fabiochiu/medium-articles huggingface dataset:
Crispy Salmon Recipe With Lemon Butter Sauce — Cafe Delites
Tags: Recipe, Foodies, Food, Food And Drink

Today we are going to make Crispy Salmon Recipe With Lemon Butter Sauce. This recipe is very delicious. The way we are going to make this recipe, we should try to make this recipe in the same way. All your family will love it. cafe delites salmon

First of all, you have to wash the salmon fish with nice clean water and then you have to remove the skill with the help of a knife and you want to cut the 6 pieces of salmon fish into 1 inch thin slices because you want to make your salmon recipe crispy.

Now put Salt, Turmeric Powder, Black Pepper on Salmon Pieces and start Gas Flame Medium and put Pan on it and add Butter Pieces (45 gm). And Butter Melt. Now add Salmon Fish Pieces.

Fry Salmon Pieces on one side for 5 minutes and on the other side for 5 minutes. You can fry Extra for 2–3 more minutes. So that the Pieces will be crispy and delicious to eat. Remove Fry Salmon Pieces.

Now make Gas Flame Medium and put Pan on it and add Butter Pieces, White Wine, Lemon Juice, Cream, Chopped Cilantro, Salt And boil the sauce for 5 minutes. Now your Recipe is ready. cafe delites salmon

Crispy Salmon Recipe With Lemon Butter Sauce Literature
Persimmon jam tart — best way to use your ripe persimmons!
Tags: Persimmon, Dessert, Recipe, Italian Food, Tarts

Do you have some ripe persimmons and you don’t know how to manage this delicious plentiness? This persimmon jam tart can be the perfect solution to your problem!

Today you will learn:

how to prepare a delicious and super healthy jam from your persimmons , adding just some sugar and lemon juice

, adding just some sugar and lemon juice how to bake your first homemade persimmon jam tart.

Preparing a tart is definitely an easy project, but starting from raw ingredients is what makes this recipe different.

The persimmon jam

Using the fruit you buy to obtain your jam is so much healthier and satisfying than baking your tart using a jam from industrial preparation. Even more if you are lucky enough to grow your own fruit or have a farmer nearby selling fresh fruit and vegetables.

In the step-by-step instructions below, I am showing how to prepare a persimmon jam with just three ingredients:

persimmons (well, that was easy!)

cane sugar

lemon juice.

As brilliantly explained in this Serious Eats article, the basic ingredients for fruit jam is:

fruit: here, the persimmons;

sugar: my choice is cane sugar, just because I love the taste of it;

pectin: pectin, with acid (see below) and heat, will create a gel that gives the right thickness to the final product. That’s why with some fruit it is recommended to add some apple, rich in pectin. Persimmons are naturally rich in pectin, so the jam can be pure persimmon;

acid: here, the lemon juice.

The rich shortcrust pastry

My mom has always used a basic dough for any tart and most of her homemade biscuits (I love the shapes she was creating, so funny for a little child). That’s why I took some time to share with you my family recipe for rich shortcrust pastry in a separate post, so that you will be able to reuse that dough in many different ways. And if you are looking for easy Italian biscuits for coffee… try that and let me know!

But now let’s see how you can bake your persimmon jam tart in your kitchen!

To read my step-by-step guide to your first persimmon jam tart, visit:

https://www.eatalianwithroberto.com/persimmon-jam-tart/

Buon appetito!
Grandma Isabel’s brisket recipe
By: Julia Kulla-Mader
Tags: Recipes, Family

For years, I’ve associated family gatherings with a particular smell that I couldn’t quite identify. As a kid, my whole family would gather at my Aunt Pauline’s house for every Jewish and non-Jewish holiday. We hid matzah, had loud meals, and celebrated every birthday. I remember Pauline and my grandmother’s homes smelling sweet and salty at the same time.

My grandmother and Aunt Pauline lived next door to one another, so their small kitchens combined to make one giant one. Pauline’s kitchen was the home of major dishes like matzo ball soup. My grandmother’s kitchen was the home of brisket, scalloped potatoes, and a unique Jell-o concoction made in a fish mold.

With Hanukkah coming up during the pandemic, I craved the smell of my grandmother’s kitchen. The irony is that I’ve been a vegetarian since age eleven. Despite my grandma’s urgings that chicken and fish are not meat, I’ve stayed a vegetarian since then.

I feel very lucky that my Cousin Ellen (Pauline’s daughter) took the time to dictate my grandmother’s recipe to me. A couple hours into cooking the brisket, I felt like I was back in my grandmother’s kitchen. I could imagine sitting at her linoleum table, and watching her make Folgers Coffee.

