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## Bayesian Thinking & Its Underlying Principles

In the previous blog on Bayes’ Theorem, we left off at an interesting junction where we just touched upon the ideas on prior odds ratio, likelihood ratio and the resulting Posterior Odds Ratio. However, we didn’t go into much detail of what it means in real life scenarios and how should we use them.

In this blog, we will introduce the powerful concept of “Bayesian Thinking” and explain why it is so important. Bayesian Thinking is a practical application of the Bayes’ Theorem which can be used as a powerful decision-making tool too!

We’ll consider an example to understand how Bayesian Thinking is used to make sound decisions.

For the sake of simplicity, let’s imagine a management consultation firm hires only two types of employees. Let’s say, IT professionals and business consultants. You come across an employee of this firm, let’s call him Raj. You notice something about Raj instantly. Raj is shy. Now if you were asked to guess which type of employee Raj is what would be your guess?

If your guess is that Raj is an IT guy based on shyness as an attribute, then you have already fallen for one of the inherent cognitive biases. We’ll talk more about it later. But what if it can be proved Raj is actually twice as likely to be a Business Consultant?!

This is where Bayesian Thinking allows us to keep account of priors and likelihood information to predict a posterior probability.

The inherent cognitive bias you fell for is actually called – Base Rate Neglect. Base Rate Neglect occurs when we do not take into account the underlying proportion of a group in the population. Put it simply, what is the proportion of IT professionals to Business consultants in a business management firm? It would be fair to assume for every 1 IT professional, the firm hires 10 business consultants.

Another assumption could be made about shyness as an attribute. It would be fair to assume shyness is more common in IT professionals as compared to business consultants. Let’s assume, 75% of IT professionals are in fact shy corresponding to about 15% of business consultants.

Think of the proportion of employees in the firm as the prior odds. Now, think of the shyness as an attribute as the Likelihood. The figure below demonstrates when we take a product of the two, we get posterior odds.

Plugging in the values shows us that Raj is actually twice as likely to be a Business consultant. This proves to us that by applying Bayesian Thinking we can eliminate bias and make a sound judgment.

Now, it would be unrealistic for you to try drawing a diagram or quantifying assumptions in most of the cases. So, how do we learn to apply Bayesian Thinking without quantifying our assumptions? Turns out we could, if we understood what are the underlying principles of Bayesian Thinking are.

#### Rule 1 – Remember your priors!

As we saw earlier how easy it is to fall for the base rate neglect trap. The underlying proportion in the population is often times neglected and we as human beings have a tendency to just focus on just the attribute. Think of priors as the underlying or the background knowledge which is essentially an additional bit of information in addition to the likelihood. A product of the priors together with likelihood determines the posterior odds/probability.

#### Rule 2 – Question your existing belief

This is somewhat tricky and counter-intuitive to grasp but question your priors. Present yourself with a hypothesis what if your priors were irrelevant or even wrong? How will that affect your posterior probability? Would the new posterior probability be any different than the existing one if your priors are irrelevant or even wrong?

#### Rule 3 – Update incrementally

We live in a dynamic world where evidence and attributes are constantly shifting. While it is okay to believe in well-tested priors and likelihoods in the present moment. However, always question does my priors & likelihood still hold true today? In other words, update your beliefs incrementally as new information or evidence surfaces. A good example of this would be the shifting sentiments of the financial markets. What holds true today, may not tomorrow? Hence, the priors and likelihoods must also be incrementally updated.

#### Conclusion

In conclusion, Bayesian Thinking is a powerful tool to hone your judgment skills. Developing Bayesian Thinking essentially tells us what to believe in and how much confident you are about that belief. It also allows us to shift our existing beliefs in light of new information or as the evidence unfolds. Hopefully, you now have a better understanding of Bayesian Thinking and why is it so important.

On that note, we would like to say DexLab Analytics is a premium data analytics training institute located in the heart of Delhi NCR. We provide intensive training on a plethora of data-centric subjects, including data science, Python and credit risk analytics. Stay tuned for more such interesting blogs and updates!

