UI Design & Front End Development

Understanding & Applying  Design Fundamentals

  • What is UI Design?
  • UI vs UX Design
  • UI Design vs Front End Developer
  • Designing and Prototyping UI’s
  • Design Fundamentals
  • Color & Contrast
  • White Space
  • Visual Hierarchy
  • Complexity vs Simplicity
  • Consistency
  • Scale

Figma

  • Introduction
  • Learning Figma Interface
  • Setting up a new project
  • Adding and Editing Content
  • Completing the Design
  • Exporting, Sharing, and Prototyping
  • Working with clients

Front End Development

  • What is Front End Development?
  • Code Editors

HTML

  • Introduction
  • HTML Elements
  • HTML Attributes
  • HTML Headings
  • HTML Paragraphs
  • HTML Styles
  • HTML Formatting
  • HTML Quotation
  • HTML Comments
  • HTML Colors
  • HTML Links
  • HTML Images
  • HTML Tables
  • HTML Lists
  • HTML Block & Inline
  • HTML Responsive
  • HTML Forms

CSS

  • Introduction
  • CSS Rulesets
  • CSS Selectors
  • CSS Declarations
  • CSS Values
  • CSS Units
  • CSS Relative Units
  • CSS Box Models
  • CSS Functions
  • CSS Animations
  • CSS Media Queries
  • CSS Libraries & Framework (Bootstrap, Materialize, Semantic UI, Tailwind
  • CSS, Animate CSS)

Javascript

  • What is Javascript?
  • JavaScript Stats
  • What can JavaScript do?
  • JavaScript Framework
  • Top Websites Built Using JavaScript
  • Benefits of JavaScript
  • JavaScript Fundamentals
  • JavaScript Variables
  • JavaScript Constants
  • JavaScript Data Types
  • JavaScript Objects
  • JavaScript Functions
  • JavaScript Conditional Statements
  • JavaScript Loops
  • JavaScript Switch Case
  • Animation (GSAP, FullPageJS, ThreeJS)

Final Project

  • Any one Responsive Website (Corporate, Hotel, Restaurant, School, etc.)
  • Portfolio Website

Data Analysis & Visualization with Power BI

Chapter 1: Power BI Desktop

  • Installing Power BI Desktop
  • Exploring the Power BI Desktop Interface
  • Examining Data Sources
  • Discussing Data Refresh
  • Understanding Data Types

Chapter 2: Working With CSV, TXT and Excel Worksheets

  • Importing, Transforming, and Loading Data from offline and online file
  • Appending Operations
  • Working with Folders and Files
  • Using Pivot Tables

Chapter 3: Connecting to Databases

  • Importing data from database (Access, Mysql, Oracle, Sql Server)
  • Working with Relational Data
  • Understanding Relationships
  • Relationships

Chapter 4: Merging, Grouping, Summarizing, And Calculating Data

  • Understanding the Issues with Merges
  • Performing Merges
  • Grouping Options
  • Summarizing
  • Calculating

Chapter 5: Data Analysis Expression DAX

  • Basic functions that we use in excel
  • SUM ()
  • SUMX ()
  • AVERAGE ()
  • AVERAGEX()
  • MIN ()
  • MAX()
  • CALCULATE ()
  • Filter ()
  • ALL
  • MINX () / MAXX ()

Chapter 6: Visualization & Reporting

  • Creating Reports
  • Bar Chart
  • Column Chart
  • Line Chart
  • Area Chart
  • Ribbon Chart
  • Pie Chart
  • Combo Chart
  • Bubble Chart
  • Scatter Chart
  • Donut Chart
  • Gauge Chart
  • Funnel Chart
  • Waterfall Chart
  • Single Number Chart
  • Multi row Chart
  • KPI Chart
  • Table Chart
  • Matrix Chart
  • Map
  • Filled Map
  • Tree Map
  • Slicer
  • Image

Chapter 7: Power BI Online

  • Introduction to Power BI Online
  • Importing Data
  • Creating Reports
  • Configuring Dashboards
  • Sharing Reports and Dashboards
  • Exploring Power BI Mobile