While I didn’t eat the brisket, my family can attest that it was remarkably good. And it smelled great. I hope you enjoy it as much as we did.
A General Intro to AI
By: Ceyhun Burak Akgül
Tags: Articial Intelligence, Lecture Notes, Data Science, Data Analytics

While it’s probably reductionist to equate Data Analytics (DA) with Artificial Intelligence (AI), the interconnections are undeniable. To start with, they both take their raison d’être from that insurmountable human need of making sense of things that surround us. They both require data to produce knowledge out of it. At their best (but not always), they are both automatic in their core processes. They are expected to perform at scales no human being can be expected to succeed. And finally, they both need human designers and practitioners to be interesting (at least for the time being and for the foreseeable future).

In one of the very first lectures of City As A Data Mine, we introduce AI from its origins residing in the field of logic and computation. One of the starting tenets of AI has been that logical reasoning can be automated, that is, logical inference from observations does not require the involvement of a human being (leaving aside the underlying computer program and the computation hardware that runs the program). In the 1960s, that fundamental tenet hit a road block that is called perception. While logic can be automated to a great extent (modulo a number of incompleteness theorems, remember Gödel), perception has not been so much until recently. In fact, for the last half a century, it has been a daunting task to make computers transform perceptual data (images, voice, music, etc.) into practical knowledge and automated systems. That has changed with the advent of Deep Learning, designated computation hardware (GPUs, optimized CPUs, FPGAs) and digitized perceptual data produced at unparalleled zeal (take Instagram, take Spotify, take all apps in your mobile).

Here in this talk, we discuss these aspects and provide examples from the fascinating field of AI so that our architectural audience gets excited for the things to come.
Data Analytics Primer
By: Ceyhun Burak Akgül
Tags: Lecture Notes, Data Science, Data Analytics, Machine Learning

In this lecture, we introduce the field of Data Analytics to a general audience.

Starting from its very objective, we discuss the distinctions between Data, Information and Knowledge through several examples on a usefulness scale.

On the same usefulness scale, we show where Descriptive, Predictive and Prescriptive Analytics reside and introduce Machine Learning as an indispensable toolbox of Analytics methods.

The last part of the lecture is devoted to data table design for practical applications with an example from the Turkish culinary tradition. The interested reader can check the example from here.
WTM new partnership with Polecat intelligence in Data Analytics | Passionate In Analytics
Tags: Analytics, Wtm, Willis Towers Watson, Data Analytics, Polecat Intelligence

To help organizations manage the increasingly perplexing dangers they face, Willis Towers Watson (WTW) has gone into an organization with social business insight and information investigation firm Polecat Intelligence.

Through the association, the two organizations will create protection arrangements that will assist organizations with reacting to the quickly changing difficulties they currently face, an organization release said. Specifically, Polecat’s information will be utilized to help WTW’s answers in territories their core problems, for example, life sciences, reputational, and item review hazard, just as D&O. The information will be gotten from unstructured information prepared through Polecat’s machine learning and artificial intelligence.

“The association with Polecat empowers Willis Towers Watson to offer customers continuous understanding to general assessment through investigation of online media,” said WTW head of worldwide business sectors P&C center Garret Gaughan. “By working with an accomplice ready to dissect unstructured information progressively we can plan and actualize hazard relief systems for our customers.”

“Polecat exists to give premonition to corporate sheets and chief authority groups to settle on better choices on arising dangers and openings,” said Polecat co-CEO James Lawn.

Grass added that corporate sheets and chief initiative groups are searching for protection and innovation that permits them to deal with their dangers — and Polecat’s association with WTW empowers that.

“The association between Willis Towers Watson and Polecat, flags another age of imaginative danger the board arrangements consolidating AI-controlled information examination, consultancy, and protection in one spot,” said Airmic CEO John Ludlow.

according to a new study from Willis Towers Watson Demand for predictive analytics is on the rise as life insurers look for new ways to boost business performance and customer relations in a tight market. Sixty-eight percent of insurers surveyed agreed that predictive analytics had a positive impact on increased sales and cross-selling, and 41% reported that predictive analytics helped reduce issue/underwriting expenses and claims costs.

However, the survey found that while some life insurers are making progress with their predictive analytics capabilities

Willios Tower Watson’s new partnership with polecat intelligence is for new opportunities and they believe that the winners will be those organizations that apply digital technologies to be connected, analytic, and agile.
Kolmogorov–Smirnov test(KS Test) in Python.
By: Kunal Mahajan
Tags: Statistics, Machine Learning, Data Analytics, Artificial Intelligence, Data Science

KS Test is used to check if two continuous distributions follow the same distribution or not. Below is the Python code for performing KS Test.

# Import Libraries

import numpy as np

import seaborn as sns

from scipy import stats

import matplotlib.pyplot as plt #Generating Normal Random Variable X

x = stats.norm.rvs(size= 100) #Draw Kernel Density Estimation Plot for X

sns.kdeplot(np.array(x),bw = 0.5)

plt.show() #Use kstest function available under scipy to compare X with Normal Distribution. stats.kstest(x, ‘norm’)

#Output :

KstestResult(statistic=0.063772217543546034, value=0.81075786049050036)

Conclusion:

P-value is higher than the Significance level(Also known as Alpha .05 in our case) so we accept the Null Hypothesis which implies x is normally distributed.

