Data Science & Python - Maths, models, Stats PLUS Case Study

Description

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Over 14 hours of clear and concise step-by-step instructions, lessons, and engagement

This data science course provides participants with the knowledge, skills, and experience associated with Data Science. Students will explore a range of data science tools, algorithms, Machine Learning, and statistical techniques, with the aim of discovering hidden insights and patterns from raw data in order to inform scientific business decision-making.

What  you will learn:

• Introduce data and information concept

• Identify the difference between business intelligence and data science

• Understand and learn the process of data science

• Define demand and challenges for people working in data science

• Identify the difference between dispersion and descriptive vs inferential statistics discussion

• Learn after installing anaconda steps to be followed

• Learn spread of data discussion and interquartile range

• Define advantages of getting conditional probability based on example

• Identify the advantage of calculating z scores

• Learn calculating p-value and learning factors on p-value

• Know the Prerequisites and Questions for a Data Scientist

• Types of Data Acquisition

• Know Career Aspects for Data Science

• Discuss Mathematical and Statistical Concepts and Examples

• Descriptive and Inferential Statistics

• how to use the Jupyter application

• Calculate variance

• Get Conditional Probability based on example

• Distribution and Probability Density

• Z test and finding percentage under the curve

• Compare Mean and Variable discussion

• chi-squared test and discussing based on example data

• Data Preprocessing in Python

• Checking Array and Dimension Shape and Discussing on Encode Window

• Why is data visualization important in data science and how to use it

• Parametric Methods and Algorithm Trade-Off

• Classification and Concept of learning

• K means Clustering and Algorithm

• Doing cluster and using sklearn on it and encoding

• TP,TN,FP and FN of Confusion Matrix and Discussing accuracy

• Classification report and calculation on encoding window on Python

• ...and more!

Contents and Overview

You'll start with Data and Information Concept; Difference between Business Intelligence and Data Science; Business Intelligence vs Data Science based on parameters factors; Prerequisites and Questions for a Data Scientist; Questions on applying as a Data Scientist – Statistics and Data Domain; Prerequisite on Business Intelligence and discussing tools on Data Science; Types of Data Acquisition; Data Preparation, Exploration, and its factors; Process of Data Science; Know Career Aspects for a Data Scientist; Demand and Challenges for Data Science; Discussion of Mathematical and Statistical Concepts and Examples; Discussing Variables – Numerical and Categorical; Discussing Qualitative Variables and Central Tendency; Dispersion and Descriptive vs Inferential Statistics Discussion.

Descriptive and Inferential Statistics; Descriptive Statistics, Examples and steps on installing Anaconda; Steps to follow after installing Anaconda; Using Jupyter on Anaconda Application; how to use Jupyter application; Continuation of Jupyter application, explanation, and discussion; Getting data and putting data on Jupyter; Minimizing data to be see on Jupyter app and bringing data from Excel; Explaining modes used on Jupyter app on Data statistics and Analysis; Variables – continues and categorical variable; Inputting and typing data on Jupyter app; Getting mean data on Jupyter based on example; How to summarize data of median and mean; Inputting quantiles data and explaining factors; Spread of data discussion and interquartile range; Interquartile range and inputting data; Variance averaged deviation on the mean; Calculating variance; Discussing degree of freedom based on variables and calculation; Introduction to probability and overview of the lesson; Getting Conditional Probability based on example; Continuation of example based on students data on probability; Make a new column for absences and column for pivot table; Calculating and encoding of the result of condition probability of students.

We will also cover Inferential statistics; Distribution and Probability Density; Gaussion Distribution; Define distribution parameters and graphing normal distribution;  PDF and CDF - Cumulative Distribution Function; Learn what is Correlation Coefficient, Z score, and Z test; Calculating Z scores; What does Z scores tell you?; Z test and finding percentage under the curve; Getting the mean, getting data, hypothesis and comparing mean; Comparing Mean and Variable discussion; Continuing Z test, Calculating P test and continuing steps on Z test; Doing small Z test, Stats, and discussing factors; Null Hypothesis, run Z test, finding and defining P value; Calculating P value and learning factors on P value; T test, Diamond data test and mean of concerned value; how to import data set, t test and learning; Learn what is correlation coefficients, scatter plot , calculation; Getting scatter plot data correlation.

This course will also tackle chi squared test and discussing based on example data; Chi square test , getting data set and discussing factor; Chi2 contingency method discussion and result on data ci square test; Data Preprocessing in Python – Step 1: Importing the libraries; Step 2 importing data set; Step 3 handling the missing values; Step 3 continuation and factors; Step 4 Encoding categorical data; step 4 label encoding; step 5 Normalizing the data set; step 6 Splitting the data set; numpy and pandas and The numpy ndarray A multidimensional; Learn Checking Array and Dimension Shape and Discussing on Encode Window; Learn panda series and creating a panda series; data frame on panda series and know how to use reindex function; Learn Pandas Dataframe; Learn what is data visualization; why is data visualization is important and how to use it; Learn plotting libraries and know its steps; Learn what is machine learning; Learn Examples of Learning Problems, Research Fields, and Applications; Discussing the Learning Problem; Learn what is Prediction and its examples; Parametric Methods and Algorithm Trade Off; Supervised and Unsupervised Learning Terminology, and Regression vs Classification; Assessing model accuracy, Bias and Variance learning of methods and Test MS; Doing linear regression on code window; Doing scatter plot method to get linear regression; from sklearn linear model to linear regression regressor; Finding intercept regression or regressor and learning other factor; Sklearn import metrics and getting the final data on linear regression.

Next, we will discuss Learning Classification and Concept on learning; machine learning areas and Important concepts; Example of spam filter, Label data and unlabelled data, Training vs error; Classification has 2 step process, Issues Data preparation; Learning decision trees and sample problem; Learn Decision Tree Induction – Training dataset and discussing examples; Doing decision tree classification on Python; Importing some libraries and data, factors and format ; Continuation with understanding the data and discussing it; Checking on train test split and creating decision tree classified; Solution on tree plot tree too interpret data and what is Gini index, K means Clustering and Algorithm; Stopping/Convergence Criterion giving examples and Algorithm K means; Strength and weakness of K means and discussing factors; how clustering K means method works and learning factors; Combining data processing and getting data and encoding factors; Label encoding code to use, data encoding, using transform ; Doing cluster and using sklearn; Continuation of k-means clustering and other factor on coding Python; Preview on Data in sales and other factors and topic; Data science use cases in sales , Case study – future sales prediction ; Describing the data on mean standard deviation and factors; Load data, Removing the index column and Relationship between Predictor.

Then, how to change the default policy; Accuracy, MSE, RMSE, RSquare, Seaborn Library; machine learning model building; Evaluation metrics and different evaluation matrix and confusion matrix; TP,TN,FP and FN of Confusion Matrix and Discussing accuracy; precision, recall and F1 score in data science; Learn Classification report and calculation on encoding window on Python.

Who are the Instructors?

Laika Satish is your lead instructor – a professional making a living from teaching data science. As a data science expert, she has joined with content creator Peter Alkema to bring you this amazing new course.

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