What is Data Science?

What is Machine Learning?

What is Deep Learning?

What is AI?

Data Analytics & its types

What is Python?

Why Python?

Installing Python

Python IDEs

Jupyter Notebook Overview

Hands-on: Installing Python idle for Windows, Linux and Creating “Hello World” code

Pandas

Numpy

Sci-kit Learn

Mat-plot library

Hands-on: Installing Jupyter notebook for windows, Linux and

Hands-on: Installing NumPy, pandas and Matplotlib

Reading CSV files

Saving in Python data

Loading Python data objects

Writing data to CSV file

Hands-on: To generate data sets and create visualizations of that data. You learned to create simple plots with Matplotlib, and you saw how to use a scatter plot to explore random

Hands-on: You learned to create a histogram with Pygal and how to use a histogram to explore the results of rolling dice of different

Hands-on: Generating your own data sets with code is an interesting and powerful way to model and explore a wide variety of real-world

Hands-on: As you continue to work through the data visualization projects that follow, keep an eye out for situations you might be able to model with

Selecting rows/observations

Rounding Number

Selecting columns/fields

Merging data

Data aggregation

Data munging techniques

Hands-on: As you gain experience with CSV and JSON files, you’ll be able to process almost any data you want to analyze.

Hands-on: Most online data sets can be downloaded in either or both of these From working with these formats, you’ll be able to learn other data formats as well.

Central Tendency

Mean

Median

Mode

Skewness

Normal Distribution

Probability Basics

What does it mean by probability?

Types of Probability

ODDS Ratio?

Standard Deviation

Data deviation & distribution

Variance

Bias variance Tradeoff

Underfitting

Overfitting

Distance metrics

Euclidean Distance

Manhattan Distance

Outlier analysis

What is an Outlier?

Inter Quartile Range

Box & whisker plot

Upper Whisker

Lower Whisker

Scatter plot

Cook’s Distance

Missing Value Treatment

What is NA?

Central Imputation

KNN imputation

Dummification

Correlation

Pearson correlation

Positive & Negative correlation

Hands-on: Compute probability in a situation where there are equally-likely outcomes

Hands-on: Apply concepts to cards and dice

Hands-on: Compute the probability of two independent events both occurring

Hands-on: Compute the probability of either of two independent events occurring

Hands-on: Do problems that involve conditional probabilities

Hands-on: Calculate the probability of two independent events occurring

Hands-on: List all permutations and combinations

Hands-on: Apply formulas for permutations and combinations

Classification

Confusion Matrix

Precision

Recall

Specificity

F1 Score

Regression

MSE

RMSE

MAPE

Hands-on: State why the z’ transformation is necessary

Hands-on: Compute the standard error of z

Hands-on: Compute a confidence interval on ρ The computation of a confidence interval

Hands-on: Estimate the population proportion from sample proportions

Hands-on: Apply the correction for continuity

Linear Regression

Linear Equation

Slope

Intercept

R square value

Logistic regression

ODDS ratio

Probability of success

Probability of failure Bias Variance Tradeoff

ROC curve

Bias Variance Tradeoff

Hands-on: we’ve reviewed the main ways to approach the problem of modeling data using simple and definite

K-Means

K-Means ++

Hierarchical Clustering

Support Vectors

Hyperplanes

2-D Case

Linear Hyperplane

Linear

Radial

Polynomial

K – Nearest Neighbour

Naïve Bayes Classifier

Decision Tree – CART

Decision Tree – C50

Random Forest

Hands-on: We have covered the simplest but still very practical machine learning models in an eminently practical way to get us started on the complexity

Hands-on: where we will cover several regression techniques, it will be time to go and solve a new type of problem that we have not worked on, even if it’s possible to solve the problem with clustering methods (regression), using new mathematical tools for approximating unknown values.

Hands-on: In it, we will model past data using mathematical functions, and try to model new output based on those modeling.

Perceptron

Multi-Layer Perceptron

Markov Decision Process

Logical Agent & First Order Logic

AL Applications

CNN – Convolutional Neural Network

RNN – Recurrent Neural Network

ANN – Artificial Neural Network

Hands-on: We took a very important step toward solving complex problems together by means of implementing our first neural

Hands-on: Now, the following architectures will have familiar elements, and we will be able to extrapolate the knowledge acquired in this chapter, into novel

Text Pre-processing

Noise Removal

Lexicon Normalization

Lemmatization

Stemming

Object Standardization

Syntactical Parsing

Dependency Grammar

Part of Speech Tagging

Entity Parsing

Named Entity Recognition

Topic Modelling

N-Grams

TF – IDF

Frequency / Density Features

Word Embedding’s

Text Classification

Text Matching

Levenshtein Distance

Phonetic Matching

Flexible String Matching

Hands-on: provided, you will even be able to create new customized

Hands-on: As our models won’t be enough to solve very complex problems, in the following chapter, our scope will expand even more, adding the important dimension of time to the set of elements included in our generalization.

