Data science can be a daunting field. Many people will tell you that you cannot become a data scientist unless you have mastered statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and other subjects. That is simply not the case.
This workflow does not necessitate advanced mathematics, deep learning mastery, or any of the other skills listed above. However, it does necessitate knowledge of a programming language as well as the ability to work with data in that language. And, while mathematical fluency is required to excel in data science, a basic understanding of mathematics is all that is required to get started.
True, the other specialized skills listed above may one day assist you in resolving data science problems. However, you do not need to be proficient in all of these skills will help you how to learn data science. You can start right away, and we are here to help!
Step 1: Get to know Python
Python and R are both excellent data science programming languages. R is more popular in academia, while Python is more popular in the industry, both languages have a plethora of packages that support the data science workflow. We’ve taught data science in both languages and prefer Python in general.
To get started, you don’t need to know both Python and R. Instead, concentrate on learning a single language and its ecosystem of data science packages. If you’ve decided on Python (our recommendation), you should consider installing the Anaconda distribution, which simplifies package installation and management on Windows, OSX, and Linux.
Step 2: Learn visualization, data analysis, and manipulation
Similar to an Excel spreadsheet or SQL table, pandas provides a high-performance data structure (called a “DataFrame”) that is suitable for tabular data with columns of various types. It contains tools for reading and writing data, dealing with missing data, filtering data, cleaning messy data, merging datasets, visualizing data, and much more. In short, learning pandas will significantly improve your data-processing efficiency.
Pandas, on the other hand, have an overwhelming amount of functionality and (arguably) provide too many ways to accomplish the same task. These characteristics can make learning pandas and discovering best practices difficult.
Step 3: Understand Machine Learning
The exciting part of data science is creating “machine learning models” to predict the future or automatically extract insights from data. Scikit-learn is the most popular machine learning library in Python, and for good reason:
- It provides a clean and consistent interface to a wide range of models.
- It provides numerous tuning parameters for each model while also selecting sensible defaults.
- Its documentation is excellent, and it will assist you in understanding the models as well as how to use them correctly.
Step 4: Keep Practicing
Here’s our best advice for honing your data science abilities: Find “the thing” that motivates you to put what you’ve learned into practice and learn more, and then do it. Personal data science projects, Kaggle competitions, online courses, reading books, blogs, attending meetups or conferences, or anything else! This was in nutshell about Data science as a career aspect. To know more about how to become a computer science engineer, click here.
