Data Analysis

Data Analysis

Data analysis is the process of systematically examining data with the purpose of spotlighting useful information. Data analysis is the foundation of scientific research. Conducting a complete analysis of the data you have collected
Daron  Moore

Daron Moore

1626925997

Power BI vs Tableau | Top BI Tools 2021 | Tableau vs Power BI

๐๐ฎ๐ฌ๐ข๐ง๐ž๐ฌ๐ฌ ๐ˆ๐ง๐ญ๐ž๐ฅ๐ฅ๐ข๐ ๐ž๐ง๐œ๐ž ๐ญ๐จ๐จ๐ฅ๐ฌ are a must-have for businesses regardless of the size or industry they operate. Picking the right BI tools for your company makes all the difference in deriving meaningful insights and making better decisions.

But the question is ๐’‰๐’๐’˜ ๐’•๐’ ๐’„๐’‰๐’๐’๐’”๐’† ๐’•๐’‰๐’† ๐’“๐’Š๐’ˆ๐’‰๐’• ๐‘ฉ๐‘ฐ ๐’•๐’๐’๐’?

I think comparing the industry leaders is a good start.
In this video, we compared two of the top BI tools in the market to help you guide through your selection process.

Whether youโ€™re just starting or in the middle of your search, you may have already heard of ๐Œ๐ข๐œ๐ซ๐จ๐ฌ๐จ๐Ÿ๐ญ ๐๐จ๐ฐ๐ž๐ซ ๐๐ˆ and ๐“๐š๐›๐ฅ๐ž๐š๐ฎ, as both are pretty popular BI solutions. However, you probably want to know how they stack up against each other.

So, we conducted a side-by-side comparison between the two based on some crucial factors.

Where she explains:

  • 00:00 = Introduction
  • 01:14 = Agenda
  • 01:47 = What is Power BI?
  • 02:35 = Features of Power BI
  • 03:30 = Top Companies using Power BI
  • 03:51 = What is Tableau?
  • 04:41 = Features of Tableau
  • 05:24 = Top Companies using Tableau
  • 05:46 = Tableau vs Power BI
  • 20:14 = What Should You Choose Between Tableau and Power BI?
  • 21:52 = Learning Path for Microsoft Data Analyst Associate [DA-100]
  • 23:03 = Microsoft Data Analyst Associate [DA-100]
  • 23:48 = Free Class on Microsoft Data Analyst Certification
  • 24:16 = Registration Link for Free Class

#tableau #power-bi #data-analysis

Power BI vs Tableau | Top BI Tools 2021 | Tableau vs Power BI
Anil  Sakhiya

Anil Sakhiya

1626770152

How to Start a Career in Data Science and Analytics? | Data Science Career Roadmap

Great Learning brings you this live session on โ€œHow to start a career in Data Science and Analytics?โ€. In this session, you will be learning about the best practices and methodologies you should follow when beginning your career in todayโ€™s modern world of Data Science and Data Analytics. To start your career in this domain, it is key that you understand the current trends and make the best use of these while starting out. This session is aimed at anyone willing to understand how you can get up to speed and work with these domains. Since these two domains are vast in scope, it is important that you understand how you can work on learning them in a structured manner and later land a job in these fields. There are multiple ways you can start off with both Data Science and Data Analytics but there is a good chance that you might get confused when you begin learning the convoluted concepts. The instructor breaks the concepts down into bite-sized pieces along with giving you a sense of direction that you should consider when working on shaping your career in these fantastic domains.

#data-science #data-analysis #job

How to Start a Career in Data Science and Analytics? | Data Science Career Roadmap
Sarai  Thompson

Sarai Thompson

1626492900

Get Started with Anomaly Detection Algorithms in 5 Minutes

Anomaly detection has quickly moved out of computer science theory into practical everyday use by data scientists. Now, itโ€™s an essential part of data cleaning and KPI reviews for many businesses across the world. Overall, it greatly increases the accuracy of predictive models and can help businesses identify and respond to anomalies quickly.

To help you get started with this dense subject today, weโ€™ll explore a 5-minute crash course on what anomaly detection is, why itโ€™s used, and some basic algorithms.

Hereโ€™s what weโ€™ll cover today:

#algorithms #data-analysis

Get Started with Anomaly Detection Algorithms in 5 Minutes
Sydnie  Hansen

Sydnie Hansen

1626398104

Query Your Data Streams in Real Time With Kinesis Data Analytics Studio

This video shows you how Amazon Kinesis Data Analytics Studio simplifies querying data streams using SQL, Python, or Scala. A managed Apache Zeppelin notebook-based development environment and stream processing powered by Apache Flink lets you quickly analyze streaming data from a variety of sources including Amazon Kinesis Data Streams and Amazon Managed Streaming for Kafka (Amazon MSK).