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Three IBM Certifications in a row: Data Science, Analytics & AI
By: Robert Hernández
Tags: Ibm Watson, Ibm Certification, Data Analytics, Artificial Intelligence, Data Science

Just beginning pandemic confinement my purpose was to improve skills in Data Science through online programs. One of my first choices was the IBM Data Science Professional Certificate offered free for some time.

I was surprised by the great overview of this program after attending the nine courses of the series:

1. What is Data Science?

2. Tools for Data Science

3. Data Science Methodology

4. Python for Data Science, AI & Development

5. Python Project for Data Science (NEW!)

6. Databases and SQL for Data Science with Python

7. Data Analysis with Python

8. Data Visualization with Python

9. Machine Learning with Python

10. Applied Data Science Capstone

Early in 2021, IBM has added a 10th course to upgrade this certification: Python Project for Data Science, so is highly recommendable to update your certificate because “Knowledge is tomorrow’s wealth”.
Qlik Sense and Creating the First app
Tags: Data Visualization, Qlik Sense, Data Analytics

I am writing a series of articles related to Qlik Sense and visualizations. Here is my first article creating an app and loading the data. First, we know about data analytics and Qlik Sense.

Data Analytics

Data Analytics refers to the set of quantitative and qualitative approaches to deriving valuable insights from data. It involves many processes that include extracting data and categorizing it to derive various patterns, relations, connections, and other such valuable insights from it. Today, almost every organization has morphed itself into a data-driven organization, and this means that they are deploying an approach to collect more data that is related to their customers, markets, and business processes. This data is then categorized, stored, and analyzed to make sense out of it and derive valuable insights from it.

Qlik Sense

Qlik Sense is a complete data analytics platform that sets the benchmark for a new generation of analytics. With its one-of-a-kind associative analytics engine, sophisticated AI, and high-performance cloud platform, you can empower everyone in your organization to make better decisions daily, creating a truly data-driven enterprise.

If we want to build data analytics or learn about qlik sense we can signup here for free and can build apps with the 30 days free trial.
A-Z Machine Learning
By: Kunal Mahajan
Tags: Python, Machine Learning, Data Analytics, Artificial Intelligence, Data Science

The aim of this blog is to create a learning platform for Data Analytics and Machine Learning.

We will help you to start your career in Machine Learning by learning from Scratch.

This will be covered in 4 Parts.

Python Statistics and Probability Machine Learning NLP and Deep Learning

Follow our blog to get the latest updates.
Finishing December With Cool Updates to Remember
By: Miha Vidmajer
Tags: Product Updates, Music, Analytics Tool, Data Analytics, Music Business

Finishing December With Cool Updates to Remember

With the holiday season just around the corner, our “Santa’s workshop” was busy as ever. Sure, we wrapped up some presents to put under our office Christmas tree, but we also wrapped up a cool number of updates. Go check out our site and see if you notice anything different 😉

More on that later. Now, here are four important new features you should know about.

#1 Beatport analytics: see an Artist’s latest collab tracks and label releases, and compare their ranks

Beatport analytics for Artists have been upgraded with an easier overview of each Artist’s latest releases on a specific Label and their latest collabs with other Artists. This sorting allows for a quicker overview of their productivity and networking, and easier tracking of their most recent work.

That first date isn’t a glitch — once a track is available as a pre-order on Beatport, its official release date will be automatically entered into our analytics.

The track overview section now also shows how the Artist’s success in entering Beatport’s Top 100 charts compares to similarly ranked Artists (look for “average % of Tracks on Top 100 Charts”). This indicator will provide an additional frame of reference to your analysis.

UMEK’s example shows he’s doing quite well.

#2 Beatport Label analytics display each Artist’s most recent releases

To ensure an even quicker overview, the “Artists on the Label” section is further improved with listings of the latest tracks that each Artist has released on this Label.

1605’s example shows the chronological display, as well as listing the Artists’ most recent (and upcoming) collabs.

#3 New in Audience analytics: Monthly Streaming Audience City Map

Each Artist can examine where in the world their Spotify listeners and YouTube views are coming from. Both the list and the map are clickable.

#4 Explore Genres by their Spotify numbers and talked-about Artists

Genre analytics got a new section: Top Countries by Spotify Monthly Listeners. This can be especially handy for evaluating and planning your marketing strategies, as you can estimate your genre’s potential in countries worldwide (as long as Spotify’s available there).

And since the music industry is all about the right connections, our “Most Mentioned Artists” section of Genre analytics now also lists the top three most influential names who recently mentioned a certain Artist.

It wouldn’t be a holiday season without some excitement, so we’re also going to be rolling out a major announcement soon. For now, we’re keeping things under (decorative) wraps, but we’ll let you know as soon as it’s out.