Start Page

Show Me

Connecting to Excel Files

Connecting to Text Files

Connect to Microsoft SQL Server

Connecting to Microsoft Analysis Services

Creating and Removing Hierarchies

Bins

Joining Tables

Data Blending

parameters

Grouping Example 1

Grouping Example 2

Edit Groups

Set

Combined Sets

Creating a First Report

Data Labels

Create Folders

Sorting Data

Add Totals, Subtotals, and Grand Totals to Report

Hands-on: Install Tableau Desktop

Hands-on: Connect Tableau to various Datasets: Excel and CSV files

Area Chart

Bar Chart

Box Plot

Bubble Chart

Bump Chart

Bullet Graph

Circle Views

Dual Combination Chart

Dual Lines Chart

Funnel Chart

Traditional Funnel Charts

Gantt Chart

Grouped Bar or Side by Side Bars Chart

Heatmap

Highlight Table

Histogram

Cumulative Histogram

Line Chart

Lollipop Chart

Pareto Chart

Pie Chart

Scatter Plot

Stacked Bar Chart

Text Label

Tree Map

Word Cloud

Waterfall Chart

Hands-on: Create and use Static Sets

Hands-on: Create and use Dynamic Sets

Hands-on: Combine Sets into more Sets

Hands-on: Use Sets as filters

Hands-on: Create Sets via Formulas

Hands-on: Control Sets with Parameters

Hands-on: Control Reference Lines with Parameters

Dual Axis Reports

Blended Axis

Individual Axis

Add Reference Lines

Reference Bands

Reference Distributions

Basic Maps

Symbol Map

Use Google Maps

Mapbox Maps as a Background Map

WMS Server Map as a Background Map

Hands-on: Create Barcharts

Hands-on: Create Area Charts

Hands-on: Create Maps

Hands-on: Create Interactive Dashboards

Hands-on: Create Storylines

Hands-on: Understand Types of Joins and how they work

Hands-on: Work with Data Blending in Tableau

Hands-on: Create Table Calculations

Hands-on: Work with Parameters

Hands-on: Create Dual Axis Charts

Hands-on: Create Calculated Fields

Calculated Fields

Basic Approach to Calculate Rank

Advanced Approach to Calculate Ra

Calculating Running Total

Filters Introduction

Quick Filters

Filters on Dimensions

Conditional Filters

Top and Bottom Filters

Filters on Measures

Context Filters

Slicing Filters

Data Source Filters

Extract Filters

Hands-on: Creating Data Extracts in Tableau

Hands-on: Understand Aggregation, Granularity, and Level of Detail

Hands-on: Adding Filters and Quick Filters

Create a Dashboard

Format Dashboard Layout

Create a Device Preview of a Dashboard

Create Filters on the Dashboard

Dashboard Objects

Create a Story

Tableau online.

Overview of Tableau

Publishing Tableau objects and scheduling/subscription.

Hands-on: Create Data Hierarchies

Hands-on: Adding Actions to Dashboards (filters & highlighting)

Hands-on: Assigning Geographical Roles to Data Elements

Hands-on: Advanced-Data Preparation

List the features of Oracle Database 11g

Discuss the basic design, theoretical, and physical aspects of a relational database

Categorize the different types of SQL statements

Describe the data set used by the course

Log on to the database using the SQL Developer environment

Save queries to files and use script files in SQL Developer

Hands-on: Prepare your environment

Hands-on: Work with Oracle database tools

Hands-on: Understand and work with language features

List the capabilities of SQL SELECT statements

Generate a report of data from the output of a basic SELECT statement

Select All Columns

Select Specific Columns

Use Column Heading Defaults

Use Arithmetic Operators

Understand Operator Precedence

Learn the DESCRIBE command to display the table structure

Hands-on: Individual statements in SQL scripts are commonly terminated by a line break (or carriage return) and a forward slash on the next line, instead of a semicolon.

Hands-on: You can create a SELECT statement, terminate it with a line break, include a forward slash to execute the statement, and save it in a script file.

Write queries that contain a WHERE clause to limit the output retrieved

List the comparison operators and logical operators that are used in a WHERE clause

Describe the rules of precedence for comparison and logical operators

Use character string literals in the WHERE clause

Write queries that contain an ORDER BY clause to sort the output of a SELECT statement

Sort output in descending and ascending order

Hands-on: Creating the queries in a compound query must return the same number of columns.

Hands-on: Create corresponding columns in each query that must be of compatible data types.