Get started - https://amzn.to/3B8rQ3X
Learn more - https://amzn.to/36DXRTt

#data-analysis #sql #python #scala #aws

Query Your Data Streams in Real Time With Kinesis Data Analytics Studio
Anil  Sakhiya

Anil Sakhiya

1626339216

Building a Career in Data Science and Analytics for Professionals

Great Learning brings you this live session on โ€œBuilding a career in Data Science and Analytics for professionalsโ€. In this session, you will be learning about the current trends which envelope todayโ€™s modern world of Data Science and Data Analytics. As a professional, it becomes very important that you understand the current trends and make the best use of these domains. This session is aimed at anyone willing to understand how you can get up to speed and work with these domains. Since these two domains are vast in scope, it becomes key that you understand how you can work on learning them in a structured manner and later land a job in these fields. There are multiple ways you can start off with both Data Science and Data Analytics but there is a good chance that you might get confused when you begin learning the convoluted concepts. The instructor breaks the concepts down into bite-sized pieces along with giving you a sense of direction that you should consider when working on shaping your career in these fantastic domains

#data-science #data-analysis

Building a Career in Data Science and Analytics for Professionals

Designing a Data quality index

Measuring data quality is not something new. there are many data profiling tools available on the market that help data analysts understand gaps in their data and dig into root โ€” causes.

With data Lakes and warehouseโ€™s high importance and a growing number of activities around data, data quality is something that not only experts users should be aware of. Emerging of modern BI and Self-service analytics roles like data analyst, data scientist, or data engineer that are not into data quality details and could use simple metrics to get a quality overview of datasets they want to use.

How to design a good data quality score?

It should be seen from different angles and covered different dimensions the formula is not so clear. Letโ€™s see the requirements it shall fulfill:

1. Simple to understand. A user looking in to catalog of large number data sets should quickly get an initial understanding of how trustworthy it is without drilling down to details

2. Scaling proof โ€” if the score was run on a smaller but representative sample it should more or less similar.

3. Comparable with other data quality scores. Metrics can be different for different datasets but it should give users high-level comparison even if that sets are much different in size.

4. Normalized โ€” Clearly provided highest and lowest score and benchmark to see what can be expected and how far we are from perfect

Expectations for columns/attributes :

  • marked as mandatory to be completed

  • completed inline with the definition of valid value

  • completed with values defined in reference data source

  • relation between data sets setting the dependencies or correlations between column

How to define Data quality issues โ€” report of data quality problem type on attribute or record or group of elements, if 15 out of 100 mandatory values are missing then we can say data quality is 85%. Confidence represents the probability that data quality issue is a real business problem

Data quality for a single attribute in the record (for one cell)

  • itโ€™s True or false value is either fulfilling standard or not.

Data Quality score for attribute

  • certain attribute or column score based on rules set for these attributes.

#data-analysis #data-quality #data-governance #data-management #data quality

Designing a Data quality index
Jeremy  Reilly

Jeremy Reilly

1626075742

pyETT: Python Library for Eleven VR Table Tennis Data

pyETT: Python library for Eleven VR Table Tennis data

Eleven VR

Documentation

Documentation for pyETT is located at https://pyett.readthedocs.io/.

Installation

From PyPI

$ pip install pyETT

Download Details:

Author: souzatharsis
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/souzatharsis/pyETT
License: Licensed under Attribution-NonCommercial-ShareAlike 4.0 International.

#data-analysis #python

pyETT: Python Library for Eleven VR Table Tennis Data

A Collection of Learning Outcomes Data Analysis using Python and SQL

Data Analyst with PYTHON

Data Analyst berperan dalam menghasilkan analisa data serta mempresentasikan insight untuk membantu proses pengambilan keputusan manajerial pada suatu perusahaan. Tipe pemodelan data yang dilakukan oleh Data Analyst beragam mulai dari Exploratory hingga Predictive Modeling menggunakan algoritma machine learning untuk menemukan pola pada data yang diolah.


