Most Preferred Remote Training Courses
SPSS Statistics is a software package used for interactive, or batched, statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009.
Power BI is a business analytics service by Microsoft. It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.
Basic computer course is also know as MS Office Package. This is initial computer course for beginner. In this course student will learn Microsoft Word, Excel, Powerpoint, Publisher.
Python is a powerful multi-purpose programming language created by Guido van Rossum. It has simple easy-to-use syntax, making it the perfect language for someone trying to learn computer programming for the first time.
A financial accounts package keeps track of an organisation’s finances by recording and processing transactions within a business.
VBA is devleoped by Microsoft to enable programming in Microsoft application to the users. With the help of VBA code we can do Automation in all the Microsoft application like Excel, Word, Access…
Microsoft Excel is a spreadsheet program that is used to record and analyse numerical data. Think of a spreadsheet as a collection of columns and rows that form a table
R is statistical software which is used for data analysis. It includes a huge number of statistical procedures such as t-test, chi-square tests,standard linear models etc
Java is an object-oriented programming language developed by Sun Microsystems, and it was released in 1995. James Gosling initially developed Java.
What is Apache Superset?
Superset is a modern BI app with a simple interface, feature-rich when it comes to views, that allows the user to create and share dashboards.
This app is simple and doesn’t require programming, and allows the user to explore, filter and organise data. The best part is… it’s Open Source!
What will you learn in this course:
In This Course, Participants will learn about:
Basics of Oracle VM Virtual box
How to Install Oracle VM Virtual box in Windows
How to Download & Install Ubuntu Studio with Oracle VM Virtual box in Windows
How to Install Apache Superset in Ubuntu Studio Virtual Machine
How to create basic charts i.e Filters, Pie Chart, Donut Chart, Line Chart, Bar Distribution Chart, Partition Diagram, Tree Map, Big Numbers with Trend line
Table view, Pivot Table, Time Series Stacked etc.
Finally creating Dashboard with the charts
Apache Superset Course Outline
1. Install Apache Superset
Overview of Apache Superset Features and Architecture
Install Apache Superset in Windows
Intall Apache Superset in Ubuntu.
2. Database Installation
Install Mysql Database in Windows and Ubuntu
Install Postgres Database in Windows and Ubuntu
3. Configure Prouction Database
Set Database for CSV upload
Connecting Your Own Data Sources
Setup Production database for data analysis and data visualization
4. SQL for Data Analysis (SQL in Postgres)
SQL 1. Querying Data
Select – shows you how to query data from a single table.
Order By – guides you on how to sort the result set returned from a query.
Select Distinct – provides you a clause that removes duplicate rows in the result set.
SQL 2. Filtering Data
Where – filters rows based on a specified condition.
Limit – gets a subset of rows generated by a query.
Fetch– limits the number of rows returned by a query.
In – selects data that matches any value in a list of values.
Between – selects data that is a range of values.
Like – filters data based on pattern matching.
Is Null – checks if a value is null or not.
Table & column aliases – describes how to use table and column aliases in the query.
SQL 3. Joining Multiple Tables
Joins – shows you a brief overview of joins in PostgreSQL.
Inner Join – selects rows from one table that has the corresponding rows in other tables.
Left Join – selects rows from one table that may or may not have the corresponding rows in other tables.
Self-join – joins a table to itself by comparing a table to itself.
Full Outer Join – uses the full join to find a row in a table that does not have a matching row in another table.
Cross Join – produces a Cartesian product of the rows in two or more tables.
Natural Join – joins two or more tables using implicit join condition based on the common column names in the joined tables.
SQL 4. Grouping Data
Group By – divides rows into groups and applies an aggregate function on each.
Having – applies the condition for groups.
SQL 5. Performing Set Operations
Union – combines result sets of multiple queries into a single result set.
Intersect – combines the result sets of two or more queries and returns a single result set that has the rows appear in both result sets.
Except – returns the rows in the first query that does not appear in the output of the second query
5. Exploring and Visualizing Data
Bar Distribution Chart
Time Series Stacked Chart
6. Create your own dashboards
7. Handle Admin Features
8. Share Dashboard
Objectives Of This Course
This course is a common starting point in the Oracle database curriculum for administrators, developers and business users. The objective of this course is to provide an introduction to the SQL database language within the context of an Oracle database, based upon the latest features available. The primary objective of this course is to consider advanced subjects and techniques pertaining to the SQL database language, based upon the latest features available with the Oracle release. Even professionals experienced in other implementations of the industry-standard SQL language will benefit from the advanced and Oracle-specific features of SQL discussed in this course.