Power BI Certification

Analyzing Data with Microsoft Power BI (Exam DA-100)

Prepare the Data (20-25%)

Get data from different data sources

  • identify and connect to a data source
  • change data source settings
  • select a shared dataset or create a local dataset
  • select a storage mode
  • choose an appropriate query type
  • identify query performance issues
  • use Microsoft Dataverse
  • use parameters
  • use or create a PBIDS file
  • use or create a data flow
  • connect to a dataset using the XMLA endpoint

Profile the data

  • identify data anomalies
  • examine data structures
  • interrogate column properties
  • interrogate data statistics

Clean, transform, and load the data

  • resolve inconsistencies, unexpected or null values, and data quality issues
  • apply user-friendly value replacements
  • identify and create appropriate keys for joins
  • evaluate and transform column data types
  • apply data shape transformations to table structures
  • combine queries
  • apply user-friendly naming conventions to columns and queries
  • leverage Advanced Editor to modify Power Query M code
  • configure data loading
  • resolve data import errors

Model the Data (25-30%)

Design a data model

  • define the tables
  • configure table and column properties
  • define quick measures
  • flatten out a parent-child hierarchy
  • define role-playing dimensions
  • define a relationship’s cardinality and cross-filter direction
  • design the data model to meet performance requirements
  • resolve many-to-many relationships
  • create a common date table
  • define the appropriate level of data granularity

Develop a data model

  • apply cross-filter direction and security filtering
  • create calculated tables
  • create hierarchies
  • create calculated columns
  • implement row-level security roles
  • set up the Q&A feature

Create measures by using DAX

  • use DAX to build complex measures
  • use CALCULATE to manipulate filters
  • implement Time Intelligence using DAX
  • replace numeric columns with measures
  • use basic statistical functions to enhance data
  • create semi-additive measures

Optimize model performance

  • remove unnecessary rows and columns
  • identify poorly performing measures, relationships, and visuals
  • improve cardinality levels by changing data types
  • improve cardinality levels through summarization
  • create and manage aggregations

Visualize the Data (20-25%)

Create reports

  • add visualization items to reports
  • choose an appropriate visualization type
  • format and configure visualizations
  • import a custom visual
  • configure conditional formatting
  • apply slicing and filtering
  • add an R or Python visual
  • configure the report page
  • design and configure for accessibility
  • configure automatic page refresh
  • create a paginated report

Create dashboards

  • set mobile view
  • manage tiles on a dashboard
  • configure data alerts
  • use the Q&A feature
  • add a dashboard theme
  • pin a live report page to a dashboard

Enrich reports for usability

  • configure bookmarks
  • create custom tooltips
  • edit and configure interactions between visuals
  • configure navigation for a report
  • apply sorting
  • configure Sync Slicers
  • use the selection pane
  • use drillthrough and cross filter
  • drilldown into data using interactive visuals
  • export report data
  • design reports for mobile devices

Analyze the Data (10-15%)

Enhance reports to expose insights

  • apply conditional formatting
  • apply slicers and filters
  • perform top N analysis
  • explore statistical summary
  • use the Q&A visual
  • add a Quick Insights result to a report
  • create reference lines by using Analytics pane
  • use the Play Axis feature of a visualization
  • personalize visuals

Perform advanced analysis

  • identify outliers
  • conduct Time Series analysis
  • use groupings and binnings
  • use the Key Influencers to explore dimensional variances
  • use the decomposition tree visual to break down a measure
  • apply AI Insights

Deploy and Maintain Deliverables (10-15%)

Manage datasets

  • configure a dataset scheduled refresh
  • configure row-level security group membership
  • provide access to datasets
  • configure incremental refresh settings
  • promote or certify Power BI datasets
  • identify downstream dataset dependencies
  • configure large dataset format

Create and manage workspaces

  • create and configure a workspace
  • recommend a development lifecycle strategy
  • assign workspace roles
  • configure and update a workspace app
  • publish, import, or update assets in a workspace
  • apply sensitivity labels to workspace content
  • use deployment pipelines
  • configure subscriptions
  • promote or certify Power BI content

Remote Computer Training in Nepal



Most Preferred Remote Training Courses



Apache Superset

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

Filters

Bar Distribution Chart

Pie Chart

Donut Chart

Line Chart

Time Series Stacked Chart

Partition Diagram

Tree Map

6. Create your own dashboards

7. Handle Admin Features

Users

Role

Permission

8. Share Dashboard

SQL FUNDAMENTAL

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.

