Udemy – Data Science and Python – Math, Models, Stats Plus Case Studies 2023-6 – Downloadly

• Introduce the concept of data and information
• Identify the differences between business intelligence and data science
• Understanding and learning the data science process
• Defining the demands and challenges for people working in data science
• Discuss dispersion and identify the differences between descriptive and inferential statistics
• Learn the steps to follow after installing Anaconda
• Data discussion and learn how to expand the interquartile range
• Define the advantages of deriving conditional probability based on an example
• Recognize the benefits of z-score calculation
• Learn p-value calculation and learning factors on p-value
• And…

Contents and overview

You will start with the concept of data and information. Difference between Business Intelligence and Data Science; Business Intelligence vs Data Science based on parameter factors. Prerequisites and questions of a Data Scientist; Questions about applying as a Data Scientist – Statistics and Data Domain. Prerequisites for discussion of Business Intelligence and Data Science tools. Types of data acquisition; Data preparation, exploration and its agents; Data Science process; Learn the career aspects of a Data Scientist. Demands and challenges of Data Science. Discuss mathematical and statistical concepts and examples; Discuss variables – numerical and categorical. Discussion about qualitative variables and central tendency. Dispersion discussion and descriptive vs inferential statistics. Descriptive and inferential statistics; Descriptive statistics, examples and installation steps of Anaconda. Steps to be followed after installing Anaconda. Using Jupyter in Anaconda. How to use Jupyter program; Using, interpreting and discussing Jupiter; Getting data and putting data in Jupyter. Minifying data for viewing in Jupyter and importing data from Excel. Explaining the methods used in Jupyter program in statistics and data analysis. Variables – continuous variables and classification. Entering and typing data in Jupyter program. Get average data in Jupyter based on example. How to summarize median and mean data, enter quantity data and explain factors. Data discussion and expanding interquartile range. Interquartile range and input data; mean deviation of variance in mean. Variance calculation; Discuss degrees of freedom based on variables and calculation. Introduction to probabilities and overview of the lesson. Getting conditional probability based on example. Continuing the example based on students data on probability. Create a new column for absence and a column for pivot table. Calculate and encode the probability result of students bets. We will also cover inferential statistics. Probability distribution and density. Gaussian distribution; defining distribution parameters and creating a normal distribution diagram. PDF and CDF – Cumulative distribution function. Learn what correlation coefficient, Z-score and Z-test are. Calculating Z-score What do Z-scores tell you? Perform Z test and find percentage under the curve. Get the mean, get data, hypothesis and compare the mean. Mean comparison and variable discussion. Continuation of Z-test, calculation of P-test and continuation of steps in Z-test. Conducting a small Z-test, statistics and discussion of factors. Null hypothesis, performing Z test, finding and defining P value, calculating P value and learning factors on P value, T test, Diamond data test and corresponding mean value. How to import data set, T-test and learning. Learn correlation coefficient, scatterplot, calculus. Get correlation of scatter plot data. Next, we will discuss learning classification and concept of learning. Machine learning area and important concepts; An example of spam filter, labeled data and unlabeled data, training vs error. Classification has a 2-step process, data preparation issues. Decision tree learning and sampling problem. Learning decision tree induction – discussion on training datasets and examples. Performing decision tree classification in Python. Import some libraries and data, agents and templates. Continue understanding and discussing data; examining split train test and creating a classified decision tree. Solution in tree plot also explain data and what is Gini index, K means clustering and algorithms. Stopping/convergence criteria with examples and K-Means algorithm. Strengths and weaknesses of K means and discussion factors. How K-Means clustering method works and learning factors. Combining data processing and data reception and encryption agents. Label encryption code to use, encrypt data using transforms; performing clustering and using sklearn. Continuous k-Means clustering and other factors in Python coding. Preview data on sales and other factors and topics. Data science use cases in sales, case study – forecasting future sales. Description of data standard deviation and mean of factors. Data loading, deleting index columns and relationship between predictors. Sign up now and we will help you improve your data science skills!

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