The target audience
for this course is all Oracle professionals, both business and systems
professionals. Among the specific groups for whom this course will be helpful
• Application designers and developers
• Database administrators
• Business users and non-technical senior end users
Course outline for SQL Fundamental
RELATIONAL DATABASES & SQL
• ABOUT RELATIONAL DATABASES
• ELEMENTS OF SQL
CHOOSING A SQL & PL/SQL INTERFACE
• ABOUT DATABASE CONNECTIONS
• ABOUT BIND VARIABLES
• USING SQL DEVELOPER
• USING SQL*PLUS
• USING APPLICATION EXPRESS
BUILDING A SELECT STATEMENT
• ABOUT THE SELECT STATEMENT
• USING ALIAS NAMES
RESTRICTING DATA WITH THE WHERE CLAUSE
• ABOUT LOGICAL OPERATORS
• EQUALITY OPERATOR
• BOOLEAN OPERATORS
• NULL & BETWEEN OPERATORS
• IS [NOT] NULL Operator
• [NOT] BETWEEN Operator
• FINDING TEXT STRINGS
• [NOT] LIKE Operator
• IN OPERATOR
SORTING DATA WITH THE ORDER BY CLAUSE
• ABOUT THE ORDER BY CLAUSE
• MULTIPLE COLUMN SORTS
• SPECIFYING THE SORT SEQUENCE
• ABOUT NULL VALUES WITHIN SORTS
• USING COLUMN ALIASES
PSEUDO COLUMNS & FUNCTIONS
• USING ROWID
• USING ROWNUM
• USING THE FUNCTIONS
• USER & UID
• USING THE DUAL TABLE
• SESSIONTIMEZONE FUNCTION
• ABOUT JOINS
• INNER JOIN
• REFLEXIVE JOIN
• NON-KEY JOIN
• OUTER JOIN
• ABOUT SUB-QUERIES
• STANDARD SUB-QUERIES
• CORRELATED SUB-QUERIES
AGGREGATING DATA WITHIN GROUPS
• ABOUT SUMMARY GROUPS
• FINDING GROUPS WITHIN THE BASE TABLES
• SELECTING DATA FROM THE BASE TABLES
• SELECTING GROUPS FROM THE RESULT TABLE
USE DATA DEFINITION LANGUAGE
• CREATE TABLE STATEMENT
• NOT NULL
• ALTER TABLE STATEMENT
• DROP TABLE STATEMENT
• SUPPORTIVE STATEMENTS
USE DATA MANIPULATION LANGUAGE
• ABOUT THE INSERT STATEMENT
• ABOUT THE DELETE STATEMENT
• ABOUT THE UPDATE STATEMENT
• ABOUT TRANSACTIONS
• SET TRANSACTION
• TRUNCATE TABLE
• COMPLEX TABLE REFERENCES
USING THE CASE EXPRESSION
SQL FUNCTIONS: CHARACTER
• STRING FORMATTING FUNCTIONS
• ASCII CODES FUNCTIONS
• PAD & TRIM FUNCTIONS
• STRING MANIPULATION FUNCTIONS
• STRING COMPARISON FUNCTIONS
• PHONETIC SEARCH FUNCTIONS
SQL FUNCTIONS: NUMERIC
• ABOUT THE NUMERIC FUNCTIONS
• NULL VALUE FUNCTIONS
SQL FUNCTIONS: DATE
• DATE FORMAT FUNCTIONS
• DATE ARITHMETIC FUNCTIONS
DATABASE OBJECTS: RELATIONAL VIEWS
• ABOUT DATABASE OBJECTS
• ABOUT RELATIONAL VIEWS
• UPDATING VIEW DATA
• MAINTAINING VIEW DEFINITIONS
• ALTER VIEW
• DROP VIEW
DATABASE OBJECTS: DATA DICTIONARY STORAGE
• ABOUT THE DATA DICTIONARY
• OBJECT-SPECIFIC DICTIONARY VIEWS
• UNDERSTANDING THE DATA DICTIONARY STRUCTURE
DATABASE OBJECTS: INDEXES
• ABOUT INDEXES
• USE B-TREE INDEXES
DATABASE OBJECTS: OTHER OBJECTS
• MORE ABOUT CREATING TABLES
• ABOUT SEQUENCES
• ALTER SEQUENCE & DROP SEQUENCE
• ALTER SEQUENCE
• DROP SEQUENCE
• ABOUT SYNONYMS
• DROP SYNONYM
• CREATE SCHEMA AUTHORIZATION
DATABASE OBJECTS: SECURITY
• USER PASSWORDS
• OBJECT SECURITY
DATA INTEGRITY USING CONSTRAINTS
• ABOUT CONSTRAINTS
• NOT NULL CONSTRAINT
• CHECK CONSTRAINT
• UNIQUE CONSTRAINT
• PRIMARY KEY CONSTRAINT
• REFERENCES CONSTRAINT
• DEFINING CONSTRAINTS ON EXISTING TABLES
MANAGING CONSTRAINT DEFINITIONS
• RENAMING &
• ENABLING & DISABLING CONSTRAINTS
• DEFERRED ENFORCEMENT
• SET CONSTRAINTS
• HANDLING CONSTRAINT EXCEPTIONS
• CONSTRAINTS WITH VIEWS
• DATA DICTIONARY STORAGE
About Epi Info
Epi Info is statistical software for epidemiology developed by Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia (US).
