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MongoDB Basics Part-II

Posted on February 20, 2021 by Dexlab

In our previous blog we discussed about few of the basic functions of MQL like .find() , .count() , .pretty() etc. and in this blog we will continue to do the same. At the end of the blog there is a quiz for you to solve, feel free to test your knowledge and wisdom you have gained so far.

Given below is the list of functions that can be used for data wrangling:-

updateOne() :- This function is used to change the current value of a field in a single document.

After changing the database to “sample_geospatial” we want to see what the document looks like? So for that we will use .findOne() function.

Now lets update the field value of “recrd” from ‘ ’ to “abc” where the “feature_type” is ‘Wrecks-Visible’.

Now within the .updateOne() funtion any thing in the first part of { } is the condition on the basis of which we want to update the given document and the second part is the changes which we want to make. Here we are saying that set the value as “abc” in the “recrd” field . In case you wanted to increase the value by a certain number ( assuming that the value is integer or float) you can use “$inc” instead.

2. updateMany() :- This function updates many documents at once based on the condition provided.

3. deleteOne() & deleteMany() :- These functions are used to delete one or many documents based on the given condition or field.

4. Logical Operators :-

“$and” : It is used to match all the conditions.

“$or” : It is used to match any of the conditions.

The first code matches both the conditions i.e. name should be “Wetpaint” and “category_code” should be “web”, whereas the second code matches any one of the conditions i.e. either name should be “Wetpaint” or “Facebook”. Try these codes and see the difference by yourself.

So, with that we come to the end of the discussion on the MongoDB Basics. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

ARIMA (Auto-Regressive Integrated Moving Average)

Posted on January 27, 2021February 13, 2021 by Dexlab

This is another blog added to the series of time series forecasting. In this particular blog I will be discussing about the basic concepts of ARIMA model.

So what is ARIMA?

ARIMA also known as Autoregressive Integrated Moving Average is a time series forecasting model that helps us predict the future values on the basis of the past values. This model predicts the future values on the basis of the data’s own lags and its lagged errors.

When a data does not reflect any seasonal changes and plus it does not have a pattern of random white noise or residual then an ARIMA model can be used for forecasting.

There are three parameters attributed to an ARIMA model p, q and d :-

p :- corresponds to the autoregressive part

q:- corresponds to the moving average part.

d:- corresponds to number of differencing required to make the data stationary.

In our previous blog we have already discussed in detail what is p and q but what we haven’t discussed is what is d and what is the meaning of differencing (a term missing in ARMA model).

Since AR is a linear regression model and works best when the independent variables are not correlated, differencing can be used to make the model stationary which is subtracting the previous value from the current value so that the prediction of any further values can be stabilized . In case the model is already stationary the value of d=0. Therefore “differencing is the minimum number of deductions required to make the model stationary”. The order of d depends on exactly when your model becomes stationary i.e. in case the autocorrelation is positive over 10 lags then we can do further differencing otherwise in case autocorrelation is very negative at the first lag then we have an over-differenced series.

The formula for the ARIMA model would be:-

To check if ARIMA model is suited for our dataset i.e. to check the stationary of the data we will apply Dickey Fuller test and depending on the results we will using differencing.

In my next blog I will be discussing about how to perform time series forecasting using ARIMA model manually and what is Dickey Fuller test and how to apply that, so just keep on following us for more.

So, with that we come to the end of the discussion on the ARIMA Model. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

Autocorrelation- Time Series – Part 3

Posted on January 22, 2021February 13, 2021 by Dexlab

Autocorrelation is a special case of correlation. It refers to the relationship between successive values of the same variables .For example if an individual with a consumption pattern:-

spends too much in period 1 then he will try to compensate that in period 2 by spending less than usual. This would mean that Ut is correlated with Ut+1 . If it is plotted the graph will appear as follows :

Positive Autocorrelation : When the previous year’s error effects the current year’s error in such a way that when a graph is plotted the line moves in the upward direction or when the error of the time t-1 carries over into a positive error in the following period it is called a positive autocorrelation.
Negative Autocorrelation : When the previous year’s error effects the current year’s error in such a way that when a graph is plotted the line moves in the downward direction or when the error of the time t-1 carries over into a negative error in the following period it is called a negative autocorrelation.

Now there are two ways of detecting the presence of autocorrelation
By plotting a scatter plot of the estimated residual (ei) against one another i.e. present value of residuals are plotted against its own past value.

If most of the points fall in the 1st and the 3rd quadrants , autocorrelation will be positive since the products are positive.

