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The Almighty Central Limit Theorem

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?

If you liked this blog, then do please leave a comment or suggestions below.

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.

 

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Upskill and Upgrade: The Mantra for Budding Data Scientists

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 Challenges Ahead

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.

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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

 

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Bayes’ Theorem: A Brief Explanation

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,

Also, the relationship:

Law of total probability states:

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 meantime try this exercise and leave your comments below in the comments section.

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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!

 

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General Python Guide 2019: Learning Data Analytics with Python

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:

Reasons to Choose Python:

  • 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.

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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.

 

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Being a Statistician Matters More, Here’s Why

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.

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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

 

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How Deep Learning is Solving Forecasting Challenges in Retail Industry

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.

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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
 

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Databricks Supports Apache Spark 2.4 and Adds ML Runtime

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.

About Apache Spark:

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.

 

The blog was sourced from ― www.datanami.com/2018/11/19/databricks-upgrades-spark-support-adds-ml-runtime

 

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Private Banks, Followed by E-commerce and Telecom Industry Shows High Adoption Rates for Data Analytics

Private Banks, Followed by E-commerce and Telecom Industry Shows High Adoption Rates for Data Analytics

Are you looking for a data analyst job? The chances of bagging a job at a private bank are more than that a public bank. The former is more likely to hire you than the latter.

As a matter of fact, data analytics is widely being used in the private banking and e-commerce sectors – according to a report on the state of data analytics in Indian business. The veritable report was released last month by Analytics India Magazine in association with the data science institute INSOFE. Next to banking and ecommerce, telecom and financial service sectors have started to adopt the tools of data analytics on a larger scale, the report mentioned.

The report was prepared focusing on 50 large firms across myriad sectors, namely Maruti Suzuki and Tata Motors in automobiles, ONGC and Reliance Industries under oil-drilling and refineries, Zomato and Paytm under e-commerce tab, and HDFC and the State Bank of India in banking.

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If you follow the study closely, you will discover that in a nutshell, data analytics and data science boasts of a healthy adoption rate all throughout – 64% large Indian firms has started implementing this wonder tool at their workplaces. As a fact, if a firm is found to have an analytics penetration rate of minimum 0.75% (which means, at least one analytics professional is found out of 133 employees in a company), we can say the company has adopted analytics.

Nevertheless, the rate of adoption was not universal overall. We can see that infrastructure firms have zero adoption rates – this might be due to a lack of resources to power up a robust analytics facility or whatever. Also, steel, power and oil exhibited low adoption rates as well with not even 40% of the surveyed firms crossing the 0.75% bar. On contrary, private banks and telecom industry showed a total 100% adoption rates.

Astonishingly, public sector banks showed a 50% adoption rate- almost half of the rate in the private sector.

The study revealed more and more companies in India are looking forward to data analytics to boost sales and marketing initiatives. The tools of analytics are largely employed in the sales domain, followed by finance and operations.

Apparently, not much of the results were directly comparable with that of the last year’s study. Interestingly, one metric – analytics penetration rate – was measured last year as well, which is nothing but the ratio of analytics-oriented employees to the total. Also, last year, you would have found one out of 59 employees in an average organization, which has now reached one data analyst for every 36 employees.

For detailed information, read the full blog here: qz.com/india/1482919/banks-telcos-e-commerce-firms-hire-most-data-analysts-in-india

If you are interested in following more such interesting blogs and technology-related updates, follow DexLab Analytics, a premium analytics training institute headquartered in Gurgaon, Delhi. Grab a data analyst certification today and join the bandwagon of success.

 

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6 Essential Skills Data Scientists Need to Add to Their Resumes

6 Essential Skills Data Scientists Need to Add to Their Resumes

Like all other career paths, cracking the hottest job of 21st century is mainly about gaining knowledge and developing important skills relevant to the job. And your resume should reflect all these skills. So what must the resume of a professional data scientist look like? Here are 6 key skills that must be in the fingertips of a good data scientist.

Stats and Math:

Not only blue-chip tech companies, even medium and small scale enterprises are operated by data science these days. And statistical knowledge is vital for that. You should be thorough with general statistical concepts, like distributions, tests, range, likelihood estimators, etc.

In mathematics, one must know the basics of linear algebra and multivariable calculus. This will definitely make a difference in your work outcomes as it enables you to improve predictive presentations.

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Excellent Programming and Computing Skills:

Simply put, being good at coding is a must. So, if you are a budding data scientist you must actively work towards developing a computing mind; you should be able to understand, write and even analyze code whenever necessary. This level of dexterity only comes through meticulous study and practice of not one, but a number of programming languages.

If you want to develop a programming skill which is especially designed for data scientists, then get enrolled for R programming certification. Over 40 percent data scientists prefer R for solving stat problems. But it must be noted that R isn’t easy to learn, especially for those who aren’t comfortable with codes.

Python is another language which is highly preferred by data scientists because it is very adaptable and hence, can be employed in all the different steps part of a data science project. Moreover, data sets can be created with ease and SQL tables can be imported into working codes when required. Considering these benefits and the fact that over 50% data scientists favor Python, an excellent Python Certification in Delhi should be first in your list of courses to undertake.

Live Projects

Learning isn’t effective unless you implement it practically. Moreover, your skills get duly appreciated when it’s demonstrated. Hence, always look for live projects you can join and try to understand the data architecture behind the screen. It may be up there in your head, but it needs to be implemented. Large companies actually prefer candidates who have more practical experience rather than just bookish knowledge.

Managing Unstructured Data

Unstructured data is any type of content that doesn’t fit into traditional database tables. These data types aren’t well organized and hence, sorting them becomes very difficult. Blogs, videos and customer reviews are some examples of unstructured data. Being able to manage unstructured data is an important skill for data scientists. Apache Hadoop, NoSQL and Microsoft HDI insight are some good software for tackling unstructured data. If you are interested to learn the techniques, you can look up the course details for Hadoop certification in Delhi at DexLab Analytics.

Storytelling with Data

Data scientists might have to work with complicated models and datasets, but they must know how to express their deductions in lucid language that’s simple and engaging. Hence their raw data must be expressed in the form of tables, charts and graphs, which are visually appealing and can capture the attention of stakeholders.

Academics and Degrees

A strong educational background is the door to the world of data science. Big companies prefer applicants who are master degree holders in either stats or math or computer science or physical science.

Data science is definitely the trendiest job and you might be eager to land one, but it’s not easy to acquire the above mentioned skills. If you are looking for guidance from experts who have previously worked in this field, then you should get enrolled for Data Science Courses in Delhi right away. The industry experts at DexLab Analytics tailor the courses to the unique needs of students and incorporate ample practical cases to help them get ready for the challenges ahead.

 

Reference: www.analyticsindiamag.com/7-things-data-scientists-must-have-in-their-resumes

 

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