MATERI

PYTHON

Python for Data Professional Beginner - Part 1
Python for Data Professional Beginner - Part 2
Python for Data Professional Beginner - Part 3

SQL

PROJECT

Download Details:

Author: ladyayasophia
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/ladyayasophia/Data-Analyst-DQLab

#python #data-analysis #sql

A Collection of Learning Outcomes Data Analysis using Python and SQL
Lulu  Hegmann

Lulu Hegmann

1626074260

Stream Analyser: A Tool That Analyses YouTube Live Streams

Stream Analyser

Stream Analyser is a configurable data analysis tool that analyses live streams, detects and classifies highlights based on their intensity, finds keywords, and even guesses contexts.

For now the target environment is Vtuber streams on YouTube (particularly Hololive) by default. Though it can be manually configured to satisfy your needs, which will be explained later.

Also can be expanded to other live stream platforms such as Twitch if thereโ€™s enough support.

Currently in Alpha.

Table of contents

Installation

Use pip to install.

pip install stream-analyser

Usage

from streamanalyser import streamanalyser as sa

if __name__ == '__main__':
    id = 'Vl_N4AXspo'  # id of the stream, not the whole URL.
    analyser = sa.StreamAnalyser(id)
    analyser.analyse()
    analyser.print_highlights(top=10)
    analyser.print_urls(top=10)

Console output:

Highlights:
[1:57:35] stream end/understood: ใฏใƒผใ„, ใŠใคใบใ“ใƒผ, ใŠใคใบใ“๏ฝž, bye (331 messages, ultra high intensity, 3.479 diff, 48s duration)
[0:00:01] None: ใซใƒผใ‚“ใ˜ใ‚“pekoใซใ‚“ใ˜ใ‚“ใซใƒผใ‚“ใ˜ใ‚“pekoใซใ‚“ใ˜ใ‚“, ninjin, ใซใƒผใ‚“โ€ฆ, ใซใƒผใ‚“ (406 messages, ultra high intensity, 3.309 diff, 87s duration)
[2:02:12] None: ใ•ใ‚“ใฃpekoใณใฃใใ‚ŠใพใƒผใpekoใŠใƒผใฃ, ๅœ่ฝฆ, peko, ใ•ใ‚“ใฃ๏ผ (246 messages, ultra high intensity, 3.008 diff, 61s duration)
[1:00:17] funny moment: moona, hey, moon, ่‰ (361 messages, ultra high intensity, 2.823 diff, 78s duration)
[1:30:27] funny moment: peko, ใ‚ใƒผใŠ, big, lol (365 messages, very high intensity, 2.570 diff, 82s duration)
[1:40:12] funny moment: ่‰, lol, peko, ใ‚ใƒผใŠ (226 messages, very high intensity, 2.531 diff, 48s duration)
[1:06:24] shocked or suprised: ๏ผ๏ผŸ, ใˆ, ใˆ๏ผŸ, en (225 messages, very high intensity, 2.312 diff, 61s duration)
[1:13:50] funny moment/shocked or suprised: ่‰, ๏ผ๏ผŸ, lol, ใ‚ใƒผใŠ (304 messages, very high intensity, 2.258 diff, 64s duration)
[1:16:38] funny moment: peko็„ฆใ‚Š้ก”peko็„ฆใ‚Š้ก”peko็„ฆใ‚Š้ก”, ๏ฝบ๏พ†๏พ๏พœ๏ฝฐ, ่‰, ใ“ใ‚ (235 messages, very high intensity, 2.232 diff, 41s duration)
[1:28:12] funny moment/cute moment/shocked or suprised: ่‰, ใฏใ„ใƒโ€ฆ, ใ‹ใ‚ใ„ใ„, ใ‚ (303 messages, very high intensity, 2.189 diff, 75s duration)

Links:
1:57:35 -> https://youtu.be/K1RayPkG9xQ?t=7055
0:00:01 -> https://youtu.be/K1RayPkG9xQ?t=1
2:02:12 -> https://youtu.be/K1RayPkG9xQ?t=7332
1:00:17 -> https://youtu.be/K1RayPkG9xQ?t=3617
1:30:27 -> https://youtu.be/K1RayPkG9xQ?t=5427
1:40:12 -> https://youtu.be/K1RayPkG9xQ?t=6012
1:06:24 -> https://youtu.be/K1RayPkG9xQ?t=3984
1:13:50 -> https://youtu.be/K1RayPkG9xQ?t=4430
1:16:38 -> https://youtu.be/K1RayPkG9xQ?t=4598
1:28:12 -> https://youtu.be/K1RayPkG9xQ?t=5292

Important: Please see possible issues if you canโ€™t see Japanese characters in console.

