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Answered on 04/01/2020 IT Courses/Digital Publishing Functional Training/Internet & Digital Media/Affiliate Marketing Functional Training/Data Analytics

Vinay Kumar

Digital Marketing Trainer cum Specialist

Learn Digital Marketing Course with Photoshop, and Excel
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Lesson Posted on 11/11/2019 IT Courses/Tableau Functional Training/Data Analytics

Tableau is a Good tool for Visualisation

Cadgild Learning Academy

Cadgild is a institute started with a moto of providing the quality education to te students with necessary...

Tableau is one of the best BI Tool we use to show the Data in a Visualization Mode. Tableau is a very Powerful & Fastly growing data visualization tool used in the field of Data Analytics. It will help the Analyser to Simplify the Raw data into Processed data for easy understanding. For Better... read more

Tableau is one of the best BI Tool we use to show the Data in a Visualization Mode.

Tableau is a very Powerful & Fastly growing data visualization tool used in the field of Data Analytics. It will help the Analyser to Simplify the Raw data into Processed data for easy understanding.

For Better Understanding on tableau Contact Caddgild technologies

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Answered on 09/10/2019 Functional Training/Data Analytics

Business Toys

Hello Munis, Career in Business Intelligence and reporting will be vertical integration for your profile. Moreover you can also oversee profiles into Business Analytics which will be perfect choice for you.
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Answered on 07/08/2019 Functional Training/Data Analytics

How can I learn Scala programming as I am already a Spark Resource want to enhance the skill?

Skill Sigma

Hi.Scala is a scalable programming and pure OOPS language. It has become popular after Spark was built on the same.It is one of the most suited languages for Spark programming and is even better than Python.For learning Scala, you may need to have some pre-requisite knowledge of OOPS (Object-oriented... read more

Hi.
Scala is a scalable programming and pure OOPS language. It has become popular after Spark was built on the same.
It is one of the most suited languages for Spark programming and is even better than Python.
For learning Scala, you may need to have some pre-requisite knowledge of OOPS (Object-oriented programming). 
 

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Lesson Posted on 09/02/2019 Functional Training/Data Analytics

GDPR Data Privacy in 90 seconds

Vijay B

I can teach every topic very easily and make it stronger for the students.

What is GDPR? -The General Data Protection Regulation is a law, that is meant to protect the privacy of an Individual belonging to the European Union. GDPR enhances the powers of regulatory authorities to take actions against businesses that breach the new laws/rights related to individual data... read more
  • What is GDPR?

-The General Data Protection Regulation is a law, that is meant to protect the privacy of an Individual belonging to the European Union. GDPR enhances the powers of regulatory authorities to take actions against businesses that breach the new laws/rights related to individual data privacy.

  • So what’s this got to do with us? It is a European Law...

 - Good question! It also law applies to non-European companies that process personal data of Individuals in the EU. Also, the international transfer of data remains to be governed under EU GDPR laws.  Therefore, we need to comply with GDPR as long as we do business with the European Union.

  • So, what happens if we ignore GDPR?

 -  Ignoring any law bring forth ramifications. In this case, it could be in the form of penalty (among other things). Penalties can go up to 4% of a company’s global revenue or 20 million Euros – whichever is higher!

  • What kind of personal data is protected under GDPR?

 - Any personal identifier that could be used to identify an individual; such as genetic, mental, physical, cultural, economic, social  identifiers are protected under GDPR (Examples: Simple data such as first and last names, email addresses, phone numbers and high-risk data such as medical data, financial statements etc.)

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Answered on 14/02/2019 Functional Training/Data Analytics

Amit Raj

Trainer

Hi Sudhir , You must aware with proper JD and company . You must be good on project architecture knowledge along with your roles n responsibilites . you must know your coding part , challenges and solution , performance tunning . you take complete use case with proper data set while explaning . kindly... read more

Hi Sudhir ,

You must aware with proper JD and company . You must be good on project architecture knowledge along with your roles n responsibilites . you must know your coding part , challenges and solution , performance tunning . you take complete use case with proper data set while explaning . kindly make sure for proper data format . you must be good on Machine learning algorithm so that you need to explain with one example .

