Learning new software or skills for the betterment of your career has now become an essential process. This is for either gaining an edge over others or dealing with a new generation of team members. Cooperates require their employee to bring everything they have in their platter so that they know what they can do with their skills. They also require them to master new skills in no time so that can attain benefits from it. But, mastering a skill requires time and also correct guidance and approach towards it. There are numerous software available after offices have shifted to computers. Softwares that make work easier. To learn these software an employee has to be certified or go under on-the-job training. One such software is Tableau. Tableau is used by cooperates to scan large numbers of data and determine valuable information from it. Tableau has been in the market for decades and has clients like Amazon, Walmart, Adobe, and Cisco. It also has products like Desktop, Prep and Server that have helped its clients to decode data. To master such software takes time and luckily here is a list of five books that an analyst can read to achieve mastery in Tableau. So, let’s take a look at these books.

5 Must Read Books to Master Tableau

There are various books that claim to teach and guide analysts on how to use Tableau and decode even the most complex data structure in minutes. But, we have picked five of these books that are very good and have easy-to-understand language that may help an analyst to up their skill and also learn some new features of this amazing software. These books are best sellers and are widely read by analysts to understand the workings of Tableau. Let’s not waste much time and see these books.

Tableau Best Practices10.0 by Jenny Zhang

If you have used Tableau before then this book by Zhang is a good read as it has ample real-life problems that can help you learn new things about this software. This book helps if you spend most of your time data analyzing and visualizing. It also guides you on how to connect to a ton of variety of data from cloud or local servers and blend this data in a fast and efficient way and also perform complex calculations like LOD and Table calculations. The problems mentioned in the book also have a step-by-step guide given by Tableau experts. This book is very helpful for analysts who want to upgrade their skills in data analytics and also for data enthusiasts.

Learning Tableau 10 Second Edition by Joshua N. Milligan

This book by Joshua N. Milligan is also a good book for analysts. In this book, the author has made sure that he has written everything he knows about this software and also mentioned instructions related to the features. It has a dedicated guide from scratch that is how to make a pie chart, bar chart, and tree maps and also an installation guide to various tools that the software has to offer to its users. It also has detailed information on different techniques used to tackle different challenges. The book also deals with how to effectively use data for storytelling and also how to get insights from data that can help the business to flourish. This book is very helpful to learn how to manage data and also derive insightful information that can help make crucial decisions for business growth. This book is good for beginners and also advanced-level data analysts.

Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master by Ryan Sleeper

Ryan Sleeper is one of the most qualified Tableau consultants. In this book, he has given instructions about how Tableau works and has given numerous ways to derive insights from a large pile of data. This book is a good guide to understanding and working on Tableau. This book is as good as a manual for Tableau as it has everything an analyst should know while using Tableau and enjoy the full features of this software. It also has a step-by-step guide for every feature that is offered by Tableau for data analysis. This book also is a good read for people who want to become data analysts and want to learn this software and use it in the future.

Mastering Tableau by David Baldwin

David Baldwin is also a prolific writer who has written many books that have helped employees enhance their skills in business intelligence for almost 17 years. In this book, he has shared his experience while using Tableau. For this software, he has focused on Tableau training by shedding light on developing, BI solutions, Project management, technical writing, and web and graphic design. He has also written a detailed guide on the new features introduced by Tableau in its new version. i.e. 10.0. The features that are introduced in this version consist of creative use of different types of calculations like row-level, and aggregate-level, and how this software is able to solve complex data visualization challenges put to it. He also guides the reader about the tools offered by Tableau and helps them understand the tools of this software. The book has a systematic approach to training its reader to use Tableau as it starts from basic level training of features and then slowly moves towards advanced tools that include calculations, R integration parameters and sets and also data blending techniques.

Tableau 10: Business Intelligence Cookbook by Donabel Santos

This book is also a good pick for analysts and people who want to pursue a career in data analysis. This book also covers all practical cases but with a different approach. It has arranged cases from basic level to advanced level cases to make the readers understand each and every tool in Tableau and also ensure that the readers are getting practical experience too. The book also involves a step-by-step guide to creating basic and advanced charts and also an attempt to make the Tableau interface familiar to its readers. It also guides the readers on how to create effective dashboards and many other wonders about this software. As Santos itself is a data geek and has spent a lot of time around data she has tried to answer all the questions about Tableau in this book. She has also focused on the ratings of this book as the better the rating more it sells so this book is packed with some valuable tips and tricks that an analyst of any level can use and master this software. This book is very helpful to up your skills and learn new things about Tableau.

These are the top five books that are recommended to master Tableau in no time. But, reading and keeping it aside will not help as to master skills one needs to practice whatever they have learned and hone that skill with time. These books will give you information that you require but mastering Tableau is ultimately in your hands. If you keep practicing the tips and tricks given by these experts then you can master it and also get appreciation from your seniors and also have an edge over your peers. As one says perfect practice makes a man perfect. 

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Integrated Development Environments (IDEs):

The software suite that consolidates the combination of basic tools like code editor, code compiler, and code debugger is called an Integrated Development Environment. An IDE usually combines commonly used developer tools into a compact Graphical User Interface (GUI). An IDE can be a standalone application or it can be part of a larger package. The user writes and edits source code in the code editor. The compiler translates the source code into a readable language that is executable for a computer, and the debugger tests the software to solve any issues or bugs. 

The IDE choices reflect the pragmatism of the language as a whole. The Julia community has built powerful industry-established IDEs and there are a few that every developer needs to be experimental in their programming.

(Made with Canva)

Juno is a minimalistic yet potent open-source Integrated Development Environment (IDE) designed for Julia programming. It features an autocomplete capability, allowing it to suggest functions or variables as you type, which streamlines the coding process for both novices and seasoned professionals. This makes it an excellent tool for developing superior software more efficiently and achieving quicker outcomes. Additionally, Juno offers a unique hybrid canvas programming approach, blending the investigative flexibility of notebooks with the efficiency of traditional IDEs, thereby enhancing the programming experience.

Atom

Atom, renowned for its exceptional customizability, transforms into a formidable Integrated Development Environment (IDE) for Julia programming upon integrating the Juno package. This combination elevates Atom by incorporating Juno’s specialized enhancements designed explicitly for Julia development. Key features include inline evaluation, which allows for the execution of code snippets directly within the editor, providing immediate feedback and streamlining the development process. Additionally, Juno enriches Atom with seamlessly integrated documentation, offering instant access to comprehensive reference materials and function definitions. This synergy not only augments the functionality of Atom but also significantly boosts productivity and efficiency for developers working with Julia, catering to a wide range of programming needs from debugging to writing complex code structures.

While the Julia integration in Visual Studio Code may not match the comprehensive capabilities of Juno, it still delivers an excellent coding environment for those who choose it. Visual Studio Code supports Julia with a variety of helpful features, including syntax highlighting, code completion, on-hover tips, Julia code evaluation, linting, and code navigation tools. Moreover, Visual Studio Code is known for its responsive performance and lower system resource consumption compared to Atom. This makes it a particularly attractive choice for users working on less robust machines. Nonetheless, it’s worth noting that Atom has made significant strides in improving its performance and efficiency in its latest versions.

