## What you would learn in Data Science & Python - Maths, Models, Stats PLUS Case Study course?

This course in data science equips students with the necessary knowledge, abilities, experience, and skills associated with Data Science. Students will learn about a range of tools for data science, including algorithmic techniques, Machine Learning, and statistical methods, to identify confidential information and patterns in raw data to aid in the business process with scientifically-based decision making.

What you'll learn:

Introduce the concepts of data and information

Find the difference between business intelligence and data science.

Learn and understand the data science process

Define the need and the challenges facing individuals working in data science.

Find the difference between descriptive and dispersion inferential discussion of statistics

Find out after installing anaconda the steps to adhere to

Find out about the data spread discussion and inter-quartile range

Define the advantages of obtaining conditional probabilities based on an example

Find the benefit of using z scores to calculate

Learn to calculate the p-value and the learning aspects of the p-value

Be aware of the requirements and questions for Data Scientists

Different types of data acquisition

Learn about the Career Pathways for Data Science

Examine Mathematical concepts and statistical ones, and examples

Inferential and descriptive statistics

How to make use of the Jupyter application

Calculate the variance

Find Conditional Probability using an examples

The distribution and probability density

Z test, and getting the percentage of the curve

Compare between Means and Variables discussions

Chi-squared test and discussion of the results based on examples of data

Data Processing in Python

Examining Dimension Shape and Array and discussing on Encode Window

What is the significance of data visualization in data science? And how to make use of it

Parametric Methods and Algorithm Trade-Off

Learning Concept and Classification

K stands for Clustering and Algorithm.

Doing clusters and using sklearn and encode

The FN, TP, TN, and TP of the Confusion Matrix and Discussion of the accuracy

Classification report and calculation of Encoding window in Python

...and more!

Contents and an Overview

It will start with Data and Information concepts, The difference Between Data Science and Business Intelligence as well as Data Science; Business Intelligence and Data Science based on parameters elements; Prerequisites and Questions for an applicant to become a Data Scientist; Questions on how to become Data Scientist. Data Scientist - Statistics and Data Domain; Prerequisite on Business Intelligence and discussing tools for Data Science; Types of Data Acquisition and Data Preparation Exploration, Preparation and the factors that influence it and the process for Data Science; Know Career aspects for Data Scientists Data Scientist; Demand and the challenges of Data Science; Discussion of mathematical and statistical concepts and examples; discussing Variables - Categorical and Numerical; Discussing Qualitative Variables as well as Central Tendency Dispersion and Descriptive vs .. Inferential Statistics Discussion.

Inferential and Descriptive Statistics; Descriptive Statistics, Examples, and the steps to install Anaconda and steps to follow following the installation of Anaconda and using Jupyter to access the Anaconda Application; how to utilize Jupyter application; continuation on Jupyter application, its explanation, and discussion; getting data and then putting it onto Jupyter minimizing the data that is visible in Jupyter app and then importing the data into Excel; Explaining modes used in Jupyter application on data statistics and analysis; Variables - categorical and continues variables entering and typing data into Jupyter application; getting the mean data from Jupyter with an example how to summarise data of median and mean; inputting quantiles of data and explaining variables; spread of discussion on data and interquartile range. Interquartile range and entering data and calculating variance averaged deviations on the mean Calculating variance; Discussion of the degree of freedom using variables and calculations of probability; Introduction to the concept and an overview of the lesson. Obtaining Conditional Probability using an example; Continued discussion of the example using data from students on probability. Make an additional column for absences and another column for the pivot tables. Calculating the recording of the results of the conditional probability of students.

We will also discuss Inferential statistics, Probability Density, and Distribution Gaussion distribution; define parameters for distribution and graphing normal distributions PDF and CDF Cumulative Distribution Function; Understand Correlation Coefficients, Z score, and Z test, and calculate Z scores. What do Z scores reveal about you?; Z test and finding percent under the curve Finding the mean, gathering data, hypotheses and the comparison of mean and Variable discussions; Continuing Z test, calculating P-value and completing steps of Z test; Practicing minor Z test, testing statistics, and discussing the factors; Null Hypothesis, running Z test, determining P-value and defining it; Calculating the P-value and learning variables on P-value Test test Diamond data test the mean value of the test and how to import data sets testing, t-test, and learning learn about correlation coefficients, scatter plots, calculation, and obtaining a scatter plots for data correlation.

