Statistics for Data Science: A complete guide for beginners
Statistics is one of the core disciplines of Data Science. Statistics is a vast field of study and Data Science requires only certain knowledge areas from Statistics such as data harnessing from various sources, understanding types of data and mathematical operations that can be performed on it, exploratory data analysis, measures of central tendencies, and variability, hypothesis testing, etc. As Data Science is about deriving insights from Data, Statistics becomes an important knowledge area.
Categories in statistics:
- Descriptive Statistics - This offers methods to summarise data by transforming raw optical discernments into consequential information that is facile to interpret and apportion.
- Inferential Statistics - This offers methods to study experiments done on minuscule samples of data and chalk out the inferences to the entire population (entire domain).
Statistics and Machine Learning
The core of machine learning is centred around statistics. You can’t solve authentic-world quandaries with machine learning if you don’t have good prehension of statistical fundamentals. There are certainly some factors that make learning statistics hard. I'm verbalizing about mathematical equations, greek notation, and meticulously defined concepts that make it arduous to develop an interest in the subject.
We can address these issues with simple and clear explications, congruously paced tutorials, and hands-on labs to solve quandaries with applied statistical methods. From exploratory data analysis to designing hypothesis testing experiments, statistics play an integral role in solving quandaries across all major industries and domains.
Anyone who wishes to develop a deep understanding of machine learning should learn how statistical methods form the substructure for regression algorithms and relegation algorithms, how statistics sanction us to learn from data, and how it avails us to extract meaning from unlabeled data.
Why 'Statistics for Data Science' Course?
Statistics as a field is vast and requires significant time and effort to gain practitioner-level knowledge. This course is specifically designed to impart required statistics knowledge for data science practitioners. The syllabus of the course is in line with the international curriculum including knowledge areas of statistics that are quintessential for data science.
Statistics for the Data Science course takes a practical approach by applying statistics concepts through case studies in the python platform, thereby enabling the candidates to learn essential statistics in the shortest and efficient way.
Advantages of learning 'Statistics for Data Science' course
- Statistics is an essential skill in the field of Data Science. This course enables data science aspirants to gain statistics skills in the most effective way
- The course also takes a practical approach; thereby the candidates learn to apply these techniques for any data problems in various areas
Who should choose this 'Statistics for Data Science' course?
- Business Analytics practitioners improve their analytics works through statistical methods
- Data Engineers and related professionals enhance their statistics knowledge
- Managers, who require making decisions based on data
- Anyone aspiring to become a Data Science professional
- Research students to gain essential statistics skills
Every organization is striving to become data-driven. Therefore, we are witnessing such an incrementation in demand for data scientists and analysts. Now, to solve problems, answer questions, and map out a strategy, we require to make sense of the data. Fortuitously, statistics offers an accumulation of implements to engender those insights.
About Sankhyana: Sankhyana Consultancy Services is India’s Premium and best data analytics training institute in India offers the best classroom, online/ live- web, corporate & academia Training on SAS & Data Management tools. Our programs feature instructor-led classroom and real-world projects to ensure you get hands-on experience and relevant skills.
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