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Chpter Integrated System (CIS) | Integration
By: Mark Kiarie
Tags: Machine Learning, Data Analytics, Fintech, AI

In most financial institutions, the debt appraisal process is thoroughly decentralised. Consider the financial forecasting process for example, existing workflows result in prolonged deliveries due to a heavy reliance on traditional inputs such as pen and paper, spreadsheets and word documents which are laborious and time consuming.

chpter. is on a mission to build financial forecasting infrastructure that will revolutionise the future of Property and Asset finance across Africa. Automation, Integration and Analytics are at the core of the solution being provided by the Chpter Integrated System (CIS).

Existing workflows mean credit specialists have to interact with a wide array of software applications to accomplish simple tasks in the debt appraisal process. Not only does this cause great inefficiencies but also possible data loss through unstructured operational processes. The result is inaccurate financial assumptions and delayed decision making.

The CIS workflow offers an integrated solution that allows for importing and cross checking of applicant data, financial forecasting through scenario analysis and quick reporting. This is done through CIS Integrate. Our integration module creates a framework where all tasks party to the debt appraisal process are undertaken in one place. This greatly increases efficiency and accuracy.

A single centralised data input framework will enable financial institutions reduce on missed opportunities and provide a significantly better experience in appraising debt for their customers. By leveraging our technology these institutions can reduce delays and increase turnover, ensuring high returns.
Importance of Analytics and Master Data
Tags: Data, Analytics, Data Analytics, Master Data Management

Data influence the future of a business; the organizations are more focused on data to improve the quality and standard of the product or service. The data scientists are predicting the future of business by analyzing the data.

As we know, various factors are influence the future of business. But the data scientist is forecasting the future by using previous data, current trends, environmental situation, etc. The environmental factors regularly changing, this may mislead the prediction. Moreover, the relevance of the data may affect negatively. The master data plan helps to identify the relevance of data. One of the major barriers faced by the data scientist is the irrelevancy of data. The quality and quantity of data influence the prediction process. So the data scientists try to create master data to overcome these barriers.

The organizations are aware of the importance of data and the analytical process. But some of the organization still follows the conventional method of approach. But the scientific approach of prediction helps to improve the productivity and growth of the business. Not only in economic, but also for social growth. If the organization is aware of the future market, then they can change their strategies to adapt to the situation. For example, if aware of the covid-19 pandemic before 2019, we try to prevent the outbreak of the virus at any cost. At the same, the organization takes advanced measures to succeed.

The prediction process is not a simpler task; data scientists are taking different factors into account to predict wisely. The accuracy of prediction may vary; different known and unknown factors influence the upcoming happening. The prediction also influences the growth of the organization. Investors are confidently investing in such organizations for increases the value of money. The merging and acquisition are happening depending upon the predictive result. The probability of growth rate analysis and easily identifies the benefits of merging and acquisition. The corporate is always rely upon these scientific predictive methods. These methods are helpful for the organization to achieve the objective easily. Technology always helps to improve the growth of the organization.
Bias and Variance Explained Clearly
By: Alka Pandey
Tags: Bias Variance Tradeoff, Machine Learning, Data Science Training, Regression Analysis, Data Analytics

Bias is a lack of signal — When your model misses capturing relationships that actually exist between dependent and independent variables, These relationships are important for accurate prediction. This is also called underfitting

Variance is when the model takes noise as a signal — i.e. Captures patterns and relationships in data that do not exist. These patterns won’t appear in the test set and the model does not generalize well. This is also called overfitting

We can say that if the variance increases, the spread of our data point increases which results in less accurate prediction. And as the bias increases the error between our predicted value and the observed values increases

Ideal Situation always is Low Variance and Low Bias as depicted below:

Source: Scott-foreman-roe illustration

Possible solutions:

1. Increase training data

2. Manage model complexity via regularisation — Try ridge/lasso regression techniques

Source: Google

Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression.

Ridge Regression:

1.) It shrinks the parameters, therefore it is mostly used to prevent multicollinearity.

2.) It reduces the model complexity by coefficient shrinkage.

3.) It uses the L2 regularization technique

LASSO (Least Absolute Shrinkage Selector Operator)

1.) It uses the L1 regularization technique

2.) It is generally used when we have more number of features because it automatically does feature selection
Learning SQL, Part 5 — Group By Statements
By: Barrett Studdard
Tags: Data Analysis, Data Analytics, Data Science, Sql

Learning SQL, Part 5 — Group By Statements

This is the fifth article in the learning SQL series:

Aggregating in SQL: GROUP BY Statements

Previous articles reviewed how to select relevant records from tables and apply filter conditions as needed. However, many times in reporting you want to aggregate results and perform additional calculations — such as counting the number of rows for a group or adding subtotals. This is where the GROUP BY statement comes in.

For this example, a new table will be used — FactResellerSales — in order to demonstrate how to aggregate sales dollars by date. The table has an OrderDate column that we’d like to see the total ExtendedAmount displayed for each day.

