The Coursera course series, known as the Data Science Foundations Specialization, is intended to give students a thorough introduction to the area of data science. Provided by IBM and the University of London, this specialization is appropriate for novices with little to no data science background.
We will explore this course in this article and see what it has to offer students.
What Is Data Science Foundations Specialization On Coursera?
The Data Science Foundations Specialization introduces the learners to data science, statistics, programming, machine learning, computational thinking, etc. The specialization explains the role of data science in today’s world and equips you with theoretical and practical knowledge of data science.
This specialization comprises eight courses on data science that explain the basics to advanced concepts of data science. The following are the courses included in this specialization.
- The Data Science Profession – Student View
- What is Data Science?
- Tools of Data Science
- Problems, Algorithms & Flowcharts
- Python for Data Science, AI, and Development
- Statistics and Clustering in Python
- Data Science Project Capstone: Predicting Bicycle Rental
- Python Project for Data Science
At the end of this specialization, you will do two capstone projects, applying your skills and knowledge gained from this course.
Also Read: What will you learn in the IBM Data Science Professional Certificate course on Coursera?
What Will You Learn In This Specialization?
This data science specialization gives a detailed explanation of data science concepts such as data collection, cleaning, visualization, analysis, etc. You will learn fundamental concepts and practical knowledge of data science, which will be helpful for your career.
Data analysis techniques using Python libraries such as Numpy and Pandas will be taught in this course. Additionally, you will learn how to create simple dashboards and work on clustering algorithms.
You will learn to use tools like GitHub and Jupyter Notebook for data science applications.
By the end of this course, you will have a proper understanding of the fundamental concepts of data science and be prepared for academic and career opportunities.
What Concepts Are Taught In This Course?
The Data Science Foundations Specialization course begins with a proper explanation of the data science profession and how to apply it in real-world projects. Also, it describes the role of machine learning and Python in data science applications.
After that, the course explains the basic concepts of data science and its significance in today’s data-driven world. You will also learn about various career opportunities in the field of data science.
Then, the specialization teaches about the tools used for data science, such as Python libraries, machine learning models, big data tools, etc. Also, you will learn about programming languages used in data science, such as Python, R, and SQL.
The next course of this specialization explains algorithms, discretisation & pseudocode, and Euclidean algorithms in pseudocode.
Then, you will learn foundational concepts of Python, applications of Python in data science, and popular Python libraries like Pandas and Numpy. Various mathematical and programming techniques for statistics and clustering in Python will be explained in the next course.
Finally, you will have to complete two capstone projects by applying all the skills you learned throughout this specialization. These projects will concrete your understanding of data science concepts so that you can look for career opportunities in this domain.
Who Should Join The Data Science Foundations Specialization On Coursera?
The Data Science Foundations Specialization course is designed for beginners with no prior experience in data science. Students who want to build a career in the field of data science can consider taking this course. This course will equip them with updated skills and knowledge of data science to help them advance in their careers.
Professionals who want to gain an understanding of data science to get better roles in their respective industries can consider this course. Since it is a self-paced course, you can complete it on your own time and showcase your credentials to your employer for job enhancement.
How Long Does It Take To Complete The Data Science Foundations Specialization On Coursera?
The Data Science Foundations Specialization takes three months to complete if you study 10 hours per week. However, you can complete it on your own time due to its self-paced structure.
Will You Get A Job After Completing The Data Science Specialization On Coursera?
A career in data science can be started by completing the Coursera Data Science Foundations Specialization, but your chances of landing a job rely on a number of variables outside of the course.
Your expertise, knowledge, and skills determine whether you get a job. However, this specialized course can increase your chances of getting a job by upskilling you with the in-demand concepts of data science, providing credentials from reputable institutions like IBM and the University of London, and offering access to Coursera’s community to connect with industry professionals.
Even while you might not acquire a full-time job as a data scientist right away, you might aim for entry-level positions after completing this course, such as:
- Data Analyst
- Business Analyst
- Junior Data Scientist
- Database Administrator
- Reporting Specialist
How Much Does This Course Cost?
You can access this course in two ways – individually or with the Coursera Plus Subscription. When you access this course individually, it will cost you $49 per month. With the Coursera Plus Subscription, you will get access to this course along with 10000+ courses in various subjects on Coursera.
Is It Worth Taking The Data Science Specialization On Coursera?
In short, if you are someone who needs a beginner-friendly course on data science to acquire fundamental knowledge, then this course is worth it. It provides a comprehensive introduction to fundamental topics in data science, including programming, statistics, data visualization, machine learning, etc.
The course focuses on industry-relevant skills and tools such as Python, SQL, R, Jupyter Notebook, and more. With a flexible learning structure, the course suits both students and working professionals.
Therefore, this specialization is a great choice for beginners who want to start their career journey in the demanding field of data science. However, this course is not the right choice for you if you already have some experience in data science and are looking for higher academic opportunities.
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