# Data Science & Python - Maths, Models, Stats PLUS Case Study #### TOP

##### Alpha and Omega
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Access MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.8 GB | Duration: 14h 37m
Learn statistics, inferential tests, supervised & unsupervised learning, data science careers PLUS Python & libraries

What you'll learn
Introduce data and information concept
Identify difference between business intelligence and data science
Understand and learn process of data science
Define demand and challenges for data scientists
Identify the difference between dispersion and descriptive vs inferential statistics discussion
Learn after install anaconda steps to be follow
Learn spread of data discussion and inter quartile range
Define advantages of getting conditional probability based on example
Identify advantage of calculating z scores and other factors
Learn calculating p value and learning other factors on p value
Know the Prerequisites and Questions for a Data Scientist
Learn Types of Data Acquisition
Know Career Aspects for a Data Scientist
Discuss Mathematical and Statistical Concepts and Examples
Learn Descriptive and Inferential Statistics and factors of it
Learn how to use Jupyter application
Calculate variance and discussing other factors
Get Conditional Probability based on example
Learn what is Distribution and Probability Density
Learn Z test and finding percentage under the curve
Compare Mean and Variable discussion
Learn what is chi squared test and discussing based on example data
Learn Data Preprocessing in Python
Checking Array and Dimension Shape and Discussing on Encode Window
Learn why is data visualization is important and how to use it
Learn Parametric Methods and Algorithm Trade Off
Learning Classification and Concept on learning
Learn K means Clustering and Algorithm
Doing cluster and using sklearn on it and encoding other factors
Learn TP,TN,FP and FN of Confusion Matrix and Discussing accuracy
Learn Classification report and calculation on encoding window on Python
Requirements
No programming experience necessary, you will learn everything you need to know
Description
Introduce yourself to our community of students in this course and tell us your goals with data science
Encouragement and celebration of your progress every step of the way: 25% > 50% > 75% & 100%
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 difference between business intelligence and data science
Understand and learn 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 install anaconda steps to be follow
Learn spread of data discussion and inter quartile range
Define advantages of getting conditional probability based on example
Identify 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 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 on 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.
Our happiness guarantee...
We have a 30-day 100% money-back guarantee, so if you aren't happy with your purchase, we will refund your course - no questions asked!
We can't wait to see you on the course!
Peter and Laika
Who this course is for
This course is for anyone interested in data science, machine learning, stats, probability and business intelligence
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