21 Open Source Python Libraries You Should Know About

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The probability that you must have heard of ‘Python’ is outright. Guido Van Rossum’s brainchild – Python, which dates back to the ’80s has become an avid game changer. It is one of the most popular coding languages today and is widely used for a gamut of applications. In this article, we have listed 21 Open Source Python Libraries you should know about.

What is a Library

A library is a collection of pre-combined codes that can be used iteratively to reduce the time required to code. They are particularly useful for accessing the pre-written frequently used codes, instead of writing them from scratch every single time. Similar to the physical libraries, these are a collection of reusable resources, which means every library has a root source. This is the foundation behind the numerous open-source libraries available in Python. 

Let’s Get Started!

1. Scikit- learn: It is a free software machine learning library for the Python programming language and can be effectively used for a variety of applications which include classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and preprocessing.

Scikit-learn requires:

  • Python (>= 2.7 or >= 3.3),
  • NumPy (>= 1.8.2),
  • SciPy (>= 0.13.3).

21 open source python libraries you should know

Spotify uses Scikit-learn for its music recommendations and Evernote for building their classifiers. If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip.

2. NuPIC: The Numenta Platform for Intelligent Computing (NuPIC) is a platform which aims to implement an HTM learning algorithm and make them public source as well. It is the foundation for future machine learning algorithms based on the biology of the neocortex. Click here to check their code on GitHub.

3. Ramp: It is a Python library which is used for rapid prototyping of machine learning models. Ramp provides a simple, declarative syntax for exploring features, algorithms, and transformations. It is a lightweight pandas-based machine learning framework and can be used seamlessly with existing python machine learning and statistics tools.

4. NumPy: When it comes to scientific computing, NumPy is one of the fundamental packages for Python providing support for large multidimensional arrays and matrices along with a collection of high-level mathematical functions to execute these functions swiftly. NumPy relies on BLAS and LAPACK for efficient linear algebra computations. NumPy can also be used as an efficient multi-dimensional container of generic data.

21 open source python libraries you should know

The various NumPy installation packages can be found here.

5. Pipenv: The officially recommended tool for Python in 2017 – Pipenv is a production-ready tool that aims to bring the best of all packaging worlds to the Python world. The cardinal purpose is to provide users with a working environment which is easy to set up. Pipenv, the “Python Development Workflow for Humans” was created by Kenneth Reitz for managing package discrepancies. The instructions to install Pipenv can be found here.

6. TensorFlow: The most popular deep learning framework, TensorFlow is an open-source software library for high-performance numerical computation. It is an iconic math library and is also used for machine learning and deep learning algorithms. Tensorflow was developed by the researchers at the Google Brain team within Google AI organisation, and today it is being used by researchers for machine learning algorithms, and by physicists for complex mathematical computations. The following operating systems support TensorFlow: macOS 10.12.6 (Sierra) or later; Ubuntu 16.04 or later; Windows 7 or above; Raspbian 9.0 or later.

7. Bob: Developed at Idiap Research Institute in Switzerland, Bob is a free signal processing and machine learning toolbox. The toolbox is written in a mix of Python and C++. From image recognition to image and video processing using machine learning algorithms, a large number of packages are available in Bob to make all of this happen with great efficiency in a short time.

8. PyTorch: Introduced by Facebook in 2017, PyTorch is a Python package which gives the user a blend of 2 high-level features – Tensor computation (like numpy) with strong GPU acceleration and developing Deep Neural Networks on a tape-based auto diff system. PyTorch provides a great platform to execute Deep Learning models with increased flexibility and speed built to be integrated deeply with Python.

9. PyBrain: PyBrain contains algorithms for neural networks that can be used by entry-level students yet can be used for state-of-the-art research. The goal is to offer simple, flexible yet sophisticated and powerful algorithms for machine learning with many pre-determined environments to test and compare your algorithms. Researchers, students, developers, lecturers, you and me – we can all use PyBrain.

21 Open Source Python Libraries you should know

10. MILK: This machine learning toolkit in Python focuses on supervised classification with a gamut of classifiers available: SVM, k-NN, random forests, decision trees. A range of combination of these classifiers gives different classification systems. For unsupervised learning, one can use k-means clustering and affinity propagation. There is a strong emphasis on speed and low memory usage. Therefore, most of the performance-sensitive code is in C++. Read more about it here.

11. Keras: It is an open-source neural network library written in Python designed to enable fast experimentation with deep neural networks. With deep learning becoming ubiquitous, Keras becomes the ideal choice as it is API designed for humans and not machines according to the creators. With over 200,000 users as of November 2017, Keras has stronger adoption in both the industry and the research community even over TensorFlow or Theano. Before installing Keras, it is advised to install TensorFlow backend engine.