To get the subtotal by days, we’ll use the SUM aggregation function and GROUP BY on OrderDate. The general idea is to aggregate a metric using one of the various aggregation functions available and all the other columns (typically descriptive attributes such as dates, products, etc) will be included in both the select and group by statements.

Example 1 Query: Sum Aggregations

SELECT

OrderDate

,SUM(ExtendedAmount) as [ExtendedAmountTotal]

FROM [AdventureWorksDW2017].[dbo].[FactResellerSales]

GROUP BY

OrderDate

ORDER BY

OrderDate

Example 1 Results

Other Aggregate Functions

While SUM is the most commonly used aggregation function, there are many more that can be used. A list of aggregation functions can be viewed on the Microsoft documentation page. Max, Min, Avg, and Count are some of more frequently used functions in addition to Sum.

Next Steps

Try some of the above functions (in addition to Sum) on your own to get a feel for using these. You can even combine with previous concepts — for example COUNT(DISTINCT LastName) from the DimCustomer table will give you a distinct/unique count of customer last names.
Indian Railway use AI and Data Analytics to improve operational efficiency
Tags: Analytics, Operational Efficiency, Indian Railways, Data Analytics, Artificial Intelligence

India’s largest transporter Indian railway uses Artificial intelligence and big data analytics to improve operational efficiency. Mainly Indian railway uses Artificial intelligence and big data analytics in train day to day operations, ticket booking, systems maintenance, freight operations, and railways assets such as 121,407 track km, 34,319 route km of electrified track, 12,000 locomotives74,000 coaching stock,7,500 stations, etc. Indian railway is gigantic and wide that’s proved by its passenger and freight carrier figures, in 2018–2019 it is 1.2 billion tonnes of freight and 8.4 billion passengers. Indian railway is the world’s largest passenger carrier and fourth position in freight carrier.

With the use of big data and artificial intelligence, India’s national railway explores new opportunities and surely reach new heights. According to the official notification, Indian Railways uses Artificial intelligence and big data analytics use to analyze data after that use in their passenger reservation system(PRS). Sometimes this will help in the introduction of new trains and predictive asset maintenance.

Indian railway planned to change Present system follows by Indian railway in maintenance after the introduction of AI and big data analytics, Currently, they carried out maintenance in a periodic manner. For immediate maintenance, predictive asset maintenance will continuously monitor the conditions of the equipment and initiate the required alerts. This not only reduces maintenance costs also help to downturn costs related to assets maintenance.

The Indian Railway tied up with the Indian school of business(ISB), Hyderabad with a positive view to analyzing the data produced by Indias largest transporter. Not only for analyzing data also for Indian railway tasked ISB for designing of the introductory capacity building program and also for the construction of report on the industrial practices of artificial intelligence on rail transport. The Indian railway has a good concept, ie they planning to set up a center for excellence in the next three months.ISB offers large helping hands to develop a report on establishing a center of excellence and for the capacity building program.

In every Zonal railway, the railway has also decided to appoint a chief technology officer (CTO) and assigned the duty to keep track of emerging technologies in analytics and Artificial intelligence and apply them effectively in every rail system. The railway already trained around 100 officials in this AI and big data analytics, Because the Indian railway knows through this they will get a better return.
Data Visualization with Google Data Studio
By: Oyetola Taiwo
Tags: Google Data Studio, Data Visualization, Data Analytics Tools, Data Analytics, Data Science

If you are a professional who works with data and you are looking out for another tool that is going to be helpful in querying huge data sets and giving analytical reports/dashboards. Google Data Studio is just an amazing tool for you to look into.

The highlights of Google Data Studio are as follow:

It integrates with conventional data sources like .csv, .xlsx, .xls

Also integrates with Google SaaS applications like the Google sheets easily: in other words, you could upload your local office worksheets to Google drive and it can easily be accessed by Data Studio from there.

It already has a number connectors to any kind of data source outside google products. e.g. a connector to connect to a MySQL or POSTGRESQL database.

Data Studio also allows you build and publish our own custom connector incase you want to connect to a datasource that does not already have a connector. You can do this using App Script. (Open source Developers will find this interesting)

Three Major things Data Studio helps you do:

Connect to Any data source:

This connection is done with the help of connectors built by google or built by open source devs. The connector enables you to connect from data studio to an internet accessible data source. There is a ton of connectors that have been built by Google and other Open source organizations or individuals that you can leverage. Examples of connectors that already exist: Facebook Ads, Facebook Post Insights, Google Analytics, Google Ads, Google sheet, Mysql, Postgresql, Mssql etc. .

Visualize:

When connected to your data source the connector helps you pick up the schemas, recognizes the fields and then allows you to play around the data to generate reports in graphs, charts, tables and other analytical representation formats. You could build a nice report dashboard styled with the embedded styling tools like colors, text, number formats, dropdown filters etc. You can insert any element that enhances your report as provided in data studio work area. The visualization gives the latest representations based on the data source, The dashboard also automatically refreshes after some time to ensure the latest data is presented to the viewer.

