There are many options for studying data science: attending classes, pursuing a bachelor’s or master’s degree in data science, enrolling in a Bootcamp program, or self-learn. Nowadays, a wealth of content is accessible on the internet, mostly for free, to assist individuals in developing the skills required for Data Science.
Three key skills are needed for Data Science:
- A programming language that is used in the data ecosystem, most often Python/R or Scala.
- SQL, which is used to manipulate and retrieve data
- Statistics and Machine Learning
It is an outstanding language to master since this is your first programming language. It is a general-purpose programming language that comes pre-loaded with a large ecosystem of data libraries (pandas, sciPy…).
It is recommended to begin with a general introduction to computer science rather than a Python course focused on details. My Mooc’s Introduction to Computer Science and Programming with Python is a good place to start if you’re unfamiliar with programming concepts.
One of the most effective methods for learning a programming language is to begin by studying some of its fundamental principles, accompanied by some projects to solidify the information. Your own basic creations enable you to see how they blend together. There is no need to look outside Python’s basic libraries at this stage.
When you feel comfy using Python in your tasks, it’s a smart idea to brush up on your knowledge of the language.
With this experience, it should be possible to operate on certain personal tasks entirely in Python, editing and processing scripts. Using the CLI to build a project is an excellent way to render this useful and fun. A file organizer is an example of a project that automatically reorganizes files into subfolders depending on their filetype and material. While this method is less effective than utilizing specialist collections, it does allow for the consolidation of this experience.
If you’ve mastered the basics of developing file-processing programs, the rational next move is to familiarize yourself with the more specialized data libraries, Pandas and Numpy. Several vendors, including DataCamp and Dataquest, provide digital guides for these repositories. Understanding how to use these libraries is often needed prior to learning how to use statistical and machine learning libraries.
SQL is a critical capability for every data scientist to acquire. They make use of it to transform and retrieve data from databases.
Among the various forms of SQL available, mastering simple analytical SQL is critical for data scientists. There are many educational tools that can assist in gaining a basic understanding of SQL. W3School offers an excellent introduction to SQL, while Code academy, Hackerrank, and Khan Academy take a more realistic approach.
SQL is best learnt by practice, and there is no easier way to do that than to experiment with various databases and attempt to make sense of them. SQLite databases are an excellent place to quickly gain expertise working with limited datasets. The primary challenge of SQL experience is locating suitable datasets.
Statistics & Machine learning
If you have not taken several statistics courses during your undergraduate studies, it is beneficial to take an introductory Statistics and Machine Learning course that covers the following topics: regression (linear/logistic), decision trees, random forest, k-means, and KNN.
It is worthwhile to get a firm grasp of the algorithms by programming them from scratch. Numerous data analysis firms will conduct an interview with you to ascertain your understanding of such fundamental algorithms. Coding them from the ground up enables you to become intimately familiar with them.
On a more realistic level, Kaggle offers several good projects for familiarizing yourself with various aspects of data science workflows. They make datasets available to make it possible to see how other people solve similar issues. At the very least, it is worthwhile to attempt any of the tasks in order to gain familiarity with the various machine learning libraries and preprocessing moves. Kaggle contests are frequently overly focused on simulation rather than data transformation, and they frequently make use of very clean databases.
After completing a few Kaggle challenges, it’s critical to gain realistic experience working with actual datasets. Acquire experience in cleaning databases, debugging, and resolving problems that occur during training on those datasets. This can be accomplished by either extracting data from blogs or by gaining project expertise on websites such as freelancer.com.
There are many tools available to assist self-education in various fields of data science. It is critical to gain more coding skills, both theoretically and through project work. Combine this with a firm grasp of the fundamental machine learning models, several Kaggle exercises, and practice on real-world datasets, and you should have the required framework for
However, the educational experience should not end there. Data Science is a field of continuous learning in which it is necessary to acquire knowledge through many axes.