If most of the points fall in the 2nd and 4th quadrant , the autocorrelation will be negative, because the products are negative.
By plotting ei against time : The successive values of ei are plotted against time would indicate the possible presence of autocorrelation .If e’s in successive time show a regular time pattern, then there is autocorrelation in the function. The autocorrelation is said to be negative if successive values of ei changes sign frequently.
First Order of Autocorrelation (AR-1)
When t-1 time period’s error affects the error of time period t (current time period), then it is called first order of autocorrelation.
AR-1 coefficient p takes values between +1 and -1
The size of this coefficient p determines the strength of autocorrelation.
A positive value of p indicates a positive autocorrelation.
A negative value of p indicates a negative autocorrelation
In case if p = 0, then this indicates there is no autocorrelation.
To explain the error term in any particular period t, we use the following formula:-

Where Vt= a random term which fulfills all the usual assumptions of OLS
How to find the value of p?

One can estimate the value of ρ by applying the following formula :-

Time Series Analysis & Modelling with Python (Part II) – Data Smoothing

Posted on January 20, 2021January 20, 2021 by Dexlab

Data Smoothing is done to better understand the hidden patterns in the data. In the non- stationary processes, it is very hard to forecast the data as the variance over a period of time changes, therefore data smoothing techniques are used to smooth out the irregular roughness to see a clearer signal.

In this segment we will be discussing two of the most important data smoothing techniques :-

Moving average smoothing
Exponential smoothing

Moving average smoothing

Moving average is a technique where subsets of original data are created and then average of each subset is taken to smooth out the data and find the value in between each subset which better helps to see the trend over a period of time.

Lets take an example to better understand the problem.

Suppose that we have a data of price observed over a period of time and it is a non-stationary data so that the tend is hard to recognize.

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	?

In the above data we don’t know the value of the 6^th quarter.

….fig (1)

The plot above shows that there is no trend the data is following so to better understand the pattern we calculate the moving average over three quarter at a time so that we get in between values as well as we get the missing value of the 6^th quarter.

To find the missing value of 6^th quarter we will use previous three quarter’s data i.e.

MAS = = 15.7

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	15.7

MAS = = 13

MAS = = 14.33

QTR (quarter)	Price	MAS (Price)
1	10	10
2	11	11
3	18	18
4	14	13
5	15	14.33
6	15.7	15.7

….. fig (2)

In the above graph we can see that after 3^rd quarter there is an upward sloping trend in the data.

Exponential Data Smoothing

In this method a larger weight ( ) which lies between 0 & 1 is given to the most recent observations and as the observation grows more distant the weight decreases exponentially.

The weights are decided on the basis how the data is, in case the data has low movement then we will choose the value of closer to 0 and in case the data has a lot more randomness then in that case we would like to choose the value of closer to 1.

EMA= F_t= F_t-1 + (A_t-1 – F_t-1)

Now lets see a practical example.

For this example we will be taking = 0.5

Taking the same data……

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	?
3	18	?
4	14	?
5	15	?
6	?	?

To find the value of yellow cell we need to find out the value of all the blue cells and since we do not have the initial value of F₁ we will use the value of A_1.Now lets do the calculation:-

F₂=10+0.5(10 – 10) = 10

F₃=10+0.5(11 – 10) = 10.5

F₄=10.5+0.5(18 – 10.5) = 14.25

F₅=14.25+0.5(14 – 14.25) = 14.13

F₆=14.13+0.5(15 – 14.13)= 14.56

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	10
3	18	10.5
4	14	14.25
5	15	14.13
6	14.56	14.56

In the above graph we see that there is a trend now where the data is moving in the upward direction.

So, with that we come to the end of the discussion on the Data smoothing method. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

Digital Transformation Calls for Wider Security Transformation!

Posted on October 25, 2018October 25, 2018 by Dexlab

Digital Transformation Calls for Wider Security Transformation!

Going Digital is the buzzword – conventional businesses are getting transformed, thanks to digital bandwagon! Each day, it’s developing some new ways to engage clients, associate with partners and strike better operational efficiencies. Today’s business houses are using digital power to enhance revenue and reduce cost, and we can’t agree more.

Digital business is generally the implementation of digital technologies to support business models through user behavior evolution and considerable regulation support. For an instance, let’s look at Uber:

New Technology – Transportation technology platform
Business Model – Driver-partners and riders model
User Behavior Norm – Acceptance of non-traditional transportation method
Regulation Support – Cities and countries modify regulation to strengthen models

Today, cyber security and technology risk-management are treasure keys to future business growth and prosperity – security industry has evolved a lot over the years in terms of risk mitigation measures. Digital transformation has made way for security transformation, and in this regard, below we’ve whittled down the elements used for security transformation:

Digital Technologies – Smart watches, smart cars, health bands, voice assistants and smart home devices are some of the latest digital technologies clogging the present industry. These devices are to be supported by robust application platforms using AI, Machine Learning and Big Data.