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

## The Almighty Central Limit Theorem

The Central Limit Theorem (CLT) is perhaps one of the most important results in all of the statistics. In this blog, we will take a glance at why CLT is so special and how it works out in practice. Intuitive examples will be used to explain the underlying concepts of CLT.

First, let us take a look at why CLT is so significant. Firstly, CLT affords us the flexibility of not knowing the underlying distribution of any data set provided if the sample is large enough. Secondly, it enables us to make “Large sample inference” about the population parameters such as its mean and standard deviation.

The obvious question anybody would be asking themselves is why it is useful not to know the underlying distribution of a given data set?

To put it simply in real life, often times than not the population size of anything will be unknown. Population size here refers to the entire collection of something, like the exact number of cars in Gurgaon, NCR at any given day. It would be very cumbersome and expensive to get a true estimate of the population size. If the population size is unknown its underlying distribution will be known too and so will be its standard deviation. Here, CLT is used to approximate the underlying unknown distribution to a normal distribution. In a nutshell, we don’t have to worry about knowing the size of the population or its distribution. If the sample sizes are large enough, i.e. – we have a lot of observed data, it takes the shape of a symmetric bell-shaped curve.

Now let’s talk about what we mean by “Large sample inference”. Imagine slicing up the data into ‘n’ number of samples as below:

Now, each of these samples will have a mean of their own.

Therefore, effectively the mean of each sample is a random variable which follows the below distribution:

Imagine, plotting each of the sample mean on a line plot, and as “n”, i.e. the number of samples goes to infinity or a large number the distribution takes a perfect bell-shaped curve, i.e – it tends to a normal distribution.

Large sample inferences could be drawn about the population from the above distribution of x̅. Say, if you’d like to know the probability that any given sample mean will not exceed quantity or limit.

The Central Limit Theorem has vast application in statistics which makes analyzing very large quantities easy through a large enough sample. Some of these we will meet in the subsequent blogs.

Try this for yourself: Imagine the average number of cars transiting from Gurgaon in any given week is normally distributed with the following parameter . A study was conducted which observed weekly car transition through Gurgaon for 4 weeks. What is the probability that in the 5th week number of cars transiting through Gurgaon will not exceed 113,000?

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

About the Institute: DexLab Analytics is a premier data analytics training institute headquartered in Gurgaon. The expert consultants working here craft the most industry-relevant courses for interested candidates. Our technology-driven classrooms enhance the learning experience.

## Upskill and Upgrade: The Mantra for Budding Data Scientists

Have the right skills? Then the hottest jobs of the millennium might be waiting for you! The job profiles of data analysts, data scientists, data managers and statisticians harbour great potentials.

However, the biggest challenge in today’s age lies in preparing novice graduates for Industry 4.0 jobs. Although no one has yet cleared which roles will cease to exist and which new roles will be created, the consultants have started advising students to imbibe necessary skills and up-skill in domains that are likely to influence and carve the future jobs. Becoming adaptive is the best way to sail high in the looming technology-dominated future.

#### Data Science and Future

In this context, data science has proved to be one of the most promising fields of technology and science that exhibits a wide gap between demand and supply yet an absolute imperative across disciplines. “Today there is no shortage of data or computing abilities but there is a shortage of workforce equipped with the right skill set that can interpret data and get valuable insights,” revealed James Abdey, assistant professorial lecturer Statistics, London School of Economics and Political Science (LSE).

He further added that data science is a multidisciplinary field – drawing collectives from Economics, Mathematics, Finance, Statistics and more.

As a matter of fact, anyone, who has the right skill and expertise, can become a data scientist. The required skills are analytical thinking, problem-solving and decision-making aptitude. “As everything becomes data-driven, acquiring analytical and statistical skill sets will soon be imperative for all students, including those pursuing Social Sciences or Liberal Arts and also for professionals,” said Jitin Chadha, founder and director, Indian School of Business and Finance (ISBF).

DexLab Analytics is one of the most prominent deep learning training institutes seated in the heart of Delhi. We offer state-of-the-art in-demand skill training courses to all the interested candidates.