Pluto.jl distinguishes itself as an exceptionally interactive notebook environment tailored specifically for the Julia programming language. Designed with data scientists and researchers in mind, it excels in facilitating data exploration, allowing users to delve into datasets with ease, visualize data in dynamic and compelling ways, and construct interactive documents that bring data narratives to life. This environment supports real-time code evaluation, meaning changes in the code automatically update the outputs and visualizations, enhancing the interactive experience. Pluto.jl’s user-friendly interface and robust capabilities make it an ideal platform for those looking to experiment with data, develop complex visualizations, or share reproducible research findings in a more engaging and interactive manner.

IJulia serves as a vital bridge that connects the Julia programming language with the expansive Jupyter ecosystem, thereby expanding Julia’s reach and utility. By integrating IJulia, developers gain the ability to craft Jupyter notebooks specifically tailored for executing Julia code. This integration significantly enhances the capabilities of Jupyter notebooks, providing a robust platform for developers and data scientists to perform sophisticated data analysis and create compelling visualizations directly in Julia. It offers an intuitive, interactive environment for exploring datasets, testing algorithms, and sharing reproducible research findings, making it an indispensable tool for those working in data-driven fields.

The Julia programming language benefits from a highly supportive and active community, which plays a crucial role in its ongoing development and expansion. This vibrant community is not just a backbone for the language’s technical evolution but also serves as a dynamic support system for developers working with Julia. Individuals engaging with Julia find themselves in a collaborative environment, where expertise is freely shared, fostering a culture of learning and innovation. This extensive community involvement has enabled Julia to cater to a wide array of applications across different sectors, including finance, data science, and web development. As a result, developers utilizing Julia have the opportunity to become skilled across various domains, leveraging the language’s versatility and the community’s collective knowledge to tackle complex problems and innovate within their respective fields.

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Why Choose Graph Objects Over Plotly Express?

Plotly’s library is known for its prowess in creating interactive and exquisite plots, but when it comes to certain 3D trace-types like mesh or isosurface, Plotly Express falls short. This is where the graph objects module comes into play. Here’s why you should consider it:

1. Unleash the Power of 3D Visualization

With graph objects, you can dive into the world of 3D visualization without limitations. Unlike Plotly Express, which struggles with 3D trace-types, the graph objects module empowers you to create stunning 3D plots that elevate your data storytelling.

2. Keep Your Data Secure

When using Plotly Express, your data is uploaded to external servers for graphical representation. In contrast, graph objects ensure your data stays on your local machine, safeguarding it against potential security concerns.

3. Comprehensive Data Science Toolkit

If you’re on the lookout for a complete repository of Python libraries for data science, look no further. Graph objects offer a versatile toolkit that complements your data science arsenal, enabling you to tackle diverse visualization challenges with ease.

A Closer Look at the Data

Before we dive into the practical aspects, let’s acquaint ourselves with the dataset we’ll be working with. The data at hand revolves around the end-of-day Nifty 50 stock prices, featuring 13 essential features related to these stocks. This dataset has been sourced from Kaggle, and here’s a breakdown of the features:

• Symbol: The stock’s name.
• Open: The opening price when the market commenced trading.
• High: The highest recorded price within the day.
• Low: The lowest recorded price within the day.
• LTP: Last Traded Price – the price at which the last transaction occurred.
• Chng: The amount of change in the stock price.
• % Chng: The percentage change in the stock price.
• Volume: The total trading volume for the day.
• Turnover: The total turnover of the company.
• 52w H: The highest price the stock has traded at over the past 52 weeks.
• 52w L: The lowest price the stock has traded at over the past 52 weeks.
• 365d % Chng: The percentage change in the stock’s price over the past 365 days (1 year).
• 30d % Chng: The percentage change in the stock’s price over the past 30 days (1 month).

Visualization with Plotly Graph Objects

Importing Libraries

Let’s begin our data visualization journey by importing the necessary libraries:

import pandas as pd import numpy as np import plotly.graph_objects as go

Reading and Preprocessing the Dataset

Next, we need to read and preprocess the dataset. This involves removing commas from certain values and converting all relevant values to float for seamless data visualization.

df = pd.read_csv('Nifty50.csv') df[['52w H', '52w L', 'Open', 'High', 'Low']] = df[['52w H', '52w L', 'Open', 'High', 'Low']].replace(",", "", regex=True) df[['LTP', 'Turnover (crs.)']] = df[['LTP', 'Turnover (crs.)']].replace(",", "", regex=True) df[['52w H', '52w L', 'Open', 'High', 'Low', 'LTP', 'Turnover (crs.)']] = df[['52w H', '52w L', 'Open', 'High', 'Low', 'LTP', 'Turnover (crs.)']].astype(float) df.head()

Bar Plot

Now, let’s create a bar plot to visualize the total volume of stock trading. We’ll follow these steps:

Step 1: Define a blank figure using the go.Figure() class and store it in a variable called “fig.”

fig = go.Figure()

Step 2: Add a trace to the blank figure using the add_trace() class. Inside the trace, specify the plot details, including the x-axis, y-axis, plot name, and other parameters.

fig.add_trace(go.Bar(x=df['Symbol'], y=df['Volume (lacs)'], name='Total volume', visible=True ))

Step 3: Display the plot by calling the variable in which the figure was stored and using the show() function.

fig.show()

Enhancing the Bar Plot

The initial plot lacks some essential elements like a grid format background, x-axis label, y-axis label, and a title. Let’s enhance the plot by updating its layout using the update_layout() class.

Step 4: Use the update_layout() class to update various aspects of the plot, including legend visibility, plot background color, font settings, axis labels, and the title.

fig.update_layout(showlegend=False, plot_bgcolor='rgba(0,0,0,0)', font=dict(family='Arial', size=12, color='black'), xaxis=dict(showgrid=False, tickangle=-45, categoryorder='total descending', title_text='Name of stocks'), yaxis=dict(title_text='Total amount of stocks traded'), title=dict(text='Total volume of stock EOD', font=dict(family='Arial', size=18, color='black'), x=0.5, y=0.9, xanchor='center', yanchor='top'), )

With these updates, our final code for the bar plot looks like this:

fig = go.Figure() fig.add_trace(go.Bar(x=df['Symbol'], y=df['Volume (lacs)'], name='Total volume', visible=True )) fig.update_layout(showlegend=False, plot_bgcolor='rgba(0,0,0,0)', font=dict(family='Arial', size=12, color='black'), xaxis=dict(showgrid=False, tickangle=-45, categoryorder='total descending', title_text='Name of stocks'), yaxis=dict(title_text='Total amount of stocks traded'), title=dict(text='Total volume of stock EOD', font=dict(family='Arial', size=18,

color='black'), x=0.5, y=0.9, xanchor='center', yanchor='top'), ) fig.show()

With these updates, our bar plot is now visually appealing, sorted in descending order, and includes essential elements like axis labels and a title.

