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Top 4 Python Industrial Use-Cases: Explained

Top 4 Python Industrial Use-Cases: Explained

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Python is one of the fastest-growing and most popular coding languages in the world; a large number of developers use it on daily basis and why not, it works brilliantly for a plethora of developer job roles and data science positions – starting from scripting solution for sysadmins to supporting machine learning algorithms to fueling web development, Python can work wonders across myriad platforms!

Below, we’ve rounded up 4 amazing Python industrial use-cases; scroll ahead:

Insurance

Widely used in generating business insights; courtesy machine learning.

Case Study:

Smaller firms driven by machine learning gave stiff competition to a US multinational finance and insurance corporation. In return, the insurer formed teams and devised a new set of services and applications based on ML algorithms to enjoy a competitive edge. However, the challenge was that with so many data science tools, numerous versions of Python came into the picture and gave rise to compatibility issues. As a result, the company finalized only one version of Python, which was then used in line with machine learning algorithms and tools to derive specific results.

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Finance

Data mining helps determine cross-sell opportunities.

Case Study:

Another US MNC dealing in financial services showed interest in mining complex customer behavioral data. Using Python, the company launched a series of ML and data science initiatives to dig into its structured data that it has been gathering for years and correlated it with an army of unstructured data, gathered from social media and web to enhance cross-selling and retrieve resources.

Aerospace

Python helps in meeting system deadlines and ensured utmost confidentiality.

Case Study:

Recently, the International Space Station struck a deal with an American MNC dealing in military, defense and aerospace technology; the latter has been asked to provide a series of systems to the ISS. The critical safety systems were mostly written in languages, like Ada; they didn’t fare well in terms of scripting tasks, data science analysis or GUI creation. That’s why Python was chosen; it offered bigger contract value and minimum exposure.

Retail Banking

Enjoy flexible data manipulation and transformation – all with Python!

Case Study:

A top-notch US department store chain equipped with an in-store banking division gathered data and stored it in a warehouse. The main aim of the company was to share the information with multiple platforms to fulfill its supply chain, analytics, retail banking and reporting needs. Though the company chose Python for on-point data manipulation, each division came up with their own versions of Python, resulting in a new array of issues. In the end, the company decided to keep a standard Python; this initiative not only resulted in amplifying engineering speed but also reduced support costs.

As end notes, Python is the next go-to language and is growing each day. If you have dreams of becoming an aspiring programmer, you need to book the best Python Certification Training in Delhi. DexLab Analytics is a premier Python training institute in Delhi; besides Python, it offers in-demand skill development courses for interested candidates.

 

The blog has been sourced from www.techrepublic.com/article/python-5-use-cases-for-programmers

 

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Now Machine Learning Can Predict Premature Death, Says Research

Now Machine Learning Can Predict Premature Death, Says Research

Machine Learning yet again added another feather in its cap; a team of researchers tried and tested a suave machine learning system that can now predict early death. Yes, premature death can now be estimated, courtesy a robust technology and an outstanding panel of researchers from the University of Nottingham! At first, it may sound weird and something straight out of a science fiction novel, but fret not – machine learning has proved itself in improving the status of preventive healthcare and now it’s ready to venture into new unexplored medical territories.

Prediction at Its Best

Published in PLOS ONE in one of their special editions of Machine Learning in Health and Biomedicine, the study delves into how myriad AI and ML tools can be leveraged across diverse healthcare fields. The technology of ML is already reaping benefits in cancer detection, thanks to its sophisticated quantitative power. These new age algorithms are well-equipped to predict death risks of chronic diseases way ahead of time from a widely distributed middle-aged population.

To draw clear conclusions, the team collected data of more than half a million people falling within the age group of 40 and 69 from the UK Biobank. The data collection is from the period 2006-2010, followed up till 2016. With this data in tow, the experts analyze biometric, demographic, lifestyle and clinical factors in each individual subject. Robust machine learning models are used in the process.

Adding in, the team observed dietary consumption of vegetables, fruits and meat per day of each subject. Later, the team from Nottingham University proceeded to predict the mortality of these individuals.

“We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and ‘hospital episodes’ statistics,” says Dr. Stephen Weng, assistant professor of Epidemiology and Data Science.  “We found machine-learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert.”