12. Dash: From exploring data to monitoring your experiments, Dash is like the frontend to the analytical Python backend. This productive Python framework is ideal for data visualization apps particularly suited for every Python user. The ease which we experience is a result of extensive and exhaustive effort. 

13. Pandas: It is an open-source, BSD licensed library. Pandas enable the provision of easy data structure and quicker data analysis for Python. For operations like data analysis and modelling, Pandas makes it possible to carry these out without needing to switch to more domain-specific language like R. The best way to install Pandas is by Conda installation 

21 open source python libraries you should know about

14. Scipy: This is yet another open-source software used for scientific computing in Python. Apart from that, Scipy is also used for Data Computation, productivity, and high-performance computing and quality assurance. The various installation packages can be found here. The core Scipy packages are Numpy, SciPy library, Matplotlib, IPython, Sympy, and Pandas.

15. Matplotlib: All the libraries that we have discussed are capable of a gamut of numeric operations but when it comes to dimensional plotting, Matplotlib steals the show. This open-source library in Python is widely used for publication of quality figures in a variety of hard copy formats and interactive environments across platforms. You can design charts, graphs, pie charts, scatterplots, histograms, error charts, etc. with just a few lines of code. 

21 open source python libraries you should know

The various installation packages can be found here.

16. Theano: This open-source library enables you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. For a humongous volume of data, handcrafted C codes become slower. Theano enables swift implementations of code. Theano can recognise unstable expressions and yet compute them with stable algorithms which gives it an upper hand over NumPy. Follow the link to read more about Theano. The closest Python package to Theano is Sympy. So let us talk about it.

17. SymPy: For all the symbolic mathematics, SymPy is the answer. This Python library for symbolic mathematics is an effective aid for computer algebra system (CAS) while keeping the code as simple as possible to be comprehensible and easily extensible. SimPy is written in Python only and can be embedded in other applications and extended with custom functions. You can find the source code on GitHub. 

18. Caffe2: The new boy in town – Caffe2 is a Lightweight, Modular, and Scalable Deep Learning Framework. It aims to provide an easy and straightforward way for you to experiment with deep learning. Thanks to Python and C++ API’s in Caffe2, we can create our prototype now and optimize later. You can get started with Caffe2 now with this step-by-step installation guide.

19. Seaborn: When it comes to visualisation of statistical models like heat maps, Seaborn is among the reliable sources. This Python library is derived from Matplotlib and closely integrated with Pandas data structures. Visit the installation page to see how this package can be installed

20. Hebel: This Python library is a tool for deep learning with neural networks using GPU acceleration with CUDA through pyCUDA. Right now, Hebel implements feed-forward neural networks for classification and regression on one or multiple tasks. Other models such as Autoencoder, Convolutional neural nets, and Restricted Boltzman machines are planned for the future. Follow the link to explore Hebel.

21. Chainer: A competitor to Hebel, this Python package aims at increasing the flexibility of deep learning models. The three key focus areas of chainer include :

a. Transportation system: The makers of Chainer have consistently shown an inclination towards automatic driving cars and they have been in talks with Toyota Motors about the same.

b. Manufacturing industry: From object recognition to optimization, Chainer has been used effectively for robotics and several machine learning tools.

c. Bio-health care: To deal with the severity of cancer, the makers of Chainer have invested in research of various medical images for early diagnosis of cancer cells.

The installation, projects and other details can be found here.

So here is a list of the common Python Libraries which are worth taking a peek at and if possible familiarizing yourself with. If you feel there is some library which deserves to be in the list do not forget to mention it in the comments.

 

The quality of faculty at GL is unmatched compared to other institutes – Venkatesh Radhakrishnan, Sr. Research Analyst at RRD

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From an amateur in Data Science to a Hackathon winner, Venkatesh has come a long way in his career. He was a newbie in this field since he’s from a commerce background, but he still didn’t lose confidence or direction throughout the course. Here’s how he did it: 

What is your professional background?

I completed my graduation with B.Com in Informations System Management from Ramakrishnan Mission Vivekananda College, Chennai by 2015. Then, I started as a Research Associate in RRD – Global outsourcing solutions, APAC. In 2017, I took a BABI course with GL. Currently, I am working as a senior research analyst at RRD.

How did you develop an interest in this course? Why did you choose Great Learning?