Scatter Plot

Now, let’s explore scatter plots to visualize the variation between the highest and lowest stock prices at the end of the day.

fig = go.Figure() fig.add_trace(go.Scatter(x=df['Symbol'], y=df['52w H'], mode='lines+markers', name='High')) fig.add_trace(go.Scatter(x=df['Symbol'], y=df['52w L'], mode='lines+markers', name='Low')) fig.update_layout(showlegend=True, plot_bgcolor='rgba(0,0,0,0)', font=dict(family='Arial', size=12, color='black'), xaxis=dict(showgrid=False, tickangle=-45, title_text='Name of stocks'), yaxis=dict(title_text='Price of stocks'), title=dict(text='Variation between highest and lowest stock price', font=dict(family='Arial', size=18, color='black'), x=0.5, y=0.9, xanchor='center', yanchor='top'), ) fig.show()

This scatter plot provides a clear picture of how stock prices vary between their highest and lowest points at the end of the day. It includes markers and lines for enhanced data representation.

Pie Chart

Now, let’s create a pie chart to visualize the percentage of annual change in stock prices.

fig = go.Figure() fig.add_trace(go.Pie(labels=df['Symbol'], values=df['365 d % chng'], name="Change in year")) fig.update_traces(textposition='inside') fig.update_layout(uniformtext_minsize=12, uniformtext_mode='hide', title=dict(text='Percentage of annual change in stock price', font=dict(family='Arial', size=18, color='black'), x=0.5, y=0.9, xanchor='center', yanchor='top')) fig.show()

This pie chart provides a visual representation of how stock prices have changed over the course of a year, making it easy to identify trends and outliers.

Subplots

Lastly, let’s create subplots to display both the scatter plot of annual high and low stock prices and the bar plot of the percentage of annual change in stock prices. This allows us to compare these two aspects in a single view.

import plotly.subplots as splt fig_sub = splt.make_subplots(rows=2, cols=1, row_heights=[0.7,0.9], subplot_titles=("Annual High and Low for stocks", "Percentage of annual change in stocks")) fig_sub.add_trace(go.Scatter(x=df['Symbol'], y=df['52w H'], mode='lines+markers', name='High'), row=1, col=1) fig_sub.add_trace(go.Scatter(x=df['Symbol'], y=df['52w L'], mode='lines+markers', name='Low'),row=1, col=1) fig_sub.add_trace(go.Bar(x=df['Symbol'], y=df['365 d % chng'], name ='% change/annum', visible=True),row=2, col=1 ) fig_sub.update_xaxes(tickangle=-45, row=1, col=1) fig_sub.update_xaxes(tickangle=-45, row=2, col=1) fig_sub.update_layout(showlegend=True, plot_bgcolor='rgba(0,0,0,0)', font=dict(family='Arial', size=12, color='black'), title=dict(text='Annual variation in stocks', font=dict(family='Arial', size=18, color='red'), x=0.5, y=0.9, xanchor='center', yanchor='top'), height=550 ) fig_sub.show()

With these subplots, we can simultaneously visualize the annual high and low stock prices alongside the percentage of annual changes, offering a comprehensive view of stock performance.

Final Verdict

In conclusion, the graph objects module forms the backbone of every graph produced by Plotly. In this article, we’ve not only explored when to use graph objects but also delved into their implementation, enabling you to create interactive and captivating visuals from your data.

The post Unleash the Power of 3D Visualization with Plotly’s Graph Objects first appeared on AITechTrend.

1. 10 Questions on Banking
2. 10 Questions on Model Development and Validation
3. 10 Questions on Time Series

Let’s dive in and explore the world of credit risk analytics!

10 Questions on Banking

What are the 3 Pillars in Basel Framework?

The Basel Framework consists of three pillars that aim to ensure the stability and soundness of the banking system. These pillars are:

1. Minimum Capital Requirement: Calculated based on the risk under various heads. For credit risk, the approaches are Standardized, F-IRB, and A-IRB. For market risk, the approach is VaR. For operational risk, the approaches are Basic Indicator Approach, Standardized Approach, and Internal Measurement Approach.
2. Supervisory Review: It is based on the Internal Capital Adequacy Assessment Plan. This pillar gives banks the power to review their risk management system and make necessary improvements.
3. Market Discipline: Developing a set of disclosure requirements that require institutions to disclose details such as scope of application, capital, risk exposures, risk assessment process, and capital adequacy of the institution. This pillar enhances transparency and accountability.

What are the approaches for the treatment of impaired provisions?

When it comes to impaired provisions, there are three main approaches:

1. Standardized Approach: Regulators prescribe the risk weight. If a loss has occurred, it impacts Tier 1 capital.
2. Foundation IRB Approach: Banks estimate the 1-year Probability of Default (PD), while regulators prescribe Loss Given Default (LGD) and Exposure at Default (EAD). If Expected Loss (EL) exceeds the provision, the excess is reduced from capital. If EL is less than the provision, the excess is added to capital.
3. Advanced IRB Approach: Banks estimate PD, LGD, and EAD based on their internal models.

What is ICAAP?

ICAAP stands for Internal Capital Adequacy Assessment Plan. It is a process through which banks inform their board of directors about the ongoing assessment of the bank’s risk and how the bank intends to mitigate those risks. The purpose of ICAAP is to assess the current and future capital requirements of the bank.

What is Capital?

Capital serves as a buffer to absorb unexpected losses and fund the ongoing activities of the bank. In the Basel Framework, capital is categorized into two tiers:

1. Tier 1 Capital: Also known as core capital, Tier 1 capital includes shareholder’s equity and retained earnings. The minimum Tier 1 capital requirement is 6% of Risk-Weighted Assets (RWAs).
2. Tier 2 Capital: Also called supplementary capital, Tier 2 capital includes items such as revaluation reserves, hybrid capital instruments, subordinate term debt, general loan loss reserves, undisclosed reserves, etc. The minimum Tier 1 + Tier 2 capital requirement is 8% of RWAs. The minimum capital adequacy ratio, including the capital conservation buffer, is 10.5% of RWAs.

What are the key Capital Ratios?

There are three key capital ratios used to measure a bank’s capital adequacy:

1. Common Equity Tier 1 (CET1) Ratio: It represents the common equity under the revised capital framework divided by the Standardized Approach to RWAs.
2. Tier 1 Ratio: It represents the Tier 1 capital under the revised capital framework divided by the Standardized Approach to RWAs.
3. Total Capital Ratio: It represents the total capital under the revised capital framework divided by the Standardized Approach to RWAs.

What is expected loss and unexpected loss?

In credit risk analytics, two important concepts are expected loss and unexpected loss:

1. Expected Loss (Provision): Expected loss is the sum of the values of all possible losses, each multiplied by the probability of that loss occurring. It is calculated as Expected Loss (EL) = Probability of Default (PD) x Exposure at Default (EAD) x Loss Given Default (LGD).
2. Unexpected Loss (Capital): Unexpected loss refers to losses beyond the expected loss. It is calculated as Unexpected Loss (UL) = EAD x SQRT[(PD^2 x σ^2LGD) + (LGD^2 x σ^2PD)]. Banks are required to hold capital to cover unforeseen financial losses.