Target Audience

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 are:
• 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
• REGEXP_LIKE()
• 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
• SYSDATE
• USER & UID
• USING THE DUAL TABLE
• SESSIONTIMEZONE FUNCTION

JOINING TABLES

• ABOUT JOINS
• INNER JOIN
• REFLEXIVE JOIN
• NON-KEY JOIN
• OUTER JOIN

USING SUB-QUERIES

• 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
• DEFAULT
• ALTER TABLE STATEMENT
• DROP TABLE STATEMENT
• SUPPORTIVE STATEMENTS
• DESCRIBE
• RENAME

USE DATA MANIPULATION LANGUAGE

• ABOUT THE INSERT STATEMENT
• ABOUT THE DELETE STATEMENT
• ABOUT THE UPDATE STATEMENT
• ABOUT TRANSACTIONS
• ROLLBACK
• COMMIT
• SAVEPOINT
• 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
• USER_UPDATABLE_COLUMNS
• 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 & DROPPING CONSTRAINTS
• ENABLING & DISABLING CONSTRAINTS
• DEFERRED ENFORCEMENT
• SET CONSTRAINTS
• HANDLING CONSTRAINT EXCEPTIONS
• CONSTRAINTS WITH VIEWS
• DATA DICTIONARY STORAGE


Epi Info

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 .

Overview.

Navigating Epi Info.

Analysis Components

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 

2. Variables 

Overview.

Defining a Variable .

Assign a Variable 

Undefine a Variable 

Recode a Variable .

Writing Results to a New Table 

3. Basics Statistics.

Overview.

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 

Overview.

Assign a Variable Based on  Variable.

Assign a Variable Based on  or More Variables .

5. Hypotheses tests

t-test

ANOVA

Non-parametric 

6. Saved Programs .

Overview.

What are Saved Programs? .

Running a Saved Program.

Opening a Saved Program.

GNU PSPP

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. Introduction

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

3.6. Exporting

4. Combining Data Files

4.1. ADD FILES

4.2. MATCH FILES

4.3. UPDATE

5. Manipulating variables

5.1. ADD VARIABLES

5.2. DELETE VARIABLES

5.3. Add Cases

5.3. DELETE CASE

5.4. ADD VALUE LABELS

5.5. DISPLAY

5.6. FORMATS

5.7. LEAVE

5.8. MISSING VALUES

5.9. SORT VARIABLES

6. Data transformations

6.1. SORT CASES

6.2. COMPUTE

6.3. RECODE

6.4. COUNT

6.5. FLIP

6.7. IF

6.8. AGGREGATE

6.9. AUTORECODE

7. Selecting data for analysis

7.1. FILTER

7.2. N OF CASES

7.3. SAMPLE

7.4. SELECT IF

7.5. SPLIT FILE

8. Operators

8.1. Arithmetic Operators

8.2. Logical Operators

8.3. Relational Operators

9. Functions

9.1. Mathematical Functions

9.2. String Functions

9.3. Time & Date Functions

9.4. Statistical Functions

10. Descriptive Statistics

10.1. DESCRIPTIVES

  • MEAN
  • MEDIAN
  • MODE

10.2. FREQUENCIES

10.3. GRAPH

  • Scatterplot
  • Histogram
  • 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

12.1. CORRELATIONS

12.2. REGRESSION

12.3. Chisquare Test

12.4. CROSSTABS

12.5. T-TEST

  • One Sample Mode
  • Independent Samples Mode
  • Paired Samples Mode

12.6. ANOVA

12.7. RELIABILITY