Data science can seem overwhelming. Many will tell you that a data science course is challenging. You need to master linear algebra, advanced calculus, statistics, programming, distributed computing, databases, visualization, machine learning, natural language processing, deep learning, clustering, experimental design, visualization, and more. It is simply false.
Data science is the act of asking questions and then using data to answer them. The data science workflow generally looks this way:
– Asking questions
– Collecting data that could help to answer the questions
– Cleaning the data
– Visualizing, analyzing, and exploring the data
– Constructing and testing a machine learning model
– Communicating the results
The workflow does not require any advanced mathematics or deep learning skills. It does require knowledge of a programming language as well as the ability to manipulate data using that language. To be able to do data science well, you don’t need to have a solid mathematical background.
To enroll in the best data science programs and start your data science career, you don’t have to be able to master every skill, but here are some that can help:
Both R and Python are excellent programming languages for data science. R is more common in academia and Python more in industry. However, both languages offer a wide range of packages to support data science workflows. Both languages are suitable for data science, but Python is preferred when it comes to data science.
Data Analysis, Visualization, and Manipulation using Pandas
You should know how to use the pandas library to work with Python data.
Pandas offer a high-performance data structure known as “DataFrame” that can be used for tabular data. It is similar to an SQL table and Excel spreadsheet. You can use it to read and write data, merge datasets, visualize data, handle missing data, filter data, clean data, merge data, and many other functions. Thus, learning about Pandas will significantly increase your productivity when working with data.
Machine learning is a complicated field. However, there are some tools that help perform machine learning effectively, but it does not answer many of the critical questions like:
– How can I determine which machine learning model is best for my data?
– What can I do to interpret the results from my NLP model?
– How can I determine if my model will be generalized to future data?
– How can I choose which features should be in my model?
– And so on.
Machine learning is a skill that can be improved upon. However, it requires further study and experience.
Continue Learning and Practicing
The best advice to improve your data science skills is to find “the thing” that motivates and inspires you to put into practice what you have learned. Then, proceed ahead without any hesitation. It could be personal data science projects or Kaggle competitions. It could also include a data science course, reading books, blogs, attending meetups and conferences, or reading books.
Your journey to data science has just begun. Data science is a vast field that requires a lifetime of learning. However, remember that you don’t need to know everything to start your data science career. Just enroll yourself into the best data science programs and get started.