What is IFRS9?

IFRS9 stands for International Financial Reporting Standard 9. It is an accounting standard issued by the International Accounting Standards Board (IASB) for the recognition of impairments. IFRS9 introduces a forward-looking approach to determine credit impairments.

Under IFRS9, impairments are classified into three stages:

1. Stage 1: This stage applies to loans that are originated or existing loans with no significant increase in credit risk. Expected Credit Loss (ECL) resulting from default events in the next 12 months is recognized.
2. Stage 2: This stage applies if the credit risk has increased significantly and is not considered low. Lifetime ECL is recognized.
3. Stage 3: This stage applies when the credit risk increases to a point where it is considered credit impaired. Lifetime ECL is recognized.

IFRS9 has implications such as earlier recognition of losses, differentiation of exposures based on deterioration, and the requirement of loss forecasting.

What is CECL?

CECL stands for Current Expected Credit Loss. It is an accounting standard issued by the Financial Accounting Standards Board (FASB) for the recognition of impairments. CECL and IFRS9 are similar in principle but have some differences.

The key difference between CECL and IFRS9 is the estimation of lifetime losses:

1. CECL: CECL estimates lifetime losses upon the initial recognition of assets.
2. IFRS9: IFRS9 requires 12-month ECL for performing loans and lifetime ECL for under-performing or non-performing loans.

What is CCAR?

CCAR stands for Comprehensive Capital Analysis and Review. It is an assessment conducted by the Federal Reserve to ensure that large systematically important banking institutions have a forward-looking, institution-specific, risk-tailored capital planning process. The objective of CCAR is to assure that banks have sufficient funds to remain solvent during times of economic and financial distress.

The CCAR process involves stress testing and sensitivity analysis to evaluate the resilience of banks under various scenarios. The Federal Reserve provides supervisory scenarios, and banks also create internal scenarios known as Bank Holding Company (BHC) scenarios.

What is Stress Testing and Sensitivity Analysis?

Stress testing and sensitivity analysis are essential components of the CCAR process. They involve assessing the impact of adverse economic conditions on the financial stability of banks. Stress testing involves subjecting banks’ balance sheets to various hypothetical scenarios, such as economic downturns or market shocks, to evaluate their resilience and ability to withstand adverse conditions.

There are three scenarios provided by the Federal Reserve (supervisory scenarios): Base, Adverse, and Severely Adverse. These scenarios forecast the performance of banks’ balance sheets and financial indicators over a period of nine quarters.

Sensitivity analysis, on the other hand, focuses on measuring the sensitivity of banks’ default exposures to changes in key risk factors. It calculates sensitivity ratios such as the adverse sensitivity and severely adverse sensitivity, which compare default exposures under different scenarios to the baseline scenario.

Both stress testing and sensitivity analysis play a crucial role in identifying potential vulnerabilities in banks’ capital adequacy and risk management practices.

10 Questions on Model Development and Validation

What is Probability of Default (PD)?

Probability of Default (PD) is a key concept in credit risk modeling. It represents the average number of obligors that default in a particular rating grade within a year. PD is estimated using statistical techniques such as logistic regression models. In these models, the outcome variable is dichotomous, indicating whether an obligor will default or not based on various independent variables.

Some of the dependent variables commonly used in PD estimation include current non-payment, historical non-payment, percentage of payment, credit limit utilization, maturity, and other relevant factors.

What is Exposure at Default (EAD)?

Exposure at Default (EAD) is an estimate of the outstanding amount a bank is exposed to in case an obligor defaults. EAD is highly relevant in revolving credit facilities such as credit cards or lines of credit. It focuses on metrics that measure the increase in balances between a reference time and the date of default.

EAD is estimated through methodologies such as the Exposure at Default Formula (EADF), which calculates EAD as the balance at default divided by the balance at the reference date.

What is Loss Given Default (LGD)?

Loss Given Default (LGD) is the percentage of exposure that a bank might lose if an obligor defaults. LGD depends on the characteristics of the loan and the collateral held by the bank.

For example, in the case of mortgages, the collateral (property) determines the potential recovery in the event of default. In contrast, for credit cards, where there is no specific collateral, the recovery is based on factors such as the cash flow generated post-default.

LGD is empirically derived and can be calculated as 1 minus the sum of payments for a specific period divided by the maximum of balances at different time intervals.

What is the difference between Through the Cycle (TtC) and Point in Time (PiT) PD?

Through the Cycle (TtC) PD and Point in Time (PiT) PD are two different approaches to modeling and estimating credit risk:

• Through the Cycle (TtC) PD: TtC PD takes a longer-term perspective into consideration and aims to capture the average default behavior of obligors over economic cycles. It is more stable and less prone to short-term fluctuations in economic conditions.
• Point in Time (PiT) PD: PiT PD focuses on capturing the current credit risk of obligors based on their specific characteristics and the prevailing macroeconomic conditions. It aligns with recent macroeconomic scenarios and provides a more granular assessment of credit risk.

Both TtC PD and PiT PD have their respective advantages and applications in credit risk modeling. The choice of approach depends on the specific requirements and objectives of the modeling exercise.

What is Information Value (IV)?

Information Value (IV) is a widely used concept in variable selection during model development. It plays a crucial role in credit risk modeling, particularly in the credit card industry.

IV measures the strength of the relationship between a predictor variable and the target variable (default or non-default). It is calculated as the sum of the differences between the distribution of defaults and non-defaults multiplied by the Weight of Evidence (WoE).

WoE is the logarithm of the ratio of the distribution of non-defaults to defaults for a specific category or bin of a predictor variable. The higher the IV, the greater the explanatory power of the variable in predicting the target variable.

What is Population Stability Index (PSI)?

Population Stability Index (PSI) is a metric used to monitor the stability of a population or data distribution over time. It is particularly relevant in credit risk modeling when assessing the performance and stability of predictive models.

PSI is calculated by comparing the differences between the actual and estimated distributions of a specific variable. It measures the extent to which changes in economic conditions or internal policy changes affect the population or data distribution.

A PSI below 0.1 indicates no significant change, between 0.1 and 0.25 suggests the need for close monitoring, and above 0.25 indicates the need to redevelop the model to account for changes in the underlying population.

What is a Confusion Matrix?

A Confusion Matrix is a square matrix that summarizes the performance of a classification model. It is commonly used in binary classification problems, where the outcome can be either positive or negative.

The Confusion Matrix consists of four cells:

• True Positive (TP): The model correctly predicted the positive cases (e.g., default) as positive.
• True Negative (TN): The model correctly predicted the negative cases (e.g., non-default) as negative.
• False Positive (FP): The model incorrectly predicted the negative cases as positive (Type I error).
• False Negative (FN): The model incorrectly predicted the positive cases as negative (Type II error).

The Confusion Matrix provides valuable insights into the performance metrics such as accuracy, precision, recall (sensitivity), and specificity, which measure the model’s effectiveness in correctly predicting the positive and negative cases.

What is Concordance?