 

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Lesson Posted on 16/08/2017 IT Courses/Hadoop/Hadoop Testing IT Courses/Big Data/Big Data Testing Functional Training/Data Analytics +1 IT Courses/Data Science less

Lesson: Hive Queries

Chitra J.

IT professional with 10 years of diversified experience with extensive knowledge and background in Software...

Lesson: Hive Queries This lesson will cover the following topics: Simple selects ? selecting columns Simple selects – selecting rows Creating new columns Hive Functions In SQL, of which HQL is a dialect, querying data is performed by a SELECT statement. A select statement has 6 key component: SELECT... read more

Lesson: Hive Queries

 This lesson will cover the following topics:

  • Simple selects ? selecting columns
  • Simple selects – selecting rows
  • Creating new columns
  • Hive Functions

In SQL, of which HQL is a dialect, querying data is performed by a SELECT statement. A select statement has 6 key component:

  • SELECT colnames
  • FROM tablename
  • GROUP BY colnames
  • WHERE conditions
  • HAVING conditions
  • ORDER by colnames

In practice, very few queries will have all of these clauses in them simplifying many queries. On the other hand, conditions in the WHERE clause can be very complex and if you need to JOIN two or more tables together then more clause (JOIN and ON) are needed.

All of the clause names above have been written in uppercase for clarity. HQL is not case sensitive. Neither do you need to write each clause on a new line, but it is often clearer to do so for all but the simplest of queries.

In this lesson we will start with the very simple and work our way up to the more complex.

Simple selects - selecting columns

[These examples are included in the ’01 – simple queries.sql’ file]

The simplest query is effectively one which returns the contents of the whole table:

SELECT *

FROM geog_all;

It is better practice and generally more efficient to explicitly list the column names that you want returned.

SELECT anonid, fueltypes, acorn_type

FROM geog_all;

Simple selects – selecting rows

In addition to limiting the columns returned by a query, you can also limit the rows returned. The simplest case is to say how many rows are wanted using the Limit clause.

SELECT anonid, fueltypes, acorn_type

FROM geog_all

LIMIT 10;

This is useful if you just want to get a feel for what the data looks like.

Usually you will want to restrict the rows returned based on some criteria. i.e. certain values or ranges within one or more columns.

SELECT anonid, fueltypes, acorn_type

FROM geog_all

WHERE fueltypes = "ElecOnly";

The Expression in the where clause can be more complex and involve more than one column.

SELECT anonid, fueltypes, acorn_type

FROM geog_all

WHERE fueltypes = "ElecOnly" AND acorn_type > 42;

SELECT anonid, fueltypes, acorn_type

FROM geog_all

WHERE fueltypes = "ElecOnly" AND acorn_type > 42 AND nuts1 <> "--";

Notice that the columns used in the conditions of the Where clause don’t have to appear in the Select clause.

Other operators can also be used in the where clause. For complex expressions, brackets can be used to enforce precedence.

SELECT anonid,

fueltypes,

acorn_type,

nuts1,

ldz

FROM geog_all

WHERE fueltypes = "ElecOnly"

AND acorn_type BETWEEN 42 AND 47

AND (nuts1 NOT IN ("UKM", "UKI") OR ldz = "--");

Creating new columns

It is possible to create new columns in the output of the query. These columns can be from combinations from the other columns using operators and/or builtin Hive functions.

SELECT anonid,

eprofileclass,

acorn_type,

(eprofileclass * acorn_type) AS multiply,

(eprofileclass + acorn_type) AS added

FROM edrp_geography_data b;

When you create a new column it is usual to provide an ‘alias’ for the column. This is essentially the name you wish to give to the new column. The alias is given immediately after the expression to which it refers. Optionally you can add the AS keyword for clarity.

If you do not provide an alias for your new columns, Hive will generate a name for you. Although the term alias may seem a bit odd for a new column which has no natural name, alias’ can also be used with any existing column to provide a more meaningful name in the output.

