Statistics in Data Science
Data collection, analysis, and interpretation are the core competencies of the academic and professional discipline of statistics. Additionally, experts who use statistics need to be able to articulate their findings. As a result, statistics is a key tool for data scientists, who must collect, evaluate, and present their findings after working with vast amounts of structured and unstructured data.
Important Statistics Concepts in Data Science
Data scientists need to comprehend the core ideas of descriptive statistics and probability theory, including the crucial ideas of the probability distribution, statistical significance, hypothesis testing, and regression, according to Elite Data Science, a platform for learning data science. Machine learning also benefits from the use of Bayesian thinking, which emphasizes the importance of conditional probability, priors and posteriors, and maximum likelihood.
Probability Theory
According to Encyclopedia Britannica, probability theory is a branch of mathematics that evaluates the probability that a random event will occur. A physical scenario in which the outcome cannot be predicted until it is observed is referred to as a random experiment. similar to flipping a coin Probability is a quantitative number that ranges from 0 to 1 and expresses the likelihood of an event occurring. The likelihood of something happening rises as the probability increases (gets closer to one). Flipping a coin has an equal chance of coming up with heads or tails, hence the probability is 0.5.
The foundation of data science is based on the laws of probability and statistics. Making a prediction is made considerably easier by using the probability theory. Predictions and estimates are crucial components of data science.
Descriptive Statistics
The fundamental characteristics of a data set can be analyzed and identified using descriptive statistics. Data summaries, descriptions, and a way to visualize the data are all provided by descriptive statistics. It's challenging to review, summarise, and present a lot of raw information. You can present the data in a meaningful fashion using descriptive statistics.
According to Towards Data Science, a blog for the data science sector, important analyses in descriptive statistics include the normal distribution (bell curve), central tendency (the mean, median, and mode), variability (25 percent, 50 percent, and 75 percent quartiles), variance, standard deviation, modality, skewness, and kurtosis.
Probability Distribution For Data Scientists
Data scientists frequently use probability to model scenarios where experiments conducted under similar conditions get diverse results (as in the case of throwing dice or a coin). It is also very useful in the corporate sector.
Dimensionality Reduction For Data Scientists
Data scientists frequently use probability to model scenarios where experiments conducted under similar conditions get diverse results (as in the case of throwing dice or a coin). It is also very useful in the corporate sector.
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