Concordance is a concept used in the evaluation of predictive models, particularly in credit risk modeling. It measures the rank ordering of the predicted probabilities.

A pair of observations is considered concordant if the observation with the desired outcome (e.g., event) has a higher predicted probability than the observation without the desired outcome (e.g., non-event). A pair is discordant if the opposite is true, and a pair is tied if both observations have the same predicted probability.

Concordance assesses the ability of a model to correctly rank the likelihood of events or outcomes. It is particularly useful in applications such as credit scoring, where the ranking of individuals based on their creditworthiness is critical.

What is a Gain and Lift Chart?

A Gain and Lift Chart is a visual representation of the performance of a predictive model. It is used to evaluate the rank ordering of predictions and assess the model’s effectiveness in identifying positive cases (e.g., defaults).

The Gain Chart shows the percentage of positive cases (events) covered at each decile level. It helps to identify the proportion of targets captured by the model at different levels of prediction.

The Lift Chart, also known as the Lift Curve, measures the ratio of the model’s performance to random expectation. It is calculated by dividing the gain percentage at each decile level by the random expectation percentage.

Gain and Lift Charts provide insights into the model’s ability to prioritize the most significant cases and identify high-risk individuals or events.

What is KS, AUROC, and Gini?

KS (Kolmogorov-Smirnov), AUROC (Area Under the ReceiverOperating Characteristic curve), and Gini coefficient are commonly used metrics to evaluate the performance of classification models, including credit risk models.

• KS (Kolmogorov-Smirnov): The KS statistic measures the separation power of a classification model. It calculates the maximum absolute difference between the cumulative distribution functions of the positive and negative cases. A higher KS value indicates better discrimination power of the model. Typically, a KS value above 30 is considered excellent.
• AUROC (Area Under the Receiver Operating Characteristic curve): AUROC is a fundamental tool for evaluating the performance of diagnostic tests, including credit risk models. The ROC curve plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various classification thresholds. The AUROC represents the area under this curve. An ideal model will have an AUROC close to 1, indicating high discriminative power.
• Gini coefficient: The Gini coefficient is derived from the AUROC and provides a single-value summary of the model’s discrimination power. It measures the ratio between the area between the ROC curve and the diagonal line (random model) and the area above the diagonal. The Gini coefficient can range from 0 to 1, with 1 indicating perfect discrimination and 0 indicating no discrimination.

These metrics help assess the effectiveness of credit risk models in differentiating between positive and negative cases, and provide valuable insights for model selection and performance evaluation.

By thoroughly understanding the concepts and answering these questions, you will be well-prepared for a risk analytics interview. Remember to adapt your responses to your specific experience and expertise. Good luck!

The post 30 Risk Analytics Interview Questions first appeared on AITechTrend.

As businesses and organizations continue to rely on data to drive decision-making, two terms that often come up are “analytics” and “statistics.” While both are related to data analysis, they have distinct differences in their approach, scope, and applications. In this article, we will delve into the disparities between analytics and statistics, exploring their definitions, types, applications, and key differences.

Introduction

In today’s data-driven world, businesses and organizations are constantly seeking insights from data to gain a competitive edge. Analytics and statistics are two methodologies that help in extracting valuable information from data, but they have different approaches and purposes. Understanding the differences between analytics and statistics can help businesses and data professionals make informed decisions about which approach to use for different data analysis tasks.

Definition of Analytics

Analytics is the process of examining, cleaning, transforming, and modeling data to extract insights, draw conclusions, and support decision-making. It involves the use of various tools, techniques, and algorithms to analyze data and discover patterns, trends, and relationships. Analytics can be descriptive, diagnostic, predictive, or prescriptive, depending on the type of analysis being performed. Descriptive analytics focuses on summarizing and understanding historical data, diagnostic analytics aims to identify the causes of past events, predictive analytics uses statistical models to forecast future outcomes, and prescriptive analytics suggests optimal solutions based on available data.

Types of Analytics

There are several types of analytics, each serving a specific purpose. Some common types of analytics include:

1. Descriptive Analytics: Descriptive analytics involves analyzing historical data to gain insights into past performance and understand trends and patterns. It answers questions such as “What happened?” and “Why did it happen?”
2. Diagnostic Analytics: Diagnostic analytics aims to identify the causes of past events or problems by analyzing data. It answers questions such as “Why did it happen?” and “What were the factors that led to it?”
3. Predictive Analytics: Predictive analytics uses statistical models to forecast future outcomes based on historical data. It answers questions such as “What is likely to happen in the future?” and “What are the possible outcomes of a particular decision or action?”
4. Prescriptive Analytics: Prescriptive analytics suggests optimal solutions based on available data and helps in decision-making. It answers questions such as “What should be done?” and “What actions should be taken to achieve a particular goal?”

Importance of Analytics

Analytics is crucial for businesses and organizations as it enables data-driven decision-making, identifies opportunities, uncovers insights, and improves performance. By analyzing data, businesses can gain a deeper understanding of their customers, markets, and operations, optimize resources, mitigate risks, and gain a competitive advantage. Analytics is widely used in various industries, including finance, healthcare, marketing, sports, and e-commerce, to name a few.

Definition of Statistics

Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It involves the use of mathematical techniques to summarize and analyze data, make inferences, and draw conclusions. Statistics can be descriptive or inferential, depending on the type of analysis being conducted. Descriptive statistics focuses on summarizing and describing data, while inferential statistics involves making inferences and drawing conclusions about a population based on a sample.

Types of Statistics

Statistics can be broadly categorized into two main types: descriptive statistics and inferential statistics.

1. Descriptive Statistics: Descriptive statistics involves the analysis and summary of data to describe its main features, such as measures of central tendency (e.g., mean, median, mode), measures of dispersion (e.g., range, variance, standard deviation), and graphical representations (e.g., histograms, bar charts, pie charts). Descriptive statistics provide a snapshot of the data and help in understanding its characteristics and trends.
2. Inferential Statistics: Inferential statistics involves making inferences and drawing conclusions about a population based on a sample. It uses probability theory and statistical techniques such as hypothesis testing, confidence intervals, and regression analysis to make predictions, estimate parameters, and test hypotheses. Inferential statistics allows researchers to make generalizations about a population based on a smaller subset of data.

Applications of Statistics

Statistics has a wide range of applications in various fields, including business, healthcare, social sciences, sports, and more. Some common applications of statistics include:

1. Business: Statistics is used in business to analyze sales data, customer behavior, market trends, and financial performance. It helps in making data-driven decisions, optimizing resources, and predicting future outcomes.
2. Healthcare: Statistics is used in healthcare to analyze patient data, conduct clinical trials, and study disease patterns. It helps in evaluating treatment effectiveness, identifying risk factors, and making evidence-based decisions.
3. Social Sciences: Statistics is used in social sciences to conduct surveys, analyze social data, and study human behavior. It helps in understanding social trends, measuring attitudes, and making policy decisions.
4. Sports: Statistics is used in sports to analyze player performance, game outcomes, and team dynamics. It helps in strategizing, scouting, and evaluating player effectiveness.