Tables can also be given an alias, this is particularly common in join queries involving multiple tables where there is a need to distinguish between columns with the same name in different tables.

In addition to using operators to create new columns there are also many Hive built?in functions that can be used.

Hive Functions:

[These examples are included in the ’02 ? functions.sql’ file]

Simple functions

Concat can be used to add strings together

SELECT anonid,

acorn_category,

acorn_group,

acorn_type,

concat (acorn_category,

",",

acorn_group,

",",

acorn_type)

AS acorn_code

FROM geog_all;

substr can be used to extract a part of a string

SELECT anon_id,

advancedatetime,

substr (advancedatetime, 1, 2) AS day,

substr (advancedatetime, 3, 3) AS month,

substr (advancedatetime, 6, 2) AS year

FROM elec_c;

examples of length, instr and reverse

SELECT anonid,

acorn_code,

length (acorn_code),

instr (acorn_code, ',') AS a_catpos,

instr (reverse (acorn_code), "," ) AS reverse_a_typepos

FROM geog_all;

Where needed functions can be nested within each other cast and type conversions

SELECT anonid,

substr (acorn_code, 7, 2) AS ac_type_string,

cast (substr (acorn_code, 7, 2) AS INT) AS ac_type_int,

substr (acorn_code, 7, 2) +1 AS ac_type_not_sure

FROM geog_all;

Aggregations

[These examples are included in the ’03 ? aggregations.sql’ file]

Aggregate functions are used perform some kind of mathematical or statistical calculation across a group of rows. The rows in each group are determined by the different values in a specified column or columns. A list of all of the available functions are available in the apache documentation.

SELECT anon_id,

count (eleckwh) AS total_row_count,

sum (eleckwh) AS total_period_usage,

min (eleckwh) AS min_period_usage,

avg (eleckwh) AS avg_period_usage,

max (eleckwh) AS max_period_usage

FROM elec_c

GROUP BY anon_id;

In the above example, thee aggregation were performed over the single column anon_id. It is possible to aggregate over multiple columns by specifying them in both the select and the group by clause. The grouping will take place based on the order of the columns listed in the group by clause.

What is not allowed is specifying a non?aggregated column in the select clause which is not mentioned in the group by clause.

SELECT anon_id,

substr (advancedatetime, 6, 2) AS reading_year,

count (eleckwh) AS total_row_count,

sum (eleckwh) AS total_period_usage,

min (eleckwh) AS min_period_usage,

avg (eleckwh) AS avg_period_usage,

max (eleckwh) AS max_period_usage

FROM elec_c

GROUP BY anon_id, substr (advancedatetime, 6, 2);

Unfortunately, the group by clause will not accept alias’.

SELECT anon_id,

substr (advancedatetime, 6, 2) AS reading_year,

count (eleckwh) AS total_row_count,

sum (eleckwh) AS total_period_usage,

min (eleckwh) AS min_period_usage,

avg (eleckwh) AS avg_period_usage,

max (eleckwh) AS max_period_usage

FROM elec_c

GROUP BY anon_id, substr (advancedatetime, 6, 2)

ORDER BY anon_id, reading_year;

But the Order by clause does.

The Distinct keyword provides a set of unique combination of column values within a table without any kind of aggregation.

SELECT DISTINCT eprofileclass, fueltypes

FROM geog_all;

date functions

[These examples are included in the ’04 ? date functions.sql’ file]

In the elec_c and gas_c tables, the advancedatetime column, although it contains a timestamp type information, it is defined as a string type. For much of the time this can be quite convenient, however there will be times when we really do need to be able to treat the column as a Timestamp.

Perhaps the most obvious example is when you need to sort rows based on the advancedatetime column.