The dearth of expert training faculty and obsolete curriculum acts as major roadblocks to the success of data science training. Such hindrances cause difficulty in preparing graduates for Industry 4.0. In this regard, Chiraag Mehta from ISBF shared that by increasing international collaborations and intensifying industry-academia connect, they can formulate an effective solution and bring forth the best practices to the classrooms. “With international collaborations, higher education institutes can bring in the latest curriculum while a deeper industry-academia connect including, guest lecturers from industry players and internships will help students relate the theory to real-world applications, ” shared Mehta during an interview with Education Times.

#### Industry 4.0: A Brief Overview

The concept Industry 4.0 encompasses the potential of a new industrial revolution – where gathering and analyzing data across machines will become the order of the day. The rise of this new digital industrial revolution is expected to facilitate faster, more flexible and efficient processes to manufacture high-quality products at reduced costs – thus, increasing productivity, switch economies, stimulate industrial growth and reform workforce profile.

Want to know more about data science courses in Gurgaon? Feel free to reach us at DexLab Analytics.

The blog has been sourced fromtimesofindia.indiatimes.com/home/education/news/learn-to-upskill-and-be-adaptive/articleshow/68989949.cms

## Bayes’ Theorem: A Brief Explanation

(This is in continuation of the previous blog, which was published on 22nd April, 2019 – www.dexlabanalytics.com/blog/a-beginners-guide-to-learning-data-science-fundamentals )

In this blog, we’ll try to get a hands-on understanding of the Bayes’ Theorem. While doing so, hopefully we’ll be able to grasp a basic understanding of concepts such as Prior odds ratio, Likelihood ratio and Posterior odds ratio.

Arguably, a lot of classification problems have their root in Bayes’ Theorem. Reverend T. Bayes came up with this superior logical function, which mathematically deducts the probability of an event occurring from a larger set by “flipping” the conditional probabilities.

Consider,  E1, E2, E3,……..En to be a partition a larger set “S” and now define an Event – A, such that A is a subset of S.

Let the square be the larger set “S” containing mutually exclusive events Ei’s.  Now, let the yellow ring passing through all Ei’s be an event – A.

Using conditional probabilities, we know,

#### Rearranging the values of  &  gives us the Bayes Theorem:

The values of  are also known as prior probabilities, the event A is some event, which is known to have occurred and the conditional probability   is known as the posterior probability.

Now that, you’ve got the maths behind it, it’s time to visualise its practical application. Bayesian thinking is a method of applying Bayes’ Theorem into a practical scenario to make sound judgements.

The next blog will be dedicated to Bayesian Thinking and its principles.

For now, imagine, there have been news headlines about builders snooping around houses they work in. You’ve got a builder in to work on something in your house. There is room for all sorts of bias to influence you into believing that the builder in your house is also an opportunistic thief.

However, if you were to apply Bayesian thinking, you can deduce that only a small fraction of the population are builders and of that population, a very tiny proportion is opportunistic thieves. Therefore, the probability of the builder in your house being an opportunistic thief is actually a product of the two proportions, which is indeed very-very small.

Technically speaking, we call the resulting posterior odds ratio as a product of prior odds ratio and likelihood ratio. More on applying Bayesian Thinking coming up in the next blog.

#### In the above example on “snooping builders”, what are your:

• Ei’s
• Event – A
• “S”

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

About the Institute: DexLab Analytics is a premier data analyst training institute in Gurgaon specializing in an enriching array of in-demand skill training courses for interested candidates. Skilled industry consultants craft state-of-the-art big data courses and excellent placement assistance ensures job guarantee.

For more from the tech series, stay tuned!

## Study: The Demand for Data Scientists is Likely to Rise Sharply

Data is like the new oil. A large number of companies are leveraging artificial intelligence and big data to mine these vast volumes of data in today’s time. Data science is a promising landmine of job opportunities – and it’s high time to consider it as a successful career avenue.

The prospect of data science is skyrocketing. Today, it is estimated that more than 50000 data science and machine learning jobs are lying vacant. Plus, nearly 40000 new jobs are to be generated in India alone by 2020. If you follow the global trends, the role of data scientist has expanded over 650% since 2012 yet only 35000 people in the US are skilled enough.