Key Differences between Analytics and Statistics

While analytics and statistics share similarities in terms of data analysis, they have key differences in their approach, scope, and applications.

1. Approach: Analytics focuses on examining data to gain insights, draw conclusions, and support decision-making. It involves the use of various tools, techniques, and algorithms to analyze data and discover patterns, trends, and relationships. On the other hand, statistics involves the collection, analysis, interpretation, presentation, and organization of data using mathematical techniques to summarize and analyze data, make inferences, and draw conclusions.
2. Scope: Analytics is a broader field that encompasses various types of data analysis, such as descriptive, diagnostic, predictive, and prescriptive analytics. It can involve both qualitative and quantitative data and can be applied to various domains, including business, healthcare, marketing, and more. Statistics, on the other hand, is a branch of mathematics that focuses on the analysis of data using statistical techniques and is primarily concerned with quantitative data.
3. Applications: Analytics is widely used in business, finance, healthcare, marketing, and other industries for decision-making, optimization, and gaining a competitive advantage. It is also used in fields such as sports, e-commerce, and social sciences, among others. On the other hand, statistics has applications in various fields, including business, healthcare, social sciences, sports, and more, and is used for data analysis, making inferences, and drawing conclusions.
4. Emphasis: Analytics emphasizes the use of advanced tools, techniques, and algorithms to analyze data and discover insights. It often involves the use of machine learning, artificial intelligence, and big data technologies to process and analyze large volumes of data. Statistics, on the other hand, emphasizes the use of mathematical techniques, probability theory, and statistical methods to analyze data and draw conclusions. It focuses on the mathematical foundations of data analysis and inference.

Conclusion

In conclusion, while analytics and statistics share similarities in terms of data analysis, they have key differences in their approach, scope, and applications. Analytics focuses on using various tools and techniques to gain insights and support decision-making, while statistics involves the use of mathematical techniques to analyze data and make inferences. Both fields have wide-ranging applications in various industries and fields, including business, healthcare, social sciences, sports, and more. Understanding the differences between analytics and statistics is essential for professionals working in data-driven fields to effectively analyze data, draw meaningful conclusions, and make informed decisions.

The post Beyond Numbers: Key Differences Between Analytics and Statistics first appeared on AITechTrend.

Neo4j^®, the world’s leading graph data platform, crossed $100 million in annual recurring revenue (ARR) during 2021. The year was marked by strategic product innovation that drove customer and partner excellence, strong community engagement, and super-sized venture funding investments.

“Neo4j has pioneered the graph space for a number of years, with critical deployments among major credit card firms for fraud detection, as well as use cases in areas driven by the pandemic, including product testing and supply chain analysis,” said Carl Olofson, Research Vice President at IDC.

Neo4j continued to grow in popularity throughout 2021 as the world’s most widely deployed graph database, maintaining its position as a top 20 database overall. Momentum drivers include the accelerated adoption of Neo4j AuraDB, a fully managed service that reduces friction as complex applications shift to the cloud, as well as the success of Neo4j Graph Data Science, a complete toolset for data scientists to apply graph algorithms for more effective machine learning and better predictions.

Over 1,000 organizations depend on Neo4j for mission-critical applications, and many thousands more experiment, prototype, and deploy Neo4j’s expanding portfolio of cloud services. Notable customers include Pfizer, PepsiCo, Inc., World Health Organization (WHO), Cable News Network, Inc. (CNN), and BMW Group.

Neo4j’s success in helping customers across industries such as Financial Services, Retail, and Healthcare caught the attention of investors, leading to $390 million in new investments raised in 2021, and launching Neo4j to a $2 billion valuation. On top of being the largest single funding round to date in the database space, Neo4j also welcomed GV (formerly Google Ventures) as a strategic investor and added former Google CFO, Patrick Pichette, to its board to offer increased industry expertise for the next phase of growth.

Patrick Pichette, Inovia Capital Partner and Neo4j Board Member, touched upon Neo4j’s momentum over the past year.

“2021 marked an incredible year for Neo4j and graph technology at large,” said Pichette. “What really sets Neo4j’s graph technology apart is that it uniquely solves some of the world’s most complex challenges. Neo4j is poised for strong, consistent growth leading into 2022, and we’re excited to be part of that journey.”

Emil Eifrem, CEO and Co-Founder of Neo4j, reflected on the past year and leading one of only a handful of private database companies to cross $100 million in ARR.

“In 2021, we demonstrated that Neo4j is a mainstay of modern data infrastructure, grounded in a global community of developers and data scientists, empowered with a rich portfolio of technology to address complex challenges, and scale without barriers,” said Eifrem. “We enter 2022 with the wind at our backs, and the right talent and leadership in place. We’re poised to deliver Neo4j to a fast-growing user base, and continue to delight our customers as their use cases become more exacting.”

The company ended 2021 with over 600 employees, representing the largest collective of graph expertise in the world. During the course of the year, Neo4j expanded rapidly in Asia-Pacific (Shanghai, Singapore, Sydney, Jakarta, and Bangalore), and Latin America (São Paulo).

Notable Neo4j 2021 milestones include:

Technology Leadership

• Breaking the Graph Scale Barrier: As part of NODES 2021, Neo4j demonstrated its super-scaling technology to show real-time query performance against a graph with over 200 billion nodes and more than a trillion relationships, running on over one thousand machines.
• Graphs and AI: Neo4j Graph Data Science was adopted by over 50 customers to build sophisticated AI, machine learning, and advanced analytics applications.
• AuraDB Enterprise: The most deployed and trusted graph technology platform was made generally available as a fully managed service, helping organizations including Levi Strauss & Co. and Adeo to radically accelerate time to value and get to production faster.
• Knowledge Graphs Accelerate Adoption: Two-thirds of Neo4j customers – including NASA – are implementing knowledge graphs to redefine what’s possible in data management and analytics.

Demonstrable Customer Value

• Unsurpassed ROI: The Neo4j Graph Data Platform pays for itself more than 4x in the span of three years (417% ROI), according to a recent Forrester TEI report.
• Accelerated Time to Value: According to Forrester, Neo4j showed 60% accelerated time to value, as average development time shrunk from 12 months to four.
• Digital Transformation: The TEI study was based on Forrester’s in-depth interviews with Neo4j customers who realized substantial cost savings from IT modernization and rationalization.

Commercial Impact

• Neo4j on Azure, GCP, and AWS: Neo4j is now globally available on Microsoft Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS) marketplaces. Customers can now seamlessly deploy Neo4j on the cloud platform of their choice.
• New Executives and Board Members: Neo4j welcomed Kristin Thornby as Chief People Officer. Nathalie Kornhoff-Bruls of Eurazeo and Patrick Pichette of Inovia Capital both joined Neo4j’s board.
• Partner Traction: Neo4j trained and certified over 1,000 graph practitioners from leading global system integrators including Accenture, Deloitte, EY, Capgemini, and PwC, in addition to closing new business with nine U.S. Federal Programs. The company expanded its partner leadership in emerging markets including Brazil, China, India, and Australia.

