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Data Science Master Program Certification Training Course
Introduction to Data Science
What is Data Science?
What is Machine Learning?
What is Deep Learning?
What is AI?
Data Analytics & its types
Introduction to Python
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
Python Basics
Python Basic Data types
Lists
Slicing
IF statements
Loops
Dictionaries
Tuples
Functions
Array
Selection by position & Labels
Hands-on: Practice and Quickly learn Python necessary skills by solving simple questions and problems.
Hands-on: How Python uses indentation to structure a program, and how to avoid some common indentation errors.
Hands-on: You executed to make simple numerical lists, as well as a few operations you can perform on numerical lists, tuples, dictionaries, and set
Python Packages
Pandas
Numpy
Sci-kit Learn
Mat-plot library
Hands-on: Installing Jupyter notebook for windows, Linux and
Hands-on: Installing NumPy, pandas and Matplotlib
Importing Data
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
Manipulating Data
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.
Statistics Basics
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
Error Metrics
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
Supervised Learning
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
Unsupervised Learning
K-Means
K-Means ++
Hierarchical Clustering
SVM
Support Vectors
Hyperplanes
2-D Case
Linear Hyperplane
SVM Kernel
Linear
Radial
Polynomial
Other Machine Learning algorithms
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.
AI Introduction
Perceptron
Multi-Layer Perceptron
Markov Decision Process
Logical Agent & First Order Logic
AL Applications
Deep Learning Algorithms
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
Introduction to NLP
Text Pre-processing
Noise Removal
Lexicon Normalization
Lemmatization
Stemming
Object Standardization
Text to Features (Feature Engineering)
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
Tasks of NLP
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.
Tableau Course Material
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
Learn Tableau Basic Reports
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
Learn Tableau Charts
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
Learn Tableau Advanced Reports
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
Learn Tableau Calculations & Filters
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
Learn Tableau Dashboards
Create a Dashboard
Format Dashboard Layout
Create a Device Preview of a Dashboard
Create Filters on the Dashboard
Dashboard Objects
Create a Story
Server
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
Introduction to Oracle Database
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
Retrieve Data using the SQL SELECT Statement
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.
Learn to Restrict and Sort Data
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
Usage of Single-Row Functions to Customize Output
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.
Invoke Conversion Functions and Conditional Expressions
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.
Aggregate Data Using the Group Functions
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.
Display Data from Multiple Tables Using Joins
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
Use Subqueries to Solve Queries
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
The SET Operators
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.
Data Manipulation Statements
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.
Use of DDL Statements to Create and Manage Tables
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
Other Schema Objects
Create a simple and complex view
Retrieve data from views
Create, maintain, and use sequences
Create and maintain indexes
Create private and public synonyms
Control User Access
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.
Management of Schema Objects
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.
Manage Objects with Data Dictionary Views
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
Manipulate Large Data Sets
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
Data Management in Different Time Zones
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
Retrieve Data Using Sub-queries
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
Regular Expression Support
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.
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