Side note: Notice that the first two highlights will most likely be at the start and the end of the stream when highlights are sorted.

with CLI

You can also use a simple pre-built CLI

# cli.py
from streamanalyser import streamanalyserCLI

if __name__ == '__main__':
    streamanalyserCLI.main()
python cli.py --help

Key features

  • Fetch metadata of the stream
    • title, author, thumbnail etc.
  • Fetch live messages of the stream
  • Create frequency table of messages
  • Detect highlights
  • Get keywords
  • Guess contexts
  • Show highlights
    • Summary
    • Detailed
    • URL
    • Open in Chrome
  • Find messages
  • Find authors
  • Find messages made by an author
  • Visualize the data
  • Export the data

About detecting highlights

Stream analyser uses live chat to detect highlights. First, it creates frequency table of the messages and calculates moving average of the table. Then it convolves that data to smoothen the moving average even further, so that the spikes of the function becomes clearer to see. Finally, it detects spikes and marks the spike duration as highlight.

The explained algorithm will be further improved in the future.

About guessing contexts

Contexts are hard-coded into the context.json file and it requires extensive analysis of the target environments demographics and behaviors to determine them.

As stated in the description, the current contexts are based on Vtuber environment by default, but they can be modified according to your needs, which is explained in advanced usage section.

Advanced usage

WIP

Possible issues

It keeps throwing error when reading cached messages

Itโ€™s most likely caused by an interrupted I/O operation. Try these in order:

  • Run the program again with clear_cache option on (--clear-cache for CLI).
from streamanalyser import streamanalyser as sa

if __name__ == '__main__':
    analyser = sa.StreamAnalyser('Vl_N4AXspo', clear_cache=True)
    analyser.get_messages()

or

python cli.py [stream-id] --clear-cache
  • Find src/metadata/[stream-id].yaml file inside the package location and set is-complete option to False. Then run the program again with limit=None.

  • Delete all the cached files by hand in src/[cache, metadata, thumbnails]

Should the error persists, please open an issue.

Canโ€™t see Japanese characters in console

Just changing the code page to 932 should work.

C:\Your\Path> chcp 932
Active code page: 932

C:\Your\Path> ไปŠๆ—ฅๆœฌ่ชžๆ›ธใ‘ใ‚‹

Use chcp 65001 to go back. Or simply reopen the CMD.

Canโ€™t see Japanese characters in graph

Download the font here and put the NotoSansCJKjp-regular.otf file into src/fonts folder, so matplotlib can use the font.

Future goals

  • Expand to other stream platforms.

  • Automatize context guessing.

  • End world hunger.

Download Details:

Author: emso-c
Download Link: Download The Source Code
Official Website: https://github.com/emso-c/stream-analyser
License: GPL v3.0

#python #youtube #data-analysis #developer #programming

Stream Analyser: A Tool That Analyses YouTube Live Streams

A complete Data Analysis workflow in Python and scikit-learn

In this short tutorial I illustrate a complete data analysis process which exploits the scikit-learn Python library.

The process includes

  • preprocessing, which includes features selection, normalization and balancing
  • model selection with parameters tuning
  • model evaluation

https://towardsdatascience.com/a-complete-data-analysis-workflow-in-python-and-scikit-learn-9a77f7c283d3

#scikit-learn #python #data-science #machine-learning #data-analysis

A complete Data Analysis workflow in Python and scikit-learn

A Complete Data Analysis Workflow in Python PyCaret

In this short tutorial I illustrate a complete data analysis process which exploits the pycaret Python library.

The process includes:

  • Preprocessing, which includes normalisation and balancing
  • Model selection with parameters tuning
  • Model evaluation
  • Deployment over unseen data.

https://towardsdatascience.com/a-complete-data-analysis-workflow-in-python-pycaret-9a13c0fa51d4

#python #pycaret #data-analysis #machine-learning #data-science

A Complete Data Analysis Workflow in Python PyCaret

Big Data vs. Data Science

Big Data and Data Science are real buzzwords at the present time. However, what are the differences between both terms and how are the fields related to each other? Can they even be considered as competitors?

Terms & Definitions

Big Data refers to large amounts of data from areas such as the internet, mobile telephony, the financial industry, the energy sector, healthcare etcโ€ฆ Big Data can also extract figure sets from sources such as intelligent agents, social media, smart metering systems, vehicles etc. which are stored, processed and evaluated by using special solutions [1].

