In this article, you will learn about the steps to be taken to become a Data Scientist. We will look at data engineering concepts such as how to collect and transform data, extract and classify data, and visualize data. So we gathered 7 steps for Learning Data Science for you.
1. Data Analysis
Data analysis is one of the main keys to Data Science and therefore, it must be understood above all. The first step in Data-Science is understanding how to effectively analyze data.
To effectively analyze your data, you must first learn to:
- Analyze the data using Excel,
- Perform a basic analysis from spreadsheets, which are scientific data,
- Learn about the tools used for data analysis,
- Program statistical tests with Python, from the database to the processing and planning stage.
The above means you need to learn at least statistics and linear algebra at the basic level!
2. Data Cleaning
In this part, you will see the different types of data, including raw data, how to clean it up and transform it from the standard format to the correct input format. You will also see how to clean the data and turn it into a data structure that you want to use for your data analysis.
3. Data Objects
At this step, you will have knowledge about some of the elements that are responsible for your dataset. This is one of the more interesting parts because it is about designing and implementing data science projects.
At this stage, you will also understand the anomalies related to your data.
4. Data Modeling
In this phase, you are about to be directed to one of the two keys to Data Science. The data model is the way you manipulate, collect, transform your data. It is also essential to use a relational database or an object database like MongoDB.
5. Data Engineering
What we will now cover is the construction of the data engineering part, the essentials for machine learning, and the testing tools in Python. At the same time, you will learn how to structure your pipeline. You will also be able to do your statistical reasoning, classification, grouping, and more.
Use sklearn and Numpy to start your learning, then Keras and Tensorflow to boost your machine learning.
6. Data Design
The Design part is one of the main parts, where you will have step-by-step instructions on how to simplify your data by eliminating unnecessary elements.
It’s not just about doing calculations, but also understanding the structure of the data. This part will give you a good understanding that will enable you to design important deep learning pipelines.
7. Data Models
Now that you have all the basics, you can trace your model. In this seventh and final step, you will learn the patterns you should follow in order to successfully ship your Data Science applications to production.