Epi Info has been in existence for over 20 years and is currently available for Microsoft Windows, Android and iOS, along with a web and cloud version. The program allows for electronic survey creation, data entry, and analysis. Within the analysis module, analytic routines include t-tests, ANOVA, nonparametric statistics, cross tabulations and stratification with estimates of odds ratios, risk ratios, and risk differences, logistic regression (conditional and unconditional), survival analysis (Kaplan Meier and Cox proportional hazard), and analysis of complex survey data. The software is an open-source project with limited support.
An analysis conducted in 2003 documented over 1,000,000 downloads of Epi Info from 180 countries.
Course outline for Epi Info
1. Introduction to Epi Info .
Navigating Epi Info.
Opening a Dataset .
Write Your Data Table Permanently to the Database .
Creating a Backup of Your Database
Reading (Importing) a Table .
Viewing Data in a List
Defining a Variable .
Assign a Variable
Undefine a Variable
Recode a Variable .
Writing Results to a New Table
3. Basics Statistics.
Calculating Derived Variables
Running a Frequency
Creating a Basic Table
Reading an Output Table.
Creating a Graph .
Creating Labels for Outputs
Using the Program Editor
4. If…Then… Statements
Assign a Variable Based on Variable.
Assign a Variable Based on or More Variables .
5. Hypotheses tests
6. Saved Programs .
What are Saved Programs? .
Running a Saved Program.
Opening a Saved Program.
About GNU PSPP
GNU PSPP is a program for statistical analysis of sampled data. It is a free as in freedom replacement for the proprietary program SPSS, and appears very similar to it with a few exceptions. Benefit of PSPP is your copy of PSPP will not “expire”. Neither are there any artificial limits on the number of cases or variables which you can use. PSPP is a stable and reliable application. It can perform descriptive statistics, T-tests, anova, linear and logistic regression, measures of association, cluster analysis, reliability and factor analysis, non-parametric tests and more. Its backend is designed to perform its analyses as fast as possible, regardless of the size of the input data. You can use PSPP with its graphical interface or the more traditional syntax commands.
A brief list of some of the PSPP’s features follows below.
- No license fees.
- No expiration period.
- Support for over 1 billion cases.
- Support for over 1 billion variables.
- Syntax and data files which are compatible with those of SPSS.
- A choice of terminal or graphical user interface.
- Easy data import from spreadsheets, text files and database sources.
- Fast statistical procedures, even on very large data sets.
- A fully indexed user manual.
- PSPP is particularly aimed at statisticians, social scientists and students requiring fast convenient analysis of sampled data.
Course Outline for GNU PSPP
1.1. Introduction to PSPP
1.2. PSPP history
2. Creating Data Files
2.1. Defining Variables
2.2. Datatype: String, Number, Date
2.3. Data entry
2.4. Saving your work
3. Starting with pspp
3.1. Getting your data in
3.2. Importing Data from a CSV File
3.3. Importing Data from a Excel File
3.4. Importing Data from a Text File
3.5. Reading data from other sources
4. Combining Data Files
4.1. ADD FILES
4.2. MATCH FILES
5. Manipulating variables
5.1. ADD VARIABLES
5.2. DELETE VARIABLES
5.3. Add Cases
5.3. DELETE CASE
5.4. ADD VALUE LABELS
5.8. MISSING VALUES
5.9. SORT VARIABLES
6. Data transformations
6.1. SORT CASES
7. Selecting data for analysis
7.2. N OF CASES
7.4. SELECT IF
7.5. SPLIT FILE
8.1. Arithmetic Operators
8.2. Logical Operators
8.3. Relational Operators
9.1. Mathematical Functions
9.2. String Functions
9.3. Time & Date Functions
9.4. Statistical Functions
10. Descriptive Statistics
- Bar Chart
11. Data Screening and Transformation
11.1. Identifying incorrect data
11.2. Dealing with suspicious data
11.3. Inverting negatively coded variables
11.4. Testing data consistency
11.5. Testing for normality
12. Statistically Test
12.3. Chisquare Test
- One Sample Mode
- Independent Samples Mode
- Paired Samples Mode