As soon as I started working in RRD as a research associate, I got aware of the Analytics field in terms of information & intelligence value. As a market researcher, I developed an immediate interest in this field and started looking for institutes that provided courses in this stream. I came across GL and it’s brand value. I got in touch with the team, got interviewed for the course and cracked admissions in BABI course at GL. 

How did you transition from a Research to a Data Science role?

During my time at GL, I spoke to my organization about my course and requested them to let me explore my skills in order to check applicable areas. After their approval, we started developing a proof of concept for clients; which turned pretty well. In the past 2 years, as a company, we have come a long way in terms of our practice and implementation of analytical tools. I feel privileged to be a part of an organization where through the help and support of management, I could transition to the Data Science field.

What did you think was the best thing about this program?

The best thing about this course is it provides flexibility in terms of learning things at one’s own pace. The course and curriculum are designed to promote learning and not spoon-feeding. There is an ample amount of time to learn, understand, practice and implement the concepts. The quality of faculty at GL is unmatched compared to other institutes.

What would be your advice to the future aspirants?

The advice would be to develop key skills in identifying areas where analytical tools can be applied to make things easy and profitable. One needs to study and research properly to understand the deployment of tools in favour of Business.

Upskill with Great Learning’s PG program in Business Analytics and Business Intelligence and unlock your dream career.

Your essential weekly guide to Data and Business Analytics 

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By 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy, according to Gartner. The march of analytics into the collective consciousness of businesses around the world is unstoppable now, and the implications are far-reaching. From a glut of new skills that employees need to learn, to the shiny new applications of Analytics that’s changing the way humans live, there’s quite a lot of activity going on here. We try to make sense of all that news in our digest that encapsulates the Analytics landscape.

Here are some articles that will take you through recent advancements in the data and analytics domains. 

Businesses Face Three Biggest Challenges While Leveraging Big Data

According to a report from Dun & Bradstreet, the three biggest challenges businesses still face when it comes to leveraging big data are protecting data privacy, having accurate data, and Analysing/processing data. The global big data market was estimated at $23.56 billion in 2015 and now is expected to reach $118.52 billion by 2022.

Big Data & Business Analytics Market to Rear Excessive Growth During 2015 to 2021

Due to the tremendous increase in organizational data the adoption of big data and business analytics has been increased within organizations to better understand their customer and drive efficiencies. Read more to know about Drivers and Challenges of Big Data and Business analytics market. 

‘Jeopardy!’ Winner Used Analytics to ‘Beat the Game’

An aggressive strategy, mathematical finesse, a sharp mind, and a willingness to take risks were some of the factors that spurred ‘Jeopardy!’ game-show contestant James Holzhauer to win 32 consecutive games and rake in more than $2.4 million. Read more to know how this happened. 

The Age of Analytics: Sequencing’s New Frontier is Clinical Interpretation

Today, genomic data is being generated faster than ever before. And those on the frontier of this field are trying to make sure that data is as useful as possible. While the surge in sequencing has benefited many patients, the genomic data avalanche has caused its own problems. Read more about the challenges and proposed solutions to manage and analyze the volumes of genomic data. 

Times Techies: Upskilling is Key to Meeting Demand For Analytics

An exhaustive Nasscom-Zinnov report released last year flags a huge talent demand-supply gap in the artificial intelligence (AI) and big data analytics (BDA) family of jobs. By 2021, the total AI and BDA job openings in India is estimated to go up by 2,30,000. But the fresh employable talent or university talent available will be just 90,000, leaving a huge gap of 1,40,000. 

Happy Reading!

10 Most Common Business Analyst Interview Questions

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Preparing for a Business Analyst Job Interview? Here are a few tips and the most useful and common business analyst interview questions that you might face. 

Before attending an interview for a business analyst position, one should be through about their previous experience in the projects handled and results achieved. The types of questions asked generally revolve around situational and behavioural acumen. The interviewer would judge both knowledge and listening skills from the answers one presents. 

The most common business analyst interview questions are:

 

1. How do you categorize a requirement to be a good requirement?

A good requirement is the one that clears the SMART criteria, i.e., 

Specific – A perfect description of the requirement, specific enough to be easily understandable

Measurable – The requirement’s success is measurable using a set of parameters

Attainable – Resources are present to achieve requirement success

Relevant – States the results that are realistic and achievable

Timely – The requirement should be revealed in time 

business analyst interview questions

 

2. List out the documents used by a Business Analyst in a project?

The various documents used by a Business Analyst are:

a. FSD – Functional Specification Document

b. Technical Specification Document

c. Business Requirement Document 

d. Use Case Diagram

e. Requirement Traceability Matrix, etc.