Accuracy and Outcome

The researchers involved in this ambitious project are excited to the bones. They are eager about the outcomes. They are in fact looking forward to a time where medical professionals would be able to distinguish potential health hazards in patients with on-point accuracy and evaluate the following steps that would lead the way towards prevention. “We believe that by clearly reporting these methods in a transparent way, this could help with scientific verification and future development of this exciting field for health care”, shares Dr. Stephen Weng.

As closing thoughts, the research is expected to build the foundation of enhanced medicine capabilities and deliver customized healthcare facilities tailoring risk management for each individual patient. The Nottingham research draws inspiration from a similar study where machine learning techniques were used to predict cardiovascular diseases.

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In case, you are interested in Machine Learning Using Python training course, DexLab Analytics is the place to be. With a volley of in-demand skill training courses, including Python certification training and AI training, we are one of the best in town. For details, check out our official website RN.

 
The blog has been sourced from
interestingengineering.com/machine-learning-algorithms-are-now-able-to-predict-premature-death
 


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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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Decoding the Equation of AI, Machine Learning and Python

Decoding the Equation of AI, Machine Learning and Python

AI is an absolute delight. Not only is it considered one of the most advanced fields in the present computer science realm but also AI is a profit-spinning tool leveraged across diverse industry verticals.

In the past few years, Python also seems to be garnering enough fame and popularity. Ideal for web application development, process automation, web scripting, this wonder tool is a very potent programming language in the world. But, what makes it so special?

Owing to ease of scalability, learning and adaptability of Python, this advanced interpreted programming language is the fastest growing global language. Plus, its ever-evolving libraries aid it in becoming a popular choice for projects, like mobile app, data science, web app, IoT, AI and many others.

Python, Machine Learning, AI: Their Equation

Be it startups, MNCs or government organizations, Python seem to be winning every sector. It provides a wide array of benefits without limiting itself to just one activity – its popularity lies in its ability to combine some of the most complex processes, including machine learning, artificial intelligence, data science and natural language processing.

Deep learning can be explained as a subset of a wider arena of machine learning. From the name itself you can fathom that deep learning is an advanced version of machine learning where intelligence is being harnessed by a machine generating an optimal or sub-optimal solution.

Combining Python and AI

Lesser Coding

AI is mostly about algorithms, while Python is perfect for developers who are into testing. In fact, it supports writing and execution of codes. Hence, when you fuse Python and AI, you drastically reduce the amount of coding, which is great in all respects.

Encompassing Libraries

Python is full of libraries, subject to the on-going project. For an instance, you can use Numpy if you are into scientific computation – for advanced computing, you have put your bet on SciPy – whereas, for machine learning, PyBrain is the best answer.

A Host of Resources

Entirely open source powered by a versatile community, Python provides incredible support to developers who want to learn fast and work faster. The huge community of web developers are active worldwide and willing to offer help at any stage of the development cycle.

Better Flexibility

Python is versatile. It can be used for a variety of purposes, right from OOPs approach to scripting. Also, it performs as a quintessential back-end and successfully links different data structures with one another.

Perfect for Today’s Millennial

Thanks to its flexibility and versatility, Python is widely popular amongst the millennials. You might be surprised to hear that it is fairly easier to find out Python developers than finding out Prolog or LISP programmers, especially in some countries. Encompassing libraries and great community support helps Python become the hottest programming language of the 21st century.

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Some of the most popular Python libraries for AI are:

  • AIMA
  • pyDatalog
  • SimpleAI
  • EasyAI

Want to ace problem-solving skills and accomplish project goals, Machine Learning Using Python is a sure bet. With DexLab Analytics, a recognized Python Training Center in Gurgaon, you can easily learn the fundamentals and advance sections of Python programming language and score goals of success.

 

The blog has been sourced from ― www.information-age.com/ai-machine-learning-python-123477066

 


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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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Top Python Interview Questions You Should Start Preparing RN

Top Python Interview Questions You Should Start Preparing RN

Welcome! May be, you are new to programming altogether or seasoned in the field; either way, you have come to the right place, and have chosen the right language. Python is a very powerful, advanced kind of object-oriented programming language. It is simple and follows an easy to use syntax. Therefore, it has stood out to be the most popular programming language to learn and master, even by those who has set foot recently into the world of computer programming.

