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A statistical distribution is a parameterized mathematical function that gives the probabilities of different outcomes for a random variable. There are discrete and continuous distributions depending on the random value it models. This article will introduce the seven most important statistical distributions, show their Python simulations with either the Numpy library embedded functions or with a random variable generator, discuss the relationships among different distributions and their applications in data science.

Bernoulli distribution is a discrete distribution. The assumptions of Bernoulli distribution include:

1, only two outcomes;

2, only one trial.

Bernoulli distribution describes a random variable that only contains two outcomes. For example, when tossing a coin one time, you can only get “Head” or “Tail.” We can also generalize it by defining the outcomes as “success” and “failure.” If I assume that when I toss a die, I only care if I get six, I can define the outcome of a die showing six as “success” and all other outcomes as “failure.” Even though tossing a die has six outcomes, in this experiment that I define, there are only two outcomes, and I can use Bernoulli distribution.

The probability mass function (PMF) of a random variable x that follows the Bernoulli distribution is:

p is the probability that this random variable x equals ‘success,’ which is defined based on different scenarios. Sometimes we have p = 1-p, like when tossing a fair coin.

From the PMF, we can calculate the expected value and variance of random variable x depending on the numerical value of x. If x=1 when “success” and x=0 when “failure,” E (x) and Var (x) are:

Simulating a Bernoulli trial is straightforward by defining a random variable that only generates two outcomes with a certain “success” probability p:

```
import numpy as np
#success probability is the same as failure probability
np.random.choice([‘success’,’failure’], p=(0.5, 0.5))
#probabilities are different
np.random.choice(['success','failure'], p=(0.9, 0.1))
```

Binomial distribution is also a discrete distribution, and it describes the random variable x as the number of success in n Bernoulli trials. You can think of the binomial distribution as the outcome distribution of n identical Bernoulli distributed random variables. The assumptions of the Binomial distribution are:

1, each trial only has two outcomes (like tossing a coin);

2, there are n identical trials in total (tossing the same coin for n times);

3, each trial is independent of other trials (getting “Head” at the first trial wouldn’t affect the chance of getting “Head” at the second trial);

4, p, and 1-p are the same for all trials (the chance of getting “Head” is the same across all trials);

There are two parameters in the distribution, the success probability p and the number of trials n. The PMF is defined using the combination formula:

The probability that we have x number of success out of n trials is like choosing x out of n when order doesn’t matter.

Thinking about Binomial distribution as n identical Bernoulli distributions helps understand the calculation of its expected value and variance:

If you are interested in getting these two equations above, you can watch these wonderful videos from Khan Academy.

Python’s Numpy library has a built-in Binomial distribution function. To simulate it, define n and p, and set to simulate 10000 times:

```
n = 100
p = 0.5
size = 10000
binomial = np.random.binomial(n,p,1000)
plt.hist(binomial)
```

#probability #statistical-analysis #data-science #probability-distributions #simulation

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For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

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The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.

IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.

With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.

Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.

#people #data science aspirants #data science course director interview #data science courses #data science education #data science education market #data science interview

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**Data Science** becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.

Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

**Advantages of Data Science**:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.

**Some Of The Advantages Are Mentioned Below**:-

**Multiple Job Options** :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

**Business benefits**: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.

**Highly Paid jobs and career opportunities**: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.

**Hiring Benefits**:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.

**Also Read: How Data Science Programs Become The Reason Of Your Success**

**Disadvantages of Data Science**: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-

**Data Privacy**: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.

**Cost**:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.

#data science training in noida #data science training in delhi #data science online training #data science online course #data science course #data science training

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Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

*(The jobs are sorted according to the years of experience r*

**Location: **Bangalore

**Skills Required:** Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

**Location: **Chennai

**Skills Required:** Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

**Location:** Bangalore

**Skills Required:** Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

**Location: **Bibinagar, Telangana

**Skills Required:** Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india