Key concepts in Data Science - Part 1

 Key concepts in Data Science



Understanding the key concepts of Data Science is crucial for anyone who wants to work with data. In this chapter we can delve into some key concepts like statistics, probability and Machine Learning. In this part of the chapter we will focus about the Statistics.

Statistics

Statistics is fundamental to Data Science. We can't use our data without involving statistics. It offers various help to analyze, interpret and present the data. Statistics helps us to derive informative decisions based on the data we have.



Important concepts in Statistics

There are two types of statistics, 1. Descriptive statistics and 2. Inferential statistics.

1. Descriptive statistics - In this type of statistics the collected data are described in a summarized way, which helps to describe the main features of the data.

This descriptive statistics include Measures of Central Tendency and Measures of Dispersion. Measures of Central Tendency are the mean, median and mode. Measures of Dispersion are the range, variance, inter-quartile range and standard deviation. 

2. Inferential statistics - Inferential statistics involves making inferences about populations using data drawn from the population. It helps us generalize findings from a sample to a larger group, estimate population parameters, and test hypotheses.


The inferential statistics include Hypothesis Testing, Confidence interval and Regression analysis.

Hypothesis Testing: Hypothesis testing is a statistical method used to determine whether there is enough evidence in a sample of data to infer that a certain condition holds true for the entire population. Techniques like t-tests, chi-square tests, and ANOVA to test assumptions.

  • Null Hypothesis (H0): A statement that there is no effect or no difference. It serves as the default or baseline assumption.
  • Alternative Hypothesis (H1 or Ha): A statement that contradicts the null hypothesis, indicating there is an effect or a difference. 

    • Confidence Intervals: Range within which a population parameter lies with a certain level of confidence. It has a range of values from the sample which would likely contain the values of the population. Using this range we can estimate the parameter with associated level of confidence.

    Regression Analysis: Relationship modeling between dependent and independent variables (e.g., linear regression).

    We can learn more about the Hypothesis testing, Confidence interval and Regression Analysis in the upcoming chapters.

    Population vs. Sample:



    Population: The entire group that you want to draw conclusions about. For example, the colorful balls in the big glass jar.

    Sample: A subset of the population used to make inferences about the whole population. Example: The balls in the small white cup, it is still a part of the whole population.


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