Business Models – Risk management techniques are perfect for determining information risks emanating from business processes. In digital businesses, dynamic processes are common and evolving. Traditional risk models can’t handle them.

Evolving User Behaviors – Consumers are king in the digital world. The users are empowered with tools to make their own choices. On the contrary, traditional security processes used to treat users as weak links.

Regulation Support – To manage risk, security and privacy, regulations around the globe are changing and control standards are being updated or modified. For effective adaptability with the relevant changes, compliance assurance and sustenance need to be modified.

A Few Fundamental Design Principles for Control Framework for Security Transformation

Business Accelerator – Only security is not just good enough for smooth digital transformation. Security has to take the role of an accelerator since the fundamental premise of going digital is to be fast in the market and enhance customer satisfaction.

Example – Biometric Authentic – it improves user speed and experience.

Technology Changes and Agile Design – The stream of technology is evolving – AI, ML, Blockchain, Virtual Reality, Internet of Things, etc. – every domain of technology is undergoing a robust transformation. Therefore, security controls have to be adaptable and agile in design.

Customer-oriented – Known to all, customers are the most important element in digital business. In the new digitized world, users are the ones who decide. Two-decade ago rule, ‘deny all, permit some’ is now changed into ‘permit all, deny some’ rule – and we are truly excited!

Automate and Digitize – It’s time security goes digital – automation is the key.

In the near future, risk management through security transformation is going to be the utmost priority for all risk managers –if you are interested in Market Risk Analytics, drop by DexLab Analytics. They are the best in town for recognized and reputable Value at Risk Model online training. For more, check out their official website.

The blog has been sourced from ― www.forbes.com/sites/forbestechcouncil/2018/09/27/the-digital-transformation-demands-large-scale-security-transformation/#64df7fc41892

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Risk Analytics: How to Frame Smarter Insights with Organizational Data

Posted on April 2, 2018April 2, 2018 by Dexlab

Companies are launching cloud-based data analytics solutions with an aim to aid banks improve and manage their risk efficiently and streamline other activities in the most cost-effective ways.

Risk analysis is a major constituent of banking circle. Analytics-intensive operations are being run in almost all banking institutions, including cyber-security, online data theft and third-party management. The concept of risk is not something new. For years, it has been the key responsibility of C-suite professionals, but the extravagant amount of awareness and recognition associated with risk analytics was missing then. Also, the regulatory and economic landscape of the world is changing and becoming more intense – hence, risks need to be managed adequately. The executive teams should make risk analytics their topmost agenda for better organization functioning.

Why risk analytics?

The first and foremost reason to incorporate risk analytics is to measure, quantify and forecast risk with amped certainty. Analytics help in developing a baseline for risk assessment in an organization by working on several dimensions of risk and pulling them in a single unified system for better results.

What are the potential benefits of risk analytics?

Risk analytics help in turning guesswork into meaningful insights by using a number of tools and techniques to draw perspectives, determine calculable scenarios and predict likely-to-happen events.
An organization stay exposed to risk. Why? Because of a pool of structured and unstructured data, including social media, blogs, websites available on both internal and external platforms. With risk analytics, you can integrate all these data into a single perspective offering actionable insights.
Risk is a largely encompassing concept, spilling across several domains of organizational structure that at times it can really be hard to know how to manage risk and pull out meaningful insights. In such situations, risk analytics play a pivotal role in ensuring organizations develop foresight for potential risks and provide answers to difficult questions so as to create a pathway for action.

Things to do now:

Ask the right questions

Analytics means research. It ushers you to ask questions and dig deeper into risk-related stuffs. But framing random questions don’t help. To have a real impact, conjure up a handful of questions that hits the real topic.

Understand interdependencies

Risk pierces into organizational boundaries. And analytics work by offering cross-enterprise insights, by inferring conclusions throughout the business. That makes it effective to tackle far-reaching issues.

Streamline productive programs

Analytics help decision-makers introspect and evaluate risks, as well as rewards – related to operational and strategic decisions. Adding insights into pre-determined actions to determine and curb risks yield sustainable value for the program, which in the end improves overall program performance.