Market Expansion

• Top 20 Database: Neo4j is the leading graph database and ranks among the 20 most popular databases globally, as tracked by DB-Engines, an independent ranking of all database management systems.
• Award Wins: Neo4j was named one of CRN’s Big Data 100 Coolest Database System Companies, 2021 Data Breakthrough Awards Graph DB Solution of the Year, and 2021 Datanami Readers’ and Editors’ Choice Awards for Best Data and AI Technology.
• Rise in Cybersecurity Graph Adoption: Companies like Deloitte and Uplevel Security increasingly sought to leverage graph databases to strengthen cybersecurity strategies in response to increasingly sophisticated threats. Graphs equip cybersecurity teams with the competitive advantage of rapid screening, improved turnaround time, quality of recommendations, and reduced false positives, ultimately allowing them to mitigate cyber threats.
• Inclusion in U.S. Department of Defense (DoD) Iron Bank: Neo4j collaborated with The DoD’s Platform One organization to develop a hardened container for its graph database technology and joins a repository of digitally signed, binary container images that have been hardened according to the Iron Bank’s Container Hardening Guide.

Community Engagement

• Growing Developer Base: The global Neo4j community surpassed 240,000 members over the last year. During 2021, developers downloaded Neo4j more than 36 million times and launched more than 150,000 Neo4j Sandbox instances. Upwards of 53,000 professionals list Neo4j as a skill on their LinkedIn profiles.
• The Pandora Papers: The International Consortium of Investigative Journalists (ICIJ) released the Pandora Papers, which used Neo4j to generate visualizations and make searchable records of the hidden riches of world leaders. Neo4j has been working with the ICIJ since the 2016 Panama Papers investigation.
• Graphs4Good: The efforts of the Neo4j community to collaborate and help fight against the spread of COVID-19 were recognized by two honorable mentions in the AI and Data and Software categories of Fast Company’s 2021 World Changing Ideas Awards.
• Largest Graph Event: Neo4j Online Developer Expo and Summit (NODES 2021) welcomed over 12,000 registrants to listen to presentations from Fujitsu Research Labs, Dataiku, BASF, Apiax, Linkurious, and more.
• 2021 Graphie Award Winners: This year’s nominations eclipsed all prior years, with Neo4j receiving nominations spanning more than 10 countries and awarding 27 winners including Pfizer, Qualicorp S.A., Commonwealth Bank of Australia, Lenovo, Volvo Cars, Levi Strauss & Co., and many more.

 Resources

• Neo4j ROI
• Neo4j Products
• Neo4j Customers
• Neo4j Community
• Neo4j on Twitter
• Neo4j on LinkedIn
• Neo4j on YouTube
• Neo4j is Hiring

About Neo4j
Neo4j is the world’s leading graph data platform. We help organizations – including Comcast, ICIJ, NASA, UBS, and Volvo Cars – capture the rich context of the real world that exists in their data to solve challenges of any size and scale. Our customers transform their industries by curbing financial fraud and cyber crime, optimizing global networks, accelerating breakthrough research, and providing better recommendations. Neo4j delivers real-time transaction processing, advanced AI/ML, intuitive data visualization and more. Find out more at neo4j.com and follow us at @Neo4j.

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Enterprise analytics solutions can be stand-alone information systems or can be provided with solutions for data mining, business intelligence, web analytics, and big data analytics.

What is Enterprise Analytics?

A unified, integrated set of decision-making tools and processes that addresses the entire business problem, rather than just one particular area, and employs the use of the complete spectrum of human and automated capabilities to maximize the performance of all decisions that affect the business.

The enterprise analytics market has been segmented into end-user, component, application, and vertical. Based on end-user, the market is segmented into media and entertainment, BFSI, retail and consumer goods, manufacturing, hospitality, and gaming. Similarly, based on components, the market is segmented into software and service.

Why is Enterprise Analytics Important?

Today, your business’s entire lifecycle is growing exponentially and there’s also been an explosion of the volume of data in organizations, ranging from desktop to the server room to the different places in between. There are various factors that are contributing to the growth of the amount of data that is currently being generated in organizations, the major one being the growth of the Internet and the growing usage of smartphones. With this large amount of data coming from various sources, it is a challenge for organizations to deal with it efficiently. Enterprise Analytics enables an organization to conduct real-time analysis of this data. In other words, enterprises can generate actionable insights from this huge amount of data.

The Importance of Data Quality in Enterprise Analytics

Data quality is a critical concern when an organization is using big data analytics software and deploying applications. One of the main concerns when dealing with big data is to guarantee the accuracy of the data. Thus, there are three major aspects of data quality. First, the data should be accurate or near accurate. This means that a particular data value should be predicted based on an associated prediction metric. The accuracy or precision of prediction depends upon the performance of the prediction metric. Second, the accuracy or precision of the prediction depends upon the quality of the prediction metric. The performance of the prediction metric should be good enough to maximize predictive power.

Benefits of Data Analytics in Business

Conclusion

The rise in popularity of various technologies and their increasing affordability has allowed enterprises to focus more on their business goals and not on the information technology (IT) infrastructure. With this knowledge in hand, IT professionals can easily identify which particular technology to invest in for improving their organization’s performance. Enterprise analytics is one such technological development. This is not to say that the companies that are lagging in analytics implementations are unable to offer a complete spectrum of software and services, but they certainly lack in their ability to leverage their data to derive value from their business processes and operations.

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Our artificial intelligence services turn data into useful information. With personalized analytics and easily accessible reporting tools like dashboards and indicators, you can see real-time results, identify growth opportunities, and make informed decisions.

Artificial Intelligence for Customer Insights

Companies use CRM technology to uncover the right customers and personalize their experiences in real-time. Nurturing customer relationships is as much about understanding individual customers as it is about knowing your customers. Insights in CRM are used to design the most relevant products and services to drive brand loyalty and drive top-line growth. Getting the most from CRM is about getting insights from your data. Insights derived from AI can uncover all sorts of insights, from customer lifetime value to correlation of historical trends with current data to engagement patterns and trends.

How AI Helps

Thanks to AI-powered tools, quality control is as simple as scanning barcodes with a camera. Or, you can use object and image recognition to detect defects in products or make automated quality control adjustments. With machine learning capabilities, robots can reduce the workload of human labor, decreasing costs and saving time.

Bringing AI to your product and service means speed and cost savings. With insights into your customers’ buying patterns and sentiments, you can adjust to meet them where they are. Or, you can change how products are sold to maximize profits. No matter what your next steps are, be sure to work with an expert and ask questions to avoid getting bogged down in the hype of the AI trend.

What to Know When Utilizing AI

AI tools can reduce time spent on administrative tasks, empowering you to focus on growth. As machine learning and data science advance, the use of AI will be a no-brainer for hospitals, providing technology to bolster patient care. Data analytics and AI will help optimize hospital staff performance through predictive tools and predictive tools and provide on-the-go support for medical professionals. AI offers unprecedented transparency into operations and patient services. Integrating and applying AI services will help increase revenue, help reduce costs, improve patient outcomes and ultimately improve the quality of care. By employing the right artificial intelligence, hospitals will be able to make informed decisions, improve patient care and drive growth. Ovid Therapeutics 1.