Hands-on: ORDER BY; it is, however, permissible to place a single ORDER BY clause at the end of the compound query

Describe the differences between single-row and multiple-row functions

Manipulate strings with character functions in the SELECT and WHERE clauses

Manipulate numbers with the ROUND, TRUNC, and MOD functions

Perform arithmetic with date data

Manipulate dates with the DATE functions

Hands-on: Creating the distinction is made between single-row functions, which execute once for each

Hands-on: row in a dataset, and multiple-row functions, which execute once for all the rows in a data- set.

Describe implicit and explicit data type conversion

Use the TO_CHAR, TO_NUMBER, and TO_DATE conversion functions

Nest multiple functions

Apply the NVL, NULLIF, and COALESCE functions to the data

Use conditional IF THEN ELSE logic in a SELECT

Hands-on: we create and discuss the NVL function, which provides a mechanism to convert null values into more arithmetic-friendly data values.

Use the aggregation functions in SELECT statements to produce meaningful reports

Divide the data into groups by using the GROUP BY clause

Exclude groups of date by using the HAVING clause

Hands-on: Group functions operate on aggregated data and return a single result per group.

Hands-on: These groups usually consist of zero or more rows of data.

Write SELECT statements to access data from more than one table

View data that generally does not meet a join condition by using outer joins

Join a table by using a self-join

Describe the types of problems that subqueries can solve

Define sub-queries

List the types of sub-queries

Hands-on: Write a query that uses subqueries in the column projection list.

Hands-on: Write single-row and multiple-row subqueries

Describe the SET operators

Use a SET operator to combine multiple queries into a single query

Control the order of rows returned

Hands-on: Create The queries in the compound query must return the same number of columns.

Hands-on: creating The corresponding columns must be of compatible data type.

Hands-on: creating The set operators have equal precedence and will be applied in the order they are specified.

Describe each DML statement

Insert rows into a table

Change rows in a table by the UPDATE statement

Delete rows from a table with the DELETE statement

Save and discard changes with the COMMIT and ROLLBACK statements

Explain read consistency

Hands-on: Expressions and create expose a vista of data manipulation possibilities through the interaction of arithmetic and character operators with column or literal data, or a combination of the two.

Categorize the main database objects

Review the table structure

List the data types available for columns

Create a simple table

Decipher how constraints can be created at table creation

Describe how schema objects work

Create a simple and complex view

Retrieve data from views

Create, maintain, and use sequences

Create and maintain indexes

Create private and public synonyms

Differentiate system privileges from object privileges

Create Users

Grant System Privileges

Create and Grant Privileges to a Role

Change Your Password

Grant Object Privileges

How to pass on privileges?

Revoke Object Privileges

Hands-on: create users and execute the privileges.

Add, Modify, and Drop a Column

Add, Drop, and Defer a Constraint

How to enable and Disable a Constraint?

Create and Remove Indexes

Create a Function-Based Index

Perform Flashback Operations

Create an External Table by Using ORACLE_LOADER and by Using ORACLE_DATAPUMP

Query External Tables

Hands-on: Create the function-based index and types.

Explain the data dictionary

Use the Dictionary Views

USER_OBJECTS and ALL_OBJECTS Views

Table and Column Information

Query the dictionary views for constraint information

Query the dictionary views for view, sequence, index, and synonym information

Add a comment to a table

Query the dictionary views for comment information

Use Subqueries to Manipulate Data

Retrieve Data Using a Subquery as Source

Insert Using a Subquery as a Target

Usage of the WITH CHECK OPTION Keyword on DML Statements

List the types of Multitable INSERT Statements

Use Multitable INSERT Statements

Merge rows in a table

Track Changes in Data over a period of time

Time Zones

CURRENT_DATE, CURRENT_TIMESTAMP, and LOCALTIMESTAMP

Compare Date and Time in a Session’s Time Zone

DBTIMEZONE and SESSIONTIMEZONE

Difference between DATE and TIMESTAMP

INTERVAL Data Types

Use EXTRACT, TZ_OFFSET, and FROM_TZ

Invoke TO_TIMESTAMP, TO_YMINTERVAL, and TO_DSINTERVAL

Multiple-Column Subqueries

Pairwise and Non-Pairwise Comparison

Scalar Subquery Expressions

Solve problems with Correlated Subqueries

Update and Delete Rows Using Correlated Subqueries

The EXISTS and NOT EXISTS operators

Invoke the WITH clause

The Recursive WITH clause

Use the Regular Expressions Functions and Conditions in SQL

Use Meta Characters with Regular Expressions

Perform a Basic Search using the REGEXP_LIKE function

Find patterns using the REGEXP_INSTR function

Extract Substrings using the REGEXP_SUBSTR function

Replace Patterns Using the REGEXP_REPLACE function

Usage of Sub-Expressions with Regular Expression Support

Implement the REGEXP_COUNT function

Hands-on: Expressions and create regular columns may be aliased using the AS keyword or by leaving a space between the column or expression and the alias. In this way, both wildcard symbols can be used as either specialized or regular characters in different segments of the same character string.