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I don’t have a background in quantitative studies, and I believe that success in transitioning into the field of data science cannot solely rely on luck. Before entering the industry, I had already started taking some data science courses. I firmly believe that if I want to pursue a career in this field, continuous learning is essential to bridge the gap between myself and those with a quantitative degree.
As I searched for various data science courses, I noticed that many individuals in Europe and the United States have already used online learning (~2018) to break into the industry or enhance their skills. Therefore, I decided to use online courses to build my profile, and they were all essential experiences for my career in Data Science.
Numerous platforms (Coursera, Udemy, Udacity, etc) offer top-notch online data science courses at affordable prices, and many of them provide certificates upon completion. Of course, these certificates may not hold the same weight as a Master’s degree, but taking these courses can deepen your understanding of data science and demonstrate your genuine interest in the field, which can be helpful when entering the industry.
I want to clarify that the following 5 Data Science courses are my genuine recommendations and learning experience, and I have not received any compensation for endorsing them.
I believe that the first step to becoming a data scientist is to master Python. Currently, python holds a significant position in data science, and many Python libraries are well-suited for modeling tasks as a data scientist. python is the starting point for my Data Science journey, and with this powerful toolkit, learning machine learning becomes more manageable, as you have the necessary tools for computation.
When I began learning Python, I followed this bootcamp taught by Jose Portilla. Personally, I find this course particularly suitable for beginners. Its design resembles a data scientist’s curriculum, focusing primarily on functional programming, which aligns well with the needs of data scientists. Additionally, it serves as a seamless transition to another intermediate-level course (introduced below).
2020 Complete Python Bootcamp: From Zero to Hero in Python
After completing the previous bootcamp course, I immediately enrolled in this data science course. This course teaches several crucial libraries in data science: Numpy, Pandas, Matplotlib, and Scikit Learn. Although the course only scratches the surface, it serves as an excellent intermediate-level course, bridging python to practical data science operations.
Let me explain why Numpy, Pandas, Matplotlib, and Scikit Learn are so important. Scikit Learn is easy to understand and almost encompasses “every” Machine Learning model, allowing you to quickly train, test, and deploy models, kick-starting your Machine Learning journey. However, having these skills alone is not enough. Data analysis, transformation, and visualization before modeling are equally crucial aspects. I delve deeper into how these pre-modeling tasks affect your model’s performance in another article, which I encourage you to read.
Pandas, Matplotlib, and Numpy offer a wealth of API functions to analyze, transform, and visualize data. This is why many Data Specialists prefer using Python — the convenience and practicality they provide make data manipulation a breeze for Data Scientists. That’s also why I recommend everyone to learn about these libraries. Until 2023, even though many alternatives try to compete with these libraries, it is still hard to find one which can replace one of them in the near future.
Python for Data Science and Machine Learning Bootcamp
Machine Learning (ML)plays a significant role in data science, and any comprehensive data science curriculum would undoubtedly include a ML course. This particular ML course is taught by the renowned former Google Brain project developer and co-founder of Coursera, Andrew Ng, making it quite famous in the data science community. Even if you eventually decide not to take this course, I recommend everyone to explore Coursera and Andrew Ng’s background.
The ML course offered on Coursera is a beginner to intermediate-level course, covering introductory concepts and training algorithms in Machine Learning. As a ML beginner, I highly recommend you take this course. Andrew Ng provides detailed explanations for most ML concepts while also simplifying some complex mathematical proofs, leaving them for those who are interested to explore on their own.
Furthermore, he teaches some industry practices in modeling, such as train-test split, validation, early stopping, etc. While these practices are not absolute, they still serve as valuable templates for modeling in today’s context.
Coursera is a Massive Open Online Course (MOOC) platform founded by Andrew Ng. It offers courses co-hosted with universities from around the world, with a focus on technology and business. You can choose to audit or subscribe to a course, with the former being free but without access to course assignments, and the latter providing full access to the entire course, including certification, at the cost of a monthly subscription fee (pricing may vary depending on the course, refer to the official website for accurate details).
Regardless of whether you complete this specific course, I highly recommend exploring Coursera as a platform. While there are other well-known online platforms like Edx, Udemy, and Udacity, I believe Coursera offers excellent value for money, reasonable pricing, and generally maintains a university-level standard for its courses. Other platforms can be evaluated based on personal preferences and needs.
Deep Learning is a specialization, part of a series of courses, and an extension of ML. It comprises five courses that delve deep into the concepts of Deep Learning, Neural Networks, and their applications in various domains. This is an advanced-level course, so it is advisable to have a certain level of Python coding experience and basic knowledge of Machine Learning before enrolling. Throughout the entire specialization, I found the Convolutional Neural Network (CNN) section to be taught exceptionally well, with numerous classic CNN examples that greatly aid in understanding Computer Vision.
For those who have completed the above courses, I highly recommend taking on the challenge of this data science course. It covers many fundamental aspects of Deep Learning, providing you with a more comprehensive understanding of data science. Deep Learning is a crucial component of data science, driving the trends in AI and Big Data, and sparking various imaginative possibilities in the field of Gen AI. As a data scientist, familiarizing yourself with deep learning will undoubtedly deepen your fascination with data science.
Finally, I encourage everyone to take some courses on relational databases and SQL. In another article, I mentioned the importance of SQL. SQL is vital for extracting data from databases, and without data, Data Scientists’ various modeling theories would be futile. Moreover, Data Scientists often need to manage the entire data pipeline (especially in a startup), and SQL plays a crucial role throughout the process, from ETL operations to data extraction for reporting purposes.
There are many different types of relational databases available on the market, such as Postgres, MySQL, Oracle, SQL Server, etc., and all of these databases support SQL. Among them, I highly recommend learning Postgres. Postgres is an Object Relational Database Management System, primarily designed based on the Unix platform (which can be understood as a prototype for MacOS). For beginners, I recommend choosing either MySQL or Postgresql as the entry point. Personally, I lean towards recommending Postgresql because it offers a broader range of functions, and its library for connecting with Python, psycopg2, is continuously updated. From a data scientist’s perspective, these features make it easier to fetch the required data efficiently.
The Complete Python/PostgreSQL Course 2.0
Each individual’s journey in learning data science is unique. The five courses mentioned above were the ones I took when starting out (in 2017-18), but it doesn’t mean that other online courses are not valuable. I encourage everyone to do thorough research and design their own Data Science Learning Path.
Becoming a data scientist requires equipping oneself with the necessary knowledge and skills. Only then can you convince others and, more importantly, convince yourself that you are a competent data scientist. In the increasingly competitive landscape of data scientist positions, having a well-rounded understanding of data science is the key to success. I firmly believe that even as data science continues to evolve, continuous learning is the path to becoming a more valuable data scientist.