The Value of Data

Data is the new oil. Every company needs data to drive growth, but few have the bandwidth to commit to managing the volume and variety of data they have today. Despite the fact that most companies invest in data analytics software, few are able to harness their data to create actionable insights that help grow the bottom line. In addition, most large organizations have struggled to put together a holistic data strategy that addresses their organizations’ business goals. And yet, many companies are committed to advancing digital transformation, which relies heavily on data to drive innovation, growth, and innovation.

The Importance of the Data Architecture in AI

The industry is moving to a new era of rapid innovation with advancements in the use of advanced technology and increased usage of artificial intelligence. CIOs need to transform business models and rework existing technologies to leverage the power of AI.

Companies that are looking to implement AI should take the following steps:

Saturate your data with AI-related use cases: If you want to leverage AI technology, you must first identify which use cases will be the most beneficial to your business and focus your efforts on that.

Developing data models is the first step.

Create a data hierarchy using hierarchical dimensions (e.g., top-to-bottom, left-to-right, left-to-right-middle, bottom-to-top) that represent common data dimensions.

The Importance of Data Analysis

From analyzing product and customer feedback to improving patient outcomes, data analysis and data visualization is the key to success. Businesses rely on data to drive progress, innovation, and growth. It’s time to break away from a spreadsheet-driven world and re-write your data. Everything starts with data analysis, whether you’re thinking about a possible merger or buying a competitor, or evaluating how well you’re performing in today’s competitive landscape.

From customer surveys to converting traffic into leads and improving patient outcomes, AI can help you cut through the clutter and make data-driven decisions.

Conclusion

Building a strong product, improving customer experience, and establishing a competitive edge through innovative technology is no longer enough to succeed in today’s competitive market. Both the product and customer experience need to be top-notch in order to stay relevant and generate growth. Insight AI is an analytical approach to delivering insights on critical business and technology issues.

The post Insight Artificial Intelligence: How To Make The Most of AI Technology first appeared on AITechTrend.

“As the convergence of technologies is changing the global technology landscape and evolving business strategies, we are seeing companies of all sizes and in all industries start to embrace digital transformation,” said James R. Scapa, founder and chief executive officer, Altair. “Future.AI is the ideal event for business leaders to gain inspiration, insights, and best practices that can be applied to their organizations, regardless of where they fall in their digital transformation journey.”

The global event will connect scientists, engineers, business teams, and creative thinkers who are harnessing the power of data analytics and AI to gain competitive advantages and drive better business results. Attendees will be empowered to discover their data potential and learn from those who are operationalizing data analytics and AI to compete more effectively. Future.AI will include insightful keynotes from thought leaders, panels of experts addressing the latest trends, and more, including:

• Keynote presentation from Dr. Anima Anandkumar, director of machine learning research, NVIDIA
• “AI Takes to the Cloud” panel featuring Intel, Google, Microsoft, NVIDIA, and Oracle
• “AI and Digital Transformation: Paving a Path to Better Business Outcomes,” fireside chat with Capgemini and Sam Mahalingam, Altair chief technical officer
• “Women in Data Analytics” panel will explore challenges and opportunities for women in a male-dominated field
• Industry-specific breakout sessions featuring Rolls-Royce, HSBC, Jaguar Land Rover, FlexTrade, KLA, BreakForth, Mabe, and Meyers Constructors

To learn more and to register for Future.AI, visit https://web.altair.com/future-ai-2021.

The post Altair Future.AI Global Event to Demonstrate How Artificial Intelligence and Analytics Accelerate Digital Transformation first appeared on AITechTrend.

 Ibex Medical Analytics, the pioneer in AI-powered cancer diagnostics, today announced a $38 million Series B financing round led by Octopus Ventures and 83North, with additional participation from aMoon, Planven Entrepreneur Ventures and Dell Technologies Capital, the corporate venture arm of Dell Technologies, bringing total funding of Ibex to $52 million since its inception in 2016 as part of the Kamet Ventures incubator.

Ibex transforms cancer diagnosis by harnessing unique artificial intelligence (AI) and machine learning technology at an unprecedented scale. The company’s Galen platform supports physicians and providers to improve diagnostic accuracy and efficiency, and enables development of new AI tools for precision medicine in oncology. 

Pathologists play a crucial role in the detection and diagnosis of disease, with their assessment being vital for correct treatment decisions in cancer care. However, a rise in cancer prevalence and advances in personalized medicine have resulted in growing diagnostic complexity that significantly increases pathologists’ workloads. Coupled with a global shortage of pathologists, these increasing workloads lead to delays and concerns over diagnostic quality. This situation has underscored the need for new tools that enable pathologists to more efficiently and accurately view and analyze tissue samples. 

Transforming patient care, health economics and the practice of pathology, Ibex’s solutions detect cancer in real-time, enabling 100% quality control, while also reducing turnaround times. Installed in labs worldwide as part of everyday clinical practice, Ibex’s Galen Prostate and Galen Breast solutions use strong-AI algorithms and routinely detect misdiagnosed and mis-graded cancers in digitized slides, guiding pathologists to areas of cancer in support of a prompt review. Moreover, Ibex is collaborating with multiple partners on developing AI-markers for prognostic and predictive applications used in cancer management and drug development. 

Ibex will use the investment to support a rapidly expanding customer base of clinical deployments in labs and health systems in North America and Europe and grow talent across R&D, clinical and commercial teams. The investment will accelerate expansion of the Galen solution portfolio at Ibex, bringing new AI tools for more tissue types, including novel AI-based enhancements of the pathology workflow and oncology focused AI-markers. 

“Ibex is at the forefront of digital transformation in pathology and we are committed to supporting our customers on their AI journey,” said Joseph Mossel, Ibex CEO and Co-founder. “Quality diagnosis is our top priority and a cornerstone of cancer care programs. I am proud of our team, demonstrating through clinical studies and, more importantly, in live clinical settings, that our AI is a game changer in eliminating misdiagnosis and ensuring real-time patient safety. This investment will help us meet the growing demand for AI and digital pathology rollouts and develop AI-markers for a more targeted treatment of cancer.” 

“The Ibex team has successfully translated a rich collection of digital pathology data assets and deep learning technology into clinical-grade products that excel in studies and already deliver value to patients. We look forward to partnering with the leadership team at Ibex as the company continues its journey to transform cancer diagnosis,” said Will Gibbs, early stage health investor at Octopus Ventures, who will be joining the Ibex Board as part of this funding round. “The Ibex platform has the potential to deliver meaningful changes to parts of the cancer pathway that have historically been neglected but are critical to improving outcomes for the most common cancer types, but this is quickly changing.”

“We continue to be impressed with Ibex’s progress since the previous investment round, making a meaningful impact on cancer diagnosis globally,” said Gil Goren, General Partner at 83North and an Ibex Board member. “In a relatively short amount of time, Ibex has proven its utility to users, making AI a key driver in their decision to go digital. We are proud of the company’s achievements and look forward to the next phase in the company’s continued growth.”

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