Share:

When you are done building your reports or dashboards you can share with anyone at all to view your work. And as the data source changes, The report interface is updated based on the new data.

To read more about Data Studio and how you can begin to make use of it for data visualization and analytics check the official page of Google Data Studio
Mohit Kothari, thank you for responding! That’s an excellent contribution. I agree.
By: Matt Przybyla
Tags: Data Analysis, Machine Learning, Towards Data Science, Data Analytics, Data Science

Would You Rather be a Data Analyst or Data Scientist?

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GRC solution for Banking and Financial Services
By: Dineshu T
Tags: Ncs, Ethic, Fintech, Dataanalytics, AI

Role of Data Analytics in Internal Audit

Data Analytics plays an increasingly growing role in Internal Audit at different stages in the Audit Lifecycle.

From planning, execution, and reporting, data analytics is being applied to achieve an effectively optimized process.

To know more about NCS Soft: https://ncssoft.in/

#DataAnalytics #AI #NCS #Fintech #eTHIC #NCS #NCSSoft #eAudit #AuditTechnology

#FinTech

NCSSoft is a product development company primarily focused on developing products for the financial sector. Completing a decade of delivering comprehensive solutions for Banking and media industries, NCSSoft is the preferred Auditing solution provider for top financial institutions across India. With a user base exceeding 1 lac, we are poised to become a global leader in the Auditing and Compliance space.
Data Analytics — Key to solving customer’s challenges
By: Sweta Dey
Tags: Data Analysis, Data Visualization, Data Analytics Tools, Data Analytics, Data Science

Let’s start from the very basic as in what really is Data Analytics?

Almost every one of us has heard this statement “Data is the key to almost every business problem”. This statement is possible only through the technique known as Data Analytics.

The process of analyzing data to draw conclusions about the information it holds is known as Data Analytics with the aid of advanced systems and software. It helps an organization in making more informed business decisions based on the results of Data Analytics. It is a widely used technique by each and every organization to help them in their business strategies and making firm decisions.

Data analytics basically summarizes large datasets to describe what’s really happening in the organization to the stakeholders. The performance of an organization can also be tracked to get an overall look at how things are working out. Performance can also be compared over various years using Key Performance Indicators(KPI’s).

Data analytics is always confused with business analytics, in fact, data analytics has a broader focus than business analytics. Business analytics is only for business uses, but data analytics gives an overall insight into the organization.

There are many benefits related which drive-in in along with Data Analytics. It helps in increasing revenues, improving efficiency on operations, optimizing customer services efforts, and responding more quickly to the changing trends in the market, and gain more control over the rivals. The only reason for all this is to boost the business performance of the organization.

With all the hype of Data Analytics in the market, Data Analytics tools have emerged to serve this purpose. There are many tools that can serve as a medium for Data Analytics.

Some of them are R programming, python, tableau, QlikView, SAS, Microsoft Excel, RapidMiner, KNIME, OpenRefine, and Apache Spark. Apart from these, there are many more tools providing these types of services.

Data Analytical Services offers reliable, impartial research performance analysis by integrating high-quality data sources with technological and bibliometric expertise which is best offered by Cymetrix Software.

Data analytics for financial services, data analytics financial services, data analytics in financial services serves various other purposes apart from just making firm decisions for the organization. It provides services such as Managing data of customers, marketing in personalized zones, prediction of values, modeling risk for investment banks, detection of fraud, customer spending pattern as well as real-time analytics.

Data Analytics Consulting Services is a recent development in the business analytics industry. It optimizes the different business intelligence activities by optimizing data using a handful of methods. Data Analytics Consulting Services is a functional role, combining business and hardcore analytics with the goal of providing value-added analytical solutions.

Advantages of Data Analytics:
La investigación como diferencia de mercado
By: Good Rebels
Tags: Marketing, Digital Marketing, Market Research, Data Analytics, Data Science

We work at the intersection of people, brands and technology, challenging our clients to explore the digital world and create rich consumer experiences.
Data Ecosystem
By: Gerrishon Sirere
Tags: Database, Data Visualization, Data Science, Data Analytics

Data Ecosystem

In data analysis, a data ecosystem includes the software, infrastructure, tools used, frameworks, and methods used to gather, prepare, analyze, mine, and visualize data.

Data can be categorized as structured semi-structured, or unstructured.

Structured data can be organized into rows and columns an example of structured data is spreadsheets.

Semi-structured data is a mix of data that has consistent characteristics and data that doesn’t conform to a rigid structure. For example, emails. An email has a mix of structured data, such as the name of the sender and recipient, but also has the contents of the email, which is unstructured data.

Unstructured data: Data that is complex, and mostly qualitative information that is impossible to reduce to rows and columns. For example, photos, videos, text files, PDFs, and social media content.