The course will also cover the Chi-squared test and discuss using examples of data Chi-square test, gathering data and discussing the factors Chi2 contingency methods discussion and the results of the test of data ci square; Data processing using Python Step 1: Importing libraries; Step 2: Importing data set; Step 3 dealing with the missing values step three continuations and factors, Step four Coding categorical data and label encoding step 4 and step 5 normalizing of the set data. Step 6. splitting the data set. Numpy pandas and pandas, and the numpy ndarray Multidimensional; Learn how to check Dimension Shape and Array; and discussing the Encode Window; learn about the panda series and create a panda-based series; Data frame of panda series and learn how to use the reindex function. Learn Pandas Dataframe. Learn the meaning of data visualization, why it is essential, and how to use it. Learn about the plotting library and understand the steps involved; Find out the meaning of machine learning and learn examples of learning Problems. Research Fields and Applications are discussing the Learning Problem and what it means. Prediction and the various examples it has Analytical Methods and Parametric Trade-off; Supervised as well as unsupervised learning terminology, and Regression vs. Classification. Assessing the accuracy of the model, bias, and Variance in learning of Methods and Test MS Regressions that are linear on a code window; Using the scatter plots to obtain linear regression. From the sklearn linear model to the linear regression regressor. Finding an intercept regression or regressor and learning another factor; sklearn how to import metrics, and then get the final results on linear regression.

The next step is to discuss Learning Classification and concepts of learning; areas of machine learning and important concepts; an example that includes spam filter label data and data that is not labeled, training vs. error; Classification is a two-step process, and issues regarding data preparation; learning decision trees and sample problems learning decision tree induction Training dataset and discuss some examples; Performing classification of decision trees using Python and importing some libraries, the data, factors, and format and Continuation of studying what data is being discussed and. Examining the split of train tests and making a decision tree classification; solution for tree plot tree to understand and interpret data. Gini Index, what K stands for Clustering and Algorithm, Stopping/Convergence Criteria with examples and what Algorithm K is the strength and weaknesses of K and discussing its factors that affect how the clustering method functions and the factors that influence learning including data processing, getting the data and encoding variables such as label encoding codes to be used, data encoding with transform clusters and using sklearn. The continuation of k-means-based clustering and other factors on programming in Python Preview on data in sales and other variables and subject using data science in sales, Case study for future sales predictions; providing the data on average standard deviation as well as factors that are used to load data; removing the index column, and establishing a relationship between Predictor and.

If you want to modify the default policy? Precision, MSE, RMSE, RSquare, Seaborn Library; the creation of models for machine learning Evaluation metrics, and the various assessment of confusion and matrix the FN, TP, TN, and TP of the Confusion Matrix, and discussing accuracy; precision; recall, and F1 score in data science. Learn Classification analysis and report of encoding window in Python.

### Course Content:

- Introduce the concept of information and data
- Find the difference between business intelligence and data science.
- Learn about and comprehend how data science works.
- Define the demand and issues for data scientists
- Define the distinction between descriptive and dispersion inferential statistics. Discussion
- Find out after installing anaconda the steps to adhere to
- Find out about the data spread discussion and inter-quartile range
- Define the advantages of obtaining conditional probability based on an example
- Find the benefit of calculating Z scores and other variables
- Learn how to calculate p-value and discover other aspects of the p-value
- Be aware of the requirements and questions for Data Scientists
- Learn about the various types of data acquisition
- Learn about the career options of Data Scientists
- Discussion of Mathematical as well as Statistical concepts as well as examples
- Learn about descriptive and inferential statistics and other aspects of it
- Learn to use the Jupyter application.
- Determine the variance and discuss other aspects
- Learn about Conditional Probability based on an example
- Find out what is distribution and Probability Density
- Learn the Z test and determine percentages below the curve
- Compare between Means and Variables discussions
- Find out what is the Chi-squared test, and then discuss using examples of data
- Learn about Data Preprocessing in Python
- Reviewing Dimension Shape and Array and chatting on Encode Window
- Find out why data visualization is important and how you can use it.
- Learn Parametric Methods, and Algorithm Trade-Off
- Learning Classification and Concepts of learning
- Learn K refers to Algorithms and Clustering
- Doing clusters and using sklearn and also encoding other variables
- Learn about TP, TN, and FN of Confusion Matrix, as well as discuss the accuracy
- Learn Classification report and calculation on the encoding window of Python

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