One day, I signed up for a Data Science Boot Camp.
One of the students asked the teacher, “Is it necessary to know statistics to be a data scientist?”
The teacher replied, “No, it’s not very necessary, but it’s good to know.”
Then he added, “I don’t have any statistics knowledge either, but I know how to code, so that’s enough for me.”
I immediately dropped out of the course and asked for a refund.
Time, Data, and Statistics
In the age of AI, data and related things are changing faster than ever before. In order not to be left behind in the future, as I have written before, now it is time for families, educational institutions and individuals to teach children and even adults how to code along with the alphabet. But that’s not the point. Since we must move quickly in accordance with the requirements and rules of the new era, we must now understand very well how to process data. But coding alone will not be enough. If I had my way, I would always put math and statistics classes at the forefront of my personal education.
I would like to clarify a concept that no one still knows, that is, artificial intelligence (AI) and machine learning (ML) are two important subfields of data science.
Data and Data Professions and Statistics
I have always been surprised to see many people around me introduce themselves as data scientists without any knowledge of statistics. Personally, I don’t think even a data analyst can be without statistics knowledge.
Statistics is the foundation of data science. Statistical concepts and techniques are required to understand data, build models, and make predictions. As a data scientist, your chances of being successful without statistics knowledge are very low.
Statistics is an integral part of data science and it is not possible to learn data science from a teacher who is not knowledgeable in this field.
Here are some reasons why statistics knowledge is important for data science:
- Statistics helps to understand data. Statistical concepts and techniques are necessary to analyze data and extract meaningful information. For example, a data scientist can use statistical analysis to analyze sales data and predict future sales trends.
- Statistics helps to build models. Statistics is required to build models that learn from data and can predict future data. For example, a data scientist can use customer data to identify customer segments for a company and create marketing campaigns for those segments.
- Statistics helps to make predictions. Statistics is required to make predictions based on the information obtained from data. For example, a data scientist can use weather data to make predictions about future weather.
If you want to be successful in the field of data science, you must have knowledge of statistics.
Data science without statistics is like
- a chef without a recipe
- A map without directions. Statistics helps us navigate the complex world of data. It shows us where to go and how to get there. Without statistics, we would be lost in a sea of data, unable to find the insights we need
If you want to learn about data and statistics in a relaxing and thought-provoking way with real-life excerpts, Richard Nisbett’s “Mindware” book is a must-read for me.