The type of data drives the kind of data repositories that the data can be collected and stored in, and also the tools that can be used to query or process the data. Data also comes in a wide-ranging variety of file formats being collected from a variety of data sources, ranging from relational and non-relational databases to APIs, web services, data streams, social platforms, and sensor devices. This brings us to data repositories: A term that includes databases, data warehouses, data marts, data lakes, and big data stores. The type, format, and sources of data influence the type of data repositories that you can use to collect, store, clean, analyze, and mine the data for analysis. If you’re working with big data, for example, you will need big data warehouses, that allow you to store and process large-volume high-velocity data and also frameworks that allow you to perform complex analytics in real-time on big data. The ecosystem also includes languages that can be classified as query languages, programming languages, and shell and scripting languages. From querying and manipulating data with SQL to developing data applications with Python, and writing shell scripts for repetitive operational tasks, these are important components in a data analyst’s workbench. Automated tools, frameworks, and processes for all stages of the analytics process are part of the Data Analysts ecosystem. From tools used for gathering, extracting, transforming, and loading data into data repositories, to tools for data wrangling, data cleaning, data mining, analysis, and data visualization — it’s a very diverse and rich ecosystem. Spreadsheets, Jupyter Notebooks, and IBM Cognos are just a few examples.

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Top 5 books to read in 2021 for data analysts and data engineers
By: Christie Wilson
Tags: Books, Data Engineering, Book Review, Book Recommendations, Data Analytics

There is a need to upgrade with the growing technology right? If not the skillset one possesses would be siloed to the world’s tech trends and as years go by, there would be a lot of ground to cover.

Books are the major source of knowledge, right from primary education to the greatest of entrepreneurships. Any successful personalities have always found a way to compliment how important a role books played in their lives. It happens to be more of a lifestyle than a practice.

However, from this list of books, data analysts and engineers could benefit either on the data cleansing, preparation and transformations or most importantly on the implementation strategies on such operations too.

Few books from this list might focus on the “what not to do” when it comes to data analytics. Few books might serve as a compilation of examples and exercises for analysts and engineers.

This list also consists of statistical learning and how such practical statistics can benefit the readers and also with R applications. To find out the list of books and to understand about the books in detail, access the following link

Top 5 Books to read in 2021 for data analysts and data engineers.
6 Wünsche, die Banken ihren Kunden dank Data Analytics und Enrichment erfüllen
By: Jannika Anne Otte
Tags: Digital Banking, Updates, Data Analytics, Customer Experience, Data Driven Banking

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
How to save EUR 135,000 annually with Google Analytics 4 + BigQuery
Tags: Integration, Google Analytics, Bigquery, Data Analytics, Digital Analytics

The term “game-changer” gets thrown around quite easily, often to an underwhelming effect. However, every once in a while, a tool or technology appears on the market that really deserves the title. Such is the case with our today’s subject, Google Analytics 4.

Not only does it introduce a set of radical updates compared to the previous version, but it also anticipates a revision of how digital marketing will be conducted in the future.

On top of that, it holds a prospect of significant monetary savings, as it offers a free (or near-free) alternative to some of the functionality previously only found in the costly Google Analytics 360, a good example of this being the out-of-the-box integration with Google BigQuery.

Learn about the impact and benefits, and the cost of this integration in the following article →
Data + Design — A Winning Combination
By: Kevin Coombs
Tags: Design Thinking, Data Analysis, Data Analytics

How Appnovation Does It — Agile Data Design Thinking Framework

We approach reporting and analytics in the same way we do designing user experience and start with assembling the right team. When working with clients, we form a team that has strategy, design and engineering/technology to ensure that our deliverables don’t fall short on one of those success elements.

However, throwing people together and asking them to collaborate doesn’t always work (even here!), so we created a process that harnesses the best of Design Thinking and the expertise of each contributor. We call it the Agile Data Design Thinking Framework. It has 4 stages:

Stage 1 — Discover and Understand

At the start, we focus on understanding what the client needs are, where they are today and what success looks like. We also learn everything we can about the context of this challenge, including speaking to end users who will be using the solution.

Stage 2 — Design and Create

Based on Stage 1, we start to turn needs/wants into specific KPIs, business rules and logic for how the data will be used to uncover insights. Designers translate journeys and needs into visuals, information architecture and a look/feel that meets the users needs. Technology team members evaluate what is needed and how it’s to be delivered against what is available. This is a critical step to ensure that we avoid over promise/under-deliver.

Stage 3 — Refine and Deliver

There will be gaps between what clients want and what can be delivered. In this phase, those gaps are surfaced and resolved, either by adjusting requirements, providing different solutions or changing designs. There is no one size fits all — it requires iterations, communication and deep client involvement. We’re transparent with clients at the beginning that this stage will require their involvement, and they should expect to see work-in-progress as a way to clarify and bridge needs.

