IBM.

1. The Instructors Review

Eight instructors create this specialization, and all of them are experts working at IBM company. Some of them have received a Ph.D. in data science, like Joseph Santarcangelo and his research on machine learning and computer vision, so that you can learn these skills from industry experts rather than amateurs creating a course about data science.

1. Course Content

2.1. Tools for Data Science

Starting by understanding some of the programming languages data scientists use, such as python language and R, and some of the tools available even for commercial use. Next, you will learn how to use these tools like sharing your code on GitHub and using Jupyter Notebooks to write your python code and RStudio for writing R code. Finally, learn about the available IBM tools for data science like Watson studio.

2.2. Python for Data Science, AI & Development

This course will teach the Python programming language and its data types, such as strings to store characters and variables like integers to store values and use mathematic operations. Next, you will learn to store many values in one variable like lists, tuples, and dictionaries. Later, learn about loops, how to use functions, handle errors, and use the panda library to work with data. Finally, collect data from the web using APIs and web scraping.

2.3. Python Project for Data Science

This section won’t teach you anything new like the previous. Still, instead, you will apply what you’ve learned to create a web scrapping to catch and collect the data from the web, and this is a beneficial skill you have to know if you are a data scientist since collecting and storing data is the role of this. You will use the python library to extract stock data. So you will apply different data types to work with data and build a dashboard using python libraries to get a real-world experience.

2.4. Statistics for Data Science with Python

This course will teach you about statistics and starting with descriptive statistics such as mean, median, mode, standard deviation, and learn measures of dispersion. Next, you will learn about data visualization and the best chart to use according to your data and the story you want to tell. You will also learn about probability distribution like normal distribution and T distribution. Later, learn the hypothesis testing lie z-test and t-test and how to deal with tails and rejections and learn about regression analysis.

2.5. Databases and SQL for Data Science with Python

Learn about the databases and how to interact with them using the SQL language and its queries to extract and pulling the data and insert data inside the database. Next, explore more about databases and tables and their relationship and use SQL to create tables. 

Later, you will discover more about SQL queries and how to filter your data and sorting them. Finally, using python and SQL to interact with the database and analyzing them with python.

Conclusion

The world is generating extensive data every day. Companies need people to analyze them. Taking this course in data science can help you get an overview of this career and choose if this is the right for you or you need to work in another industry.