Data Science is about to generate knowledge from data in order to optimize corporate management or support decision-making. Methods and knowledge from various fields such as mathematics, statistics, stochastics, computer science and industry know-how can be therefore used here [2].

Against each other or with each other?

Unlike other trends, these two areas are not in competition but empower, or enable each other. New big data technologies have made it possible to analyze large amounts of data with data science tools.

Some examples of this are:

  • IOT: Only through Big Data, real-time systems can handle the flood of data and can both manage and prepare them for analysis.
  • ML: Analyses based on artificial intelligence require a lot of computing power, which is also only possible with modern Big Data cloud architectures.
  • Self Service BI: Hundreds of users building and sharing their own reports? In this case, a solid infrastructure is crucial here, to ensure a stable environment when working with large amounts of data.

So you can see that Big Data makes many of the Data Science trends possible. Of course, data analytics can also take place without modern, cloud-based Big Data technologies, but due to the rapidly growing data volumes, these are increasingly becoming a prerequisite. Once the solid architecture is implemented, there are no limits for the data scientist and analyst. They can then run their analyses without technical limitations and mostly on their own.

#data-science #data-analysis #big-data

Big Data vs. Data Science

How to Build a Data Stack from Scratch

After 7 years working as Senior Data Manager at Criteo and as Head of Data at Payfit, and after interviewing 200+ data leaders for user research, I am starting to have a good overview of data stacks in action. So, by popular demand, Iโ€™m dropping here and there some advice on the trends I observed.

Earlier in March, I had two different calls: one from a former colleague, now head of data at Swile, and one from a friend, now head of data at Cajoo. Both had taken a new job and had to build a complete data stack, from scratch! I assume this could interest others. Here is the piece of advice I gave them.

Before we start, here is a Decision Matrix that you can use for benchmark purposes:

  • Ease of Use
  • Integration in Cloud environment
  • Community & Documentation
  • Governance Capabilities
  • Pricing

To keep things simple, weโ€™ll just follow the data, from source to reporting.

Data Warehouse

Ingestion

Transformation

Scheduler

Data Viz

Data Discovery

#data #data-visualization #data-analysis #scratch

How to Build a Data Stack from Scratch
Jigar Zala

Jigar Zala

1625550046

How useful are certificates to get a data science job?

How useful are certificates in getting a data science job? I will discuss my honest opinion on how much you should focus on getting data science or programming certificates

Topic:
Data science certificates
Certificates for data science
Certification necessary for data science

https://youtu.be/HCbZujfzDEc

#data science certificates #data-science #data-analysis

How useful are certificates to get a data science job?

He Got 4 Data Analyst Job Offers | Mechanical Engineer to Data Analyst Transition

He got 4 data analyst job offers: Sayan chakraborty is a mechanical engineer turned data analyst who recently cracked 4 job interviews as a data analyst from following known companies in USA,

  • FedEx
  • TD Bank
  • Bluecross Blueshield
  • Optum

We will discuss,

  • Tips to crack data analyst job interviews
  • Resume and networking tips
  • His transition journey from mechanical engineer to data analyst
  • Discuss various ways one can transition from mechanical (or any other domain) to data science career

Sayanโ€™s linkedin: https://www.linkedin.com/in/sayanschakraborty/
Power BI project series: https://www.youtube.com/playlist?list=PLeo1K3hjS3uva8pk1FI3iK9kCOKQdz1I9
Tableau project: https://www.youtube.com/playlist?list=PLeo1K3hjS3usDI9XeUgjNZs6VnE0meBrL

๐ŸŒŽ Website: https://www.skillbasics.com/

๐ŸŽฅ Codebasics Hindi channel: https://www.youtube.com/channel/UCTmFBhuhMibVoSfYom1uXEg

#๏ธโƒฃ Social Media #๏ธโƒฃ
๐Ÿ”— Discord: https://discord.gg/r42Kbuk
๐Ÿ“ธ Instagram: https://www.instagram.com/codebasicshub/
๐Ÿ”Š Facebook: https://www.facebook.com/codebasicshub
๐Ÿ“ฑ Twitter: https://twitter.com/codebasicshub
๐Ÿ“ Linkedin (Personal): https://www.linkedin.com/in/dhavalsays/
๐Ÿ“ Linkedin (Codebasics): https://www.linkedin.com/company/codebasics/

โ—โ— DISCLAIMER: All opinions expressed in this video are of my own and not that of my employersโ€™.

#data-analysis #developer

He Got 4 Data Analyst Job Offers | Mechanical Engineer to Data Analyst Transition