Data scientists are like the platform that connects the dots between programming and implementation of data to solve challenging business intricacies – says Pankaj Muthe, Academic Program Manager (APAC), Company Spokesperson, QlikTech. The company delivers intuitive platform solutions for embedded analytics, self-service data visualizations and guided analytics and reporting across the globe.

According to a pool of experts, data science is the hottest job trend of this century and is the second most popular degree to have at the master level next to MBA. No wonder, this new breed of science and technology is believed to be driving a new wave of innovation! Data scientists and front-end developers attracted the highest remuneration across Indian startups throughout 2017.

#### Eligibility Criteria

To become a professional data scientist, a degree in computer science/engineering or mathematics is a must. Most of the data scientists have a knack for intricate tasks and aptitude to learn challenging programming languages. Any good organization seeks interested and intelligent candidates with the zeal to learn more. The subjects in which they need to be proficient are mathematics, statistics and programming. Moreover, data science jobs need a very sound base in machine learning algorithms, statistical modeling and neural networks as well as incredible communication skills.

Today, a lot of institutes offer state-of-the-art data science online courses that prove extremely beneficial for career growth and expansion. Combining theoretical knowledge and technical aspects of data science training, these institutes provide skill and assistance to develop real-world applications. DexLab Analytics is one such institute that is located in the heart of Delhi NCR. For more, feel free to reach us at <www.dexlabanalytics.com>

#### Future Prospects

After land, labour and capital, data ranks as the fourth factor of production. According to the US Department of Statistics, the demand for data engineers is likely to grow by 40% by 2020. If you are looking for a flourishing career option, this is the place to be: an entry-level engineer begins their career as a business analyst and then proceeds towards becoming a project manager. Later, after years of experience, these virgin business analysts further get promoted to become chief data officers.

## General Python Guide 2019: Learning Data Analytics with Python

Python and data analytics are possibly three of the most commonly heard words these days. In today’s burgeoning tech scene, being skillful in these two subjects can prove very profitable. Over the years, we have seen the importance of Python education in the field of data science skyrocketing.

So here we present a general guide to help start off your Python learning:

• #### Popularity

With over 40% data scientists preferring Python, it is clearly one of the most widely used tools in data analysis. It has risen in popularity above SAS and SQL, only lagging behind R.

• #### General Purpose Language

There might be many other great tools in the market for analyzing data, like SAS and R, but Python is the only trustworthy general-purpose language valid across a number of application domains.

#### Step 1: Setup Python Environment

Setting up Python environment is uncomplicated, but a primary step. Downloading the free Anaconda Python package is recommended. Besides core Python language, it includes all the essential libraries, such as Pandas, SciPy, NumPy and IPython, and graphical installer also. Post installation, a package containing several programs is launched, most important one being iPython also known as Jupyter notebook. After launching the notebook, the terminal opens and a notebook is started in the browser. This browser works as the coding platform and there’s no need for internet connection even.

#### Step 2: Knowing Python Fundamentals

Getting familiar with the basics of Python can happen online. Active participation in free online courses, where video tutorials, practice exercises are plentiful, can help you grasp the fundamentals quickly. However, if you are seeking expert guidance, you must explore our Python data science courses.

#### Step 3: Know Key Python Packages used for Data Analysis

Since it is a general purpose language, Python’s utility stretches beyond data science. But there are plentiful Python libraries useful in data functionalities.

Numpy – essential for scientific computing

Matplotib – handy for visualization and plotting

Pandas – used in data operations

Skikit-learn – library meant to help with data mining and machine learning activities

StatsModels – applied for statistical analysis and modeling

Scipy-SciPy – the Numpy extension of Python; it is a set of math functions and algorithms

Theano – package defining multi-dimensional arrays.

#### Step 4: Load Sample Data for Practice

Working with sample datasets is a great way of getting familiar with a programming language. Through this kind of practice, candidates can try out different methods, apply novel techniques and also pinpoint areas of strength and in need of improvement.

Python library StatModels contains preloaded datasets for practice. Users can also download dataset from CSV files or other sources on web.

#### Step 5: Data Operations

Data administration is a key skill that helps extract information from raw data. Majority of times, we get access to crude data that cannot be analyzed straightaway; it needs to be manipulated before analyzing. Python has several tools for formatting, manipulating and cleaning data before it is examined.

