According to Elite Data Science, a data science educational platform, data scientists need to understand the fundamental concepts of descriptive statistics and probability theory, which include the key concepts of probability distribution, statistical significance, hypothesis testing and regression.
Do I need statistics for data science?
Both tasks require statistical knowledge so it is a must-have skill for data scientists. Data science is an interdisciplinary field. Statistics is an integral part and an absolute requirement for data scientists. Without a decent level of statistical knowledge, we can only be a tool expert.Jun 4, 2021
How do you use statistics in data science?
In data science, statistics is at the core of sophisticated machine learning algorithms, capturing and translating data patterns into actionable evidence. Data scientists use statistics to gather, review, analyze, and draw conclusions from data, as well as apply quantified mathematical models to appropriate variables.
Where can I learn statistics for data science online for free?
- Statistics with R Specialisation by Coursera (Duke University)
- Intro to Statistics by Udacity (Stanford University)
- Statistical Learning by Stanford University.
- Introduction to R by Leada.
- Statistics: The Science of Decisions by Udacity (San Jose State University)
Where can I practice data science skills?
Take online courses through Coursera, Treehouse, Lynda and CodeSchool that focus on the skills you want to learn. The courses don't necessarily have to be related to data science. A programming course in a relevant language, like Python, works too.
What level of statistics is needed for data science?
For example, data analysis requires descriptive statistics and probability theory, at a minimum. These concepts will help you make better business decisions from data. Key concepts include probability distributions, statistical significance, hypothesis testing, and regression.
Do I need to learn statistics for data science?
Data scientists work as programmers, researchers, business executives, and more. However, what all of these areas have in common is a basis of statistics. Thus, statistics in data science is as necessary as understanding programming languages.
How long does it take to learn statistics for data science?
On average, it takes approximately 6 to 7 months for an individual to become moderately proficient in the field of data science. However, by having a well-structured and thought through plan, and by committing yourself to it, you can considerably expedite this learning process and timeline.
What is the fastest way to learn data science?
- Micro courses by Kaggle. Kaggle is the best learning resource which not only teaches you the theory but also helps you follow a practical approach to learning.
- Python.
- Pandas.
- Data visualisation.
- Intro to machine learning.
- Intermediate machine learning.