Stage 4 — Deploy and Learn

Now we send the report/dashboard into the wild and see how it’s used. Does it answer those key questions we identified at the outset? Are we getting questions back for enhancements? Is it feeding into decision making? Can we scale up and add more users? No report/dashboard is ever really done, so we build this into the project to ensure that it’s adopted.
APRA Fund-Level Statistics
By: Mark Monfort
Tags: Data Analysis, Finance, Power Bi, Data Visualisation, Data Analytics

Note: This app was published prior to my joining Prosperity Advisers in January 2020

Description

APRA (Australian Prudential Regulatory Authority) releases annual updates which showcase various insights into fund performance. This data is made available as a set of Excel tables/files.

This app makes the data more explorable and has a main dashboard with 6 tiles that users can click on, an information button to describe what is on each page and a back button for users to navigate back to the homepage once they have drilled down into the app.

Application Link:

https://app.powerbi.com/view?r=eyJrIjoiMDVkYWNjMzctN2Q3YS00NmRmLWEyZGItNDg2Y2ZkZjQwMTkzIiwidCI6ImM4ODdmZGYyLWIwYTYtNDFlMy04ZmE4LTdmMjFkMDYyZGE4NSJ9
“J’ai obtenu mon poste le jour de mon Demoday !”
By: Antoine Krajnc
Tags: Jedha Bootcamp, Big Data Career, Data Science, Data Analytics

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
Midem While Snowing outside? Sure, Why Not.
By: Vasja Veber
Tags: Networking, Music Data, Data Analytics, Midem, Music Business

Midem While Snowing outside? Sure, Why Not.

Midem is one of our favorite music conferences. It features almost one week of keynotes, workshops, panels, and networking events — that’s all good. But the best thing? It’s held on the French Riviera in the summer! There’s no better feeling than going on a business meeting, wearing flip-flops and sunscreen, and trying to keep your sh*t together after a late hour networking afterparty the night before.

We had everything booked for this year’s edition: plane tickets, an entire house for the team, we even pinpointed the bakery where we would buy croissants and baguettes. But because 2020 decided we can’t have good things, we had to cancel everything.

Still, the good people of Midem decided to do a week-long online networking event, hosting artists, managers, music tech companies, investors and pretty much anyone else who has ever been backstage. Viberate was invited to speak at a kind of a speed-dating panel about music metrics, along with colleagues from Chartmetric, Nielsen, and ROSTR. We all had 15-minute blocks to discuss data analytics, and the moderators randomly selected 20 people to attend our short presentation and a Q&A session.

Here we are. That “Muppet show intro” meme comes to mind.

Takeaway 1: everyone’s into data.

It’s nice to see how the entire music analytics sector has been evolving this year and growing in influence. All of a sudden everyone and their mother is interested in looking into numbers, percentages, and graphs to make more solid business decisions.

This means you, dear both readers, should be too. You can access ours HERE (pssst, there’s a 1-week free trial period on ANY account, so go wild), and if you’re releasing through Beatport, do also join a free webinar I’ll be hosting with UMEK next week to get some fresh ideas. Sign up here.

Takeaway 2: online conferences kick ass.

Turns out digital conferences can be even better than live ones from the educational point of view. People have less stage fright because staring at your webcam doesn’t even come close to standing on a stage of a huge amphitheater, sweating profusely and holding down your puke. (That’s what I’ve read people experience. But not me. Definitely not. No way. Well, maybe sometimes.)

Still, can’t wait until I’ll go and get my vaccine shot. I will probably run straight to the airport from the doctor’s office and yes, I WILL FRIGGIN’ CLAP WHEN THE DAMN PLANE LANDS!

| Viberate.com |Instagram| Facebook | Twitter | LinkedIn | Reddit |
OKR’s vs KPI — An Beginner’s Introduction
By: Lashmi Bai Ravindrapandian
Tags: Okr Vs Kpi, Objectives, Data Analytics, Strategic Planning, Okr

In simple terms, OKR’s — Defines the process along with key results & KPI — Measures the key results

2. Goals — OKR’s are Lead Goals/Indicators ; KPI’s are Lag Goals/Indicators

3. Outcomes — OKR’s looks forward at future outcomes & results whereas KPI’s looks back at whether the targeted results being achieved

4. For E.G, OKR’s- Increase of Sales by 15% with Increased Customer Satisfaction where as KPI’s — Increase of Sales by 15% or Achieve sales of 15%

5. Felipe Castro shares the below insights about the OKR vs KPI

6. OKR’s are objectives in creating sustainable change in performance whereas KPI’s are targets which you measure periodically, which makes Key Results.

7. OKR’s are driven from Top-Down whereas KPI’s are driven by Bottom-Up

8. OKR’s are defined Quarterly whereas KPI’s are defined Annually

9. OKR’s defines goals for the achievement of KPI’s whereas KPI’s defines goals to sustain the performance

If you want to grow, improve, sustain your business, you should use both in business. KPIs are an important component of OKRs. OKR’s can be applied to One’s Personal Goals as well

Hope you find it useful.

Happy reading.