In the end, risk analytics seem to be quite a daunting subject to take up, but the truth is, some organizations are really doing well in managing their risks. If you are frustrated somehow and this whole concept of risk analytics baffles you more, take up SAS risk management certification. DexLab Analytics, a premier market risk training institute offers incredible market risk courses for data-hungry aspirants.

The article has been sourced from – https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Deloitte-Analytics/dttl-analytics-us-da-oriskanalytics3minguide.pdf

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Now Navigate Through Risks with Better Data, Improved Analytics

Posted on December 19, 2017December 19, 2017 by Dexlab

The treasure trove of data can devise new improved ways to mitigate risks.

How to reduce the range of risks and better grasp the reins of the business? Though data is being gathered, and pushed through the highly advanced risk analytics tools, how do the risk insurers utilize these insights to boost improved decision-making procedures that affect the business future and potential losses?

Continue reading “Now Navigate Through Risks with Better Data, Improved Analytics”

Incredible Future Possibilities of Market Risk Analytics

Posted on December 18, 2017May 23, 2020 by Dexlab

Global risks are burgeoning; companies of all sizes are seeking the perks of risk analytics and management. Smart companies are realizing the change is coming from people as well as recent technological breakthroughs, including Big Data and AI. And CEOs are improvising their risk teams, and transforming them into perceptive strategic advisors to address budding dangerous threats like cybercrime.

Modern risk analysts have accurate knowledge about risk, artificial intelligence and cyber security – so, it’s time they get an opportunity to show a greater presence in the stoic boardrooms as strategic advisors. AI, the cutting-edge risk analytics tool surfaced out to enhance the inexorable march of big data. As such, their importance in the organization in assessing risk has greatly increased.

Continue reading “Incredible Future Possibilities of Market Risk Analytics”

Market Risk Management 101: Types of Market Risks and How to Manage Them

Posted on November 30, 2017May 23, 2020 by Dexlab

Last year, Britain opted to leave the European Union – and that created spiking fluctuation and acute market uncertainty across the globe.

Most of the investors out there know investment involves risks and rewards, just like head and tail in a coin and so do the analysts. Higher the risk, better are the chances to gain potential rewards. As a result, it is critical for both an investor and analyst to understand the true nature of market risks that influences the market conditions and controls the shooting volatility and the ways to manage those risks.

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Common Market Risks

Relevant market risks depend largely on the nature of investment as well as geographic boundaries. Some of the key market risks are as follows:

Interest Rate Risk – It is the risk of a decrease in the value of a security owing to changes in interest rates. The rate of change of interest rates is inversely proportional to bonds – based on a rationale that a bond is the future security of a healthy stream of payments – hence as interest rate rises, the price of the issued bonds decreases.

Inflation Risk – It relates to the risk that gets affected as the prices of goods and services increases reducing the value of money. This risk results in affecting the value of investments in a negative way. It decreases the purchasing power of money, thereby reducing the value of investment. Sometimes inflation risk is also known as Purchasing Power Risk.
Currency Risk – This type of risk arises when your money needs to be converted to a different currency for investment purposes. Here, a small change in exchange rates between the home currency and US dollars can affect your investment return.
Liquidity Risk – It refers to the risk of not being able to fulfill certain investment requirements quickly for a price that determines the true value of the asset. Sometimes, one may face difficulties in selling the investment due to a lack of buyers, resulting in a drastic decrease of investment value of that product until someone is ready to pay for it. Foreign investments, over-the-counter markets and small-capitalization stocks are some of the high liquidity risks items.
Sociopolitical Risk – The socio-political environ, such as war, terrorist attack, election and corruption affects the market conditions. They affect investor perceptions, resulting in severe oscillation in stock prices.

Managing Market Risk

Well, you can’t control the market risks from taking a front seat in your financial life, though you can take some steps to manage and mitigate them.

As globalization seeped through all leading economies and market segments, a majority of fintech institutions started realizing the criticality of an enhanced operational risk, especially related to cyber-security, IT failures and data theft. Amid this, cyber risks and data theft issues posed key challenges, followed by IT failures and outsourcing issues. The revolution of digitization did many goods to our society, but the moment banks got dependent on single computer networking setups, the vulnerability of confidential customer data leakage multiplied. As a result, the need for data analysts and market researchers spiked up – they are the trained souls who possess both the experience and expertise to tackle diverse investment portfolios for clients in the best way possible to fetch maximum profits.

For that, affluent market risk courses in Delhi are available around – train your mind well, before taking the big leap in the big field of data analytics. Once you are done, reach DexLab Analytics – their comprehensive Market Risk Modelling using SAS courses are top-of-the-line courses in the industry at present.

Catch market risk modeling demo session here,

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