#### Step 6: Efficient Data Visualization

Visuals are very valuable for investigative data analysis and also explaining results lucidly. The common Python library used for visualization is Matplotlib.

#### Step 7: Data Analytics

Formatting data and designing graphs and plots are important in data analysis. But the foundation of analytics is in statistical modeling, data mining and machine learning algorithms. Having libraries like StatsModels and Scikit-learn, Python provides all necessary tools essential for performing core analyzing functions.

#### Concluding

As mentioned before, the key to learning data analytics with Python is practicing with imported data sets. So without delay, start experimenting with old operations and new techniques on data sets.

For more useful blogs on data science, follow DexLab Analytics – we help you stay updated with all the latest happenings in the data world! Also, check our excellent Python courses in Delhi NCR.

## Being a Statistician Matters More, Here’s Why

Right data for the right analytics is the crux of the matter. Every data analyst looks for the right data set to bring value to his analytics journey. The best way to understand which data to pick is fact-finding and that is possible through data visualization, basic statistics and other techniques related to statistics and machine learning – and this is exactly where the role of statisticians comes into play. The skill and expertise of statisticians are of higher importance.

Below, we have mentioned the 3R’s that boosts the performance of statisticians:

Recognize – Data classification is performed using inferential statistics, descriptive and diverse other sampling techniques.

Ratify – It’s very important to approve your thought process and steer clear from acting on assumptions. To be a fine statistician, you should always indulge in consultations with business stakeholders and draw insights from them. Incorrect data decisions take its toll.

Reinforce – Remember, whenever you assess your data, there will be plenty of things to learn; at each level, you might discover a new approach to an existing problem. The key is to reinforce: consider learning something new and reinforcing it back to the data processing lifecycle sometime later. This kind of approach ensures transparency, fluency and builds a sustainable end-result.

Now, we will talk about the best statistical techniques that need to be applied for better data acknowledgment. This is to say the key to becoming a data analyst is through excelling the nuances of statistics and that is only possible when you possess the skills and expertise – and for that, we are here with some quick measures:

Distribution provides a quick classification view of values within a respective data set and helps us determine an outlier.

Central tendency is used to identify the correlation of each observation against a proposed central value. Mean, Median and Mode are top 3 means of finding that central value.

Dispersion is mostly measured through standard deviation because it offers the best scaled-down view of all the deviations, thus highly recommended.

Understanding and evaluating the data spread is the only way to determine the correlation and draw a conclusion out of the data. You would find different aspects to it when distributed into three equal sections, namely Quartile 1, Quartile 2 and Quartile 3, respectively. The difference between Q1 and Q3 is termed as the interquartile range.

While drawing a conclusion, we would like to say the nature of data holds crucial significance. It decides the course of your outcome. That’s why we suggest you gather and play with your data as long as you like for its going to influence the entire process of decision-making.

On that note, we hope the article has helped you understand the thumb-rule of becoming a good statistician and how you can improve your way of data selection. After all, data selection is the first stepping stone behind designing all machine learning models and solutions.

Saying that, if you are interested in learning machine learning course in Gurgaon, please check out DexLab Analytics. It is a premier data analyst training institute in the heart of Delhi offering state-of-the-art courses.

The blog has been sourced from www.analyticsindiamag.com/are-you-a-better-statistician-than-a-data-analyst

## How Deep Learning is Solving Forecasting Challenges in Retail Industry

Known to all, the present-day retail industry is obsessed with all-things-data. With Amazon leading the show, many retailers are found implementing a data-driven mindset throughout the organization. Accurate predictions are significant for retailers, and AI is good in churning out value from retail datasets. Better accuracy in forecasts has resulted in widespread positive impacts.

Below, we’ve chalked down how deep learning, a subset of machine learning addresses retail forecasting issues. It is a prime key to solve most common retail prediction challenges – and here is how:

• Deep learning helps in developing advanced, customized forecasting models that are based on unstructured retail data sets. Relying on Graphic Processing Units, it helps process complex tasks – though GPUs area applied only twice during the process; once during training the model and then at the time of inference when the model is applied to new data sets.