So, if you are planning to kickstart a career in Python, pore over these frequently asked questions; they are often asked in job interviews. If you have any doubts regarding Python, feel free to ask us, or comment below.

Or you can also take up our Python Course in Delhi NCR. It’s industry-related and student-friendly.

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What is Python?

Highly readable, interactive and interpreted, Python is an object-oriented scripting language. It focuses on English language, while its competitors use punctuations more.

What are the key features of Python?

  • Dynamically typed – In Python, you don’t have to state the variable kinds. You can easily run x=111  and then x="I'm a string" without error.
  • Interpreted language – This means Python doesn’t have to be compiled before running. PHP and Ruby are other interpreted languages.
  • Object oriented programming – Python is highly suitable for such kind of programming. It lacks access specifiers (like C++’s public, private>).

What role does PYTHONPATH environment variable plays?

PYTHONPATH works similar to a PATH. It helps Python interpreter in locating the module files imported into a program. It includes Python source library directory as well as the directories comprising the Python source code.

Name the supported data types in Python.

Python has 5 supported data types:

  • Numbers
  • List
  • String
  • Dictionary
  • Tuple

Do you know how to display contents of text file in a reverse order?

  • Change the given file into a list,
  • Then reverse the list by using reversed()
  • Eg: for line in reversed(list(open(“file-name”,”r”))):
  • Print(line)

Pick out the invalid statement from below.

  1. a) abc = 1,000,000
  2. b) a b c = 1000 2000 3000
  3. c) a,b,c = 1000, 2000, 3000
  4. d) a_b_c = 1,000,000

 Answer: b

How do you manage memory in Python?

  • Python private heap space manages its memory. All Python items and data structures are stored in a private heap. The programmer is not given access to this, but an interpreter manages this heap.
  • Pythom memory manager allocates Python heap space, while giving some access to the programmer for coding.
  • Python comprises of an inbuilt garbage collector – it recycles all the redundant memory, frees the extra memory and feeds it into the heap space.

What do you mean by Python dictionary?

Python dictionary is nothing but built-in datatypes. It entails a one-to-one relationship between keys and values. Dictionaries are indexed by keys; they contain pairs of keys and their corresponding values.

Hope this set of Python interview questions have prepared you well for upcoming job interviews. All the best!

Want to learn Python and its whole set of applications? Feel free to check out our interactive Python certification course training, offered by industry experts. They help transform your career.

 

The blog has been sourced from:

www.edureka.co/blog/interview-questions/python-interview-questions

intellipaat.com/interview-question/python-interview-questions

 

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3 Most Used Data Science Tools in 2018

The humongous amount of data calls for advanced data science tools – to completely understand and analyze the information.

Data analytics fuels digital transformation. The best way to do this is by arming an expert pool of statisticians, math pundits and business analysts with suitable data science tools with which they can squelch out crucial insights from the ever-growing silos of corporate data. This kind of initiatives promote a data-driven business culture, which acts as a present prerequisite – and this why here we’ve jotted down top 3 data science tools that’s weaving wonders with the new oil of the world, data:

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Python

Both, well-performing software and a powerful programming language perfect for developing custom algorithms, Python is the most must-have tool for all data scientists. In a recent KDnuggets survey of 2052 users, Python language was recommended by 65.6% of respondents.

“We use Python both for data science and back end, which provides us with rapid development and machine learning model deployment,” shared Alexander Osipenko, lead data scientist at Cindicator Inc. “It’s also of great importance for us to ensure the security of implemented tools.”

Leslie De Jesus, innovation director and lead data scientist at Wovenware emphasized on the importance of Python libraries. “[We use] Python Libraries, including Scrapy, for web scraping and being able to extract data from the internet and upload it into a data frame for analysis,” said De Jesus.

Few others vouched for Python because of its multifaceted nature and strong optimization skills.

For Python Certification Training in Delhi, drop by DexLab Analytics.

R

Quite similar to Python, R is the go-to programming language for many data scientists and they depend on it wholly because it’s simpler and more specifically-built for data science. According to the KDnuggets poll, 48.5% respondents voted it to be one of the leading data science tools.

