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Make sense of your Data using Descriptive Statistics! Concepts applied in every Data Science Tasks, Basic to Advanced!
An excellent training about It Certification
Statistics For Data Science in Python [Hindi]
Why learn it? Interested in the field of Data Science & Machine Learning? Then this course is 4th step in that direction for you! Data Science and Machine learning is everywhere. And If you want to learn it, then After Gaining Knowledge Of Python & Numpy. Understanding Statistical Concepts and Applying it using Python to Gain understanding of Data is logical approach. Understanding Statistical Concepts is most important building block in the field of Data Professional. Without this, Data makes no sense. As Data science and data analytics become popular and specialized, Need for working with data array has exploded and without understanding maths behind data science, working and analyzing data makes no sense. Why? Because Descriptive Statistics Helps analyzing and working with data with very simple ways. In this course, you will learn Descriptive Statistics and Apply its concept over real life data using Python, Pandas And Scipy. We Will Start with Understanding every concept in first half of this course Once we gain in depth understanding of concepts then we will move to apply them on real world data, using Python. We Will start with Introduction to Descriptive Statistics. Then we will find out what are different measures of central tendency. We will then find out what is dispersion and how to measure it. After this we will find out what is Variance. Then we will have a look at Different Distributions and we will find out how they help us gaining understanding of data prediction. We will understand Gaussian Distribution. Then we will know about Sampling distribution. We will then find out what are skewness, Kurotsis and how we can apply them in gaining understanding of our data. Then we will move on to understand Covariance and Correlation. After this its time to apply these concept in practicals and we will do this for rest of our course. We will Apply these concept while simultaneously revising concept and applying them on different datasets. We will start with Understanding Pandas Library. We will then Find out how Mean and Median can be calculated and how we can predict data using them. We will then find out how outlier in data can affect our prediction and ways to deal with such situation in real world. The we will find out about Quantiles and Interquantile. We will find out Variance and Standard deviation of our data. Then we will start interpreting and visualization our data. We will see how Skewness and Kurtosis can help us gain understanding our data. We will find out practical application of Covariance and Correlation. After this we will have a overview of Another great library used in Data Science, which is Scipy. We will Apply all concepts in Python, Pandas And Scipy. We will then apply Interquartile range, Variance and Standard deviation on real world data. we will find out z score of our data and how to interpret data using it. then we will find out skewness and kurtosis on our data. then we will apply our knowledge on univariate data and bivariate data. and Finally we will calculate confidence Interval for our data.
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