Hive provides a variety of date related functions to allow you to convert strings into Timestamp and to additionally extract parts of the Timestamp.

unix_timestamp returns the current data and time – as an integer!

from_unixtime takes an integer and converts in into a recognisable Timestamp string

SELECT unix_timestamp () AS currenttime

FROM sample_07

LIMIT 1;

SELECT from_unixtime (unix_timestamp ()) AS currenttime

FROM sample_07

LIMIT 1;

There are various date part functions which will extract the relevant parts from a Timestamp string

SELECT anon_id,

from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy'))

AS proper_date,

year (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy')))

AS full_year,

month (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy')))

AS full_month,

day (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy')))

AS full_day,

last_day (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy')))

AS last_day_of_month,

date_add ( (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy'))),

10)

AS added_days

FROM elec_days_c

ORDER BY proper_date;

Hive Sample - Real Time Window:

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Asked on 04/07/2017 Functional Training/Data Analytics

I Need SQL, Python and Visualization tutors for data analytics.

Answer

Lesson Posted on 18/11/2016 IT Courses/Data Science IT Courses/Advanced Statistics IT Courses/Data Analysis +3 Financial Planning/Business Analytics Training Functional Training/Data Analytics Functional Training/Business Analysis Training less

Basics of K means classification- An unsupervised learning algorithm

Ashish R.

SAS certified analytics professionals, more than 11 years of industrial and 12 years of teaching experience....

K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set with n objects through a certain number of K clusters. K-means stands for K number of clusters to form in your training... read more

K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set with n objects through a certain number of K clusters. K-means stands for K number of clusters to form in your training sample. The idea behind doing the clustering is that elements that are very much similar with respect to each other with respect to the considered parameters/attributes should go to the same cluster. As a result we could expect that variability within cluster should be very low (as much minimum as possible) and variability a across clusters should be maximum. This is to remember when we say that the elements within a cluster should be very much similar then we articulate the similarity with respect to the considered variables that are used in the execution of the classification algorithm. The elements within a cluster/segment might differ with respect to some other parameters that are not considered in the execution of the algorithm. For example, if we have a 1000 stores from retail chain to cluster them into multiple groups based on the parameters like sales volume, size, # of SKUs available, # of labors deployed then within each cluster the stores they might vary with respect of some parameters like store managers experience, made os payment accepts inside the store etc.

Now it is to know, where it is used or what kind of problems are solved using this technique:

Telecom domain: Segment the customers based on network usage data across various services (Youtube, Google, Social media, Netflix, VPN work etc.). the idea is to cluster the customers so that the right segment of the customers could be trageted with right stratey for product/services upsell and cross sell

Reatil Banking: Segment the credit card applicants based on age, income, occupation, gender and other demographic profile to determine the credit limit.

Insurance domain: Segment the customer base based on age, lifestyle, income, demographic features to determine the insurance premium.

 

 

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Lesson Posted on 11/10/2016 IT Courses/Data Science Functional Training/Data Analytics

Data Scientist Vs Data Analyst

Ramesh Ratnala

I am a seasoned Analytics/Data Science professional with 8 years of professional experience. I have industry...

Data Scientist – Rock Star of IT A Data Scientist is a professional who understands data from a business point of view. He is in charge of making predictions to help businesses take accurate decisions. Data scientists come with a solid foundation of computer applications, modeling, statistics... read more

Data Scientist – Rock Star of IT

A Data Scientist is a professional who understands data from a business point of view. He is in charge of making predictions to help businesses take accurate decisions. Data scientists come with a solid foundation of computer applications, modeling, statistics and math. What sets them apart is their brilliance in business coupled with great communication skills, to deal with both business and IT leaders. They are efficient in picking the right problems, which will add value to the organization after resolving it.

Harvard Business Review has named ‘Data scientist’ as the “sexiest job of the 21st century. Up-skill with Data Science now to take advantage of the career opportunities that come your way.

 

A Data Scientist can also be divided into 4 different roles based on their skill sets.

  • Data Researcher
  • Data Developers
  • Data Creatives
  • Data Businesspeople

Data Analysts – No Cool Tag Yet!

Data Analysts also plays a major role in Data Science. They perform a variety of tasks related to collecting, organizing data and obtaining statistical information out of them.  They are also responsible to present the data in the form of charts, graphs and tables and use the same to build relational databases for organizations.

A Data Analyst can also be divided into 4 different roles based on their skill sets.

  • Data Architects
  • Database Administrators
  • Analytics Engineer
  • Operations

 

 

  

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