As for all, R programming language is blessed with cultivated capabilities for machine learning and statistics, and professionals love using it. It’s another favorite of data analysts, especially those who deals with a lot of data exploration.

“I can quickly see summary stats like mean, median and quartiles; quickly create different graphs; and create test data sets, which can be easily shared and exported to CSV format,” said Jon Krohn, chief data scientist at Untapt Inc.

Seeking R language certification in Delhi? We have DexLab Analytics for you!

Tableau

Bridging the gap between skilled data science teams and more business-oriented analytics consultants, Tableau Software is the fastest data visualization and dashboard tool. “It is a fantastic tool for data scientists and noobs working on data science,” said Pooja Pandey, senior executive for SEO at Entersoft Security. “[It’s a] quick dashboarding tool to visualize insights and analytical data with a very short learning curve.”

The lightening speed of Tableau’s visualization and reporting functions is commendable. It’s easy to learn, quick to implement and intuitive to use. Moreover, it helps different segments of a company to customize exhaustive reports according to their requirements.

Now, if you are looking for ways to hone your visualization skills, we would recommend Tableau BI training courses from DexLab Analytics. Their training courses are comprehensive, well-research and as per industry standards.

 

The blog has been sourced fromsearchbusinessanalytics.techtarget.com/feature/Data-scientists-weigh-in-5-data-science-tools-to-consider

 

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Periscope Data Adds Python, R and SQL on A Single Platform for Better, Powerful Data Analysis

Periscope Data Adds Python, R and SQL on A Single Platform for Better, Powerful Data Analysis

Recently, a veteran data analytics software provider, Periscope Data announced some brand new developments while updating their Unified Data Platform for Python, R programming and Structured Query Language. This new Unified Data Platform will enable data professionals to work in sync with 3 key skills all on a single platform.  Far more better analysis will be conducted using less time by altering data in SQL, executing complex statistical analyses in Python or R, followed by improved visualization, collaboration and reporting of results – all performed on Periscope’s dynamic analytics platform.

A massive data explosion is taking place around the world around us. More than 90% of the world’s data has been created in the past two years, and the numbers are still on the rise. To this, new levels of sophistication needs to be added to analyze the complexity of data – “The addition of Python and R support to our Unified Data Platform gives our customers a unique combination of tools – from machine learning to natural language processing to predictive analytics, analysts will be able to answer new questions that have yet to be explored,” says Harry Glaser, co-founder and CEO of Periscope Data.

The inclusion of Python and R support in Periscope framework comes with ample benefits, and some of them are highlighted below:

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All data at a single place

Instead of relying on several data sources, Periscope Data prefers to combine data together collected from various databases to bring them to a single platform, where nothing but a single source of truth for data is established. The data collected is updated and in crisp format.

Predictive analytics

It’s time to leverage Python and R libraries and move beyond the conventional historical reporting for the sake of modeling predictions. With lead scoring and churning prediction, businesses are now in a better position to derive significant insights about a future of a company.

No more switching between tools

Seamlessly, users can switch between querying data in SQL and analyzing data in R or Python, all at the same time on a same platform. Data professionals will be able to modify their datasets, enhance the performance of their models and update visualizations from a single location.

Mitigate data security concerns

The integration of R, Python and SQL by Periscope Data ensures the data professionals can run and share all sorts of models securely and in full compliance with all the norms, instead of seeking open source tools. Periscope Data is SOC2 and HIPAA compliant. It performs regular internal audits to check compliance requirements and safety issues.

Efficient collaboration with teams

As all the analysis takes place in a central location, be sure all your insights will be thoroughly consistent, secure and free of any version-control issues. Also, Periscope Data allows you and your team members the right to read and write access when required.

Easy visualization of analysis

To develop powerful visualizations that reach one’s heart and mind, leverage Periscope’s resources to the optimum levels. Data teams allow users to easily visualize through R packages and Python libraries so as to nudge users to explore the better horizons of data.

To learn more about R programming or Python, opt for Python & Spark training by DexLab Analytics. R language certification in Delhi NCR empowers students and professionals to collaborate and derive better insights faster and efficiently.

 

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