• Deep learning-inspired solutions help discover complex patterns in data sets. In case of big retailers, the impressive technology of Deep Learning supports multiple SKUs all at the same time, which proves productive on the part of models as they get to learn from the similarities and differences to seek correlations for promotion or competition. For example, winter gloves sell well when puffer jackets are already winning the market, indicating sales. On top of that, deep learning can also ascertain whether an item was not sold or was simply out of stock. It also possesses the ability to determine the larger problem as to why the product was not being sold or marketed.

• For a ‘cold start’, historical data is limited but deep learning has the power to leverage other attributes and boost the forecasting. The technology works by picking similar SKUs and implement that information to bootstrap forecasting process.

Nonetheless, there exists an array of challenges associated with Deep Learning technology. The very development of high-end AI applications is at a nascent stage; it is yet to become a fully functional engineering practice.

A larger chunk of successful AI implementation depends on the expertise and experience of the breed of data scientists involved. Handpicking a qualified data scientist in today’s world is the real ordeal. Being fluent in the nuances of deep learning imposes extra challenges. Moreover, apart from being labor intensive in terms of feature engineering and data cleaning, the entire methodology of developing neural network models all manually is difficult and downright challenging. It may even take a substantial amount of time to learn the tricks and scrounge through numerous computational resources and experiments performed by data scientists. All this makes the hunt down for skilled data scientists even more difficult.

Fortunately, DexLab Analytics is here with its top of the line data science courses in Gurgaon. The courses offered by the prominent institute are intensive, well-crafted and entirely industry-relevant. For more information on data analyst course in Delhi NCR, visit our homepage.

The blog has been sourced from ―
www.forbes.com/sites/nvidia/2018/11/21/how-deep-learning-solves-retail-forecasting-challenges/#6cf36740db18

## Databricks Supports Apache Spark 2.4 and Adds ML Runtime

Databricks recently embraced the Apache Spark 2.4, a latest version. They are integrating it into their platform of analytics. Also, the company is on its way to unveil another runtime feature that would simplify the intricacies of deep learning.

Needless to say, Databricks is one of the most powerful supporters of version 2.4 of Spark, the notable stream processing framework.  The latest upgraded version features improvement in the performance of machine learning framework running on Spark as well as distributed deep learning. It also includes modifications that would instantly address dependency issues related to deep learning tasks.

Project Hydrogen is an ambitious initiative; it’s under this tag the Spark upgrades were fused and introduced as a new scheduling mode, known as ‘barrier execution’. It encourages developers to embed training in lieu of distributed deep learning posed as an Apache Spark workload.

In context to above, Reynold Xin, a staunch Spark contributor and co-founder at Databricks said, “This is the largest change to Spark’s scheduler since the inception of the project.” He further mentioned that the upgrades will actually help reduce the complexities of machine learning structures and ensure high efficacy.

The latest runtime detail categorized HorovodRunner is developed to rationalize scaling and streamlining of distributed deep learning workloads. It is performed from a single machine to huge clusters. Previously, drifting from single-node workloads to huge distributed training on GPU or CPU clusters needed a bunch of full code rewrites – it was exceedingly challenging enough. Undeniably, HorovodRunner reduces training as well as programming time cutting down them from hours to a few minutes. This was claimed by the professionals working at Databricks.

Besides Horovod, Databricks is found to be saying that its platform offers native integration with TensorFlow, Kera and several other machine learning programs coupled with MLib and GraphFrames super machine learning algorithms.

On top of all this, a few weeks back, Databricks associated itself with a versatile cloud data integrator Talend with a sole aim to integrate the cloud service with their own data analytics platform to allow data scientists leverage the cluster computing framework – it would help process large data sets at scale.

Apache Spark is a robust, well-integrated analytics engine efficient in processing large datasets. Crafted for high speed, productivity and generic use, it is considered as one of the most popular projects in motion under Apache software umbrella. It is also one of the most volatile and active open source big data projects.

DexLab Analytics is a top-notch Apache Spark training institute in Gurgaon. It provides top of the line in-demand skill training on a plethora of new-age IT related courses, such as data science, data analytics courses, big data, risk analytics and more.