 

3. What is the difference between BRD and SRS?

SRS (Software Requirements Specifications) – is an exhaustive description of a system that needs to be developed and describes the software – user interactions. While a BRD (Business Requirements Document) is a formal agreement for a product between the organization and the client. 

The difference between the two are:

business analyst interview questions

 

4. Name and briefly explain the various diagrams used by a Business Analyst.

Activity Diagram – It is a flow diagram representing the transition from one activity to another. Here activity is referred to the specific operation of the system.

Data Flow Diagram – It is a graphical representation of the data flowing in and out of the system. The diagram depicts how data is shared between organizations

Use Case Diagram – Also known as Behavioural diagram, the use case diagram depicts the set of actions performed by the system with one or more actors (users).

Class Diagram – This diagram depicts the structure of the system by highlighting classes, objects, methods, operations, attributes, etc. It is the building block for detailed modelling used for programming the software.

Entity Relationship Diagram – It is a data modelling technique and a graphical representation of the entities and their relationships. 

Sequence Diagram – It describes the interaction between the objects. 

Collaboration Diagram – It represents the communication flow between objects by displaying the message flow among them.

 

5. Name different actors in a use case diagram?

Broadly, there are two types of actors in a use-case:

a. Primary Actors – Start the process

b. Secondary Actors – assist the primary actor

They can further be categorized as:

i. Human

ii. System

iii. Hardware

iv. Timer

 

6. Describe ‘INVEST’.

The full form of INVEST is Independent, Negotiable, Valuable, Estimable, Sized Appropriately, Testable. With this process, the technical teams and project managers to deliver quality products or services.

 

7. What is Pareto Analysis

Also known as the 80/20 rule, Pareto Analysis is an effective decision-making technique for quality control. As per this analysis, it is inferred that 80% effects in a system are a result of 20% causes, hence the name 80/20 rule.

 

8. Describe the Gap Analysis.

It is utilized to analyze gaps between the existing system and its functionalities against the targeted system. The gap is inferred to the number of changes and tasks that need to be brought in to attain the targeted system. It compares performance between the present and the targeted functionalities.

 

9. Name different types of gaps that could be encountered while Gap Analysis

There are mainly four types of gaps:

a. Performance Gap – Gap between expected and actual performance

b. Product/ Market Gap – Gap between budgeted and actual sales numbers

c. Profit Gap – Variance between targeted and actual profit

d. Manpower Gap – Gap between required and actual strength and quality of the workforce in the organization

 

10. What are the various techniques used in requirement prioritization?

Requirement prioritization, as the name suggests, is a process of assigning priorities to the requirements based on business urgency in different schedules, phases, and cost among others.

The techniques for requirement prioritization are:

a. Requirements Ranking Method

b. Kano Analysis

c. 100 Dollar Method

d. MoSCoW Technique

e. Five Whys

 

Stay tuned to this page for more such information on interview questions and career assistance. If you are not confident enough yet and want to prepare more to grab your dream job as a Business Analyst, upskill with Great Learning’s PG program in Business Analytics and Business Intelligence, and learn all about Business Analytics along with great career support.

Data and Analytics Weekly Round-up: July 9, 2019

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Here are a few Data and Analytics updates from last week to keep you informed.

4 Challenges with Leveraging Analytics — and How to Overcome Them

To fully capitalize on the potential of modern analytics, enterprises must balance a complex mix of technical, organizational and cultural requirements. With this complexity come possible roadblocks that can hinder efforts to gain competitive advantage and also dilute returns on investments. Read along how to combat them.

Revenues from Big Data and Business Analytics to Hit $260 bn in 2022: IDC

Worldwide revenues for Big Data and Business Analytics (BDA) solutions will reach $260 billion in 2022 with a compound annual growth rate (CAGR) of 11.9 percent over the 2017-2022 period, according to a new forecast from International Data Corporation (IDC)…. [Read More]

What Matters Most in Business Intelligence, 2019

Improving revenues using BI is now the most popular objective enterprises are pursuing in 2019. Reporting, dashboards, data integration, advanced visualization, and end-user self-service are the most strategic BI initiatives underway in enterprises today…. [Read More]

The Coolest Business Analytics Companies of the 2019 Big Data 100

As part of the 2019 Big Data 100, CRN has put together a list of business analytics software companies offering everything from simple-to-use reporting and visualization tools to highly sophisticated software for tackling the most complex data analysis problems…. [Read More]

Top Five Business Analytics Intelligence Trends for 2019

From explainable AI to natural language humanizing data analytics, James Eiloart from Tableau gives his take on the top trends in business analytics intelligence as we head into 2019…. [Read More]

 

Happy Reading!