Software development Life cycle (SDLC) is a process of producing high-quality software at the lowest cost and in possibly less time. Generally, SDLC has well-tested and ready-to-use phases which provide an organization to help in creating high-quality software. ISO/IEC 12207 is an international standard of software life cycle process. This standard defines all the tasks which need to develop and maintain software. SDLC targets to produce high-quality software by meeting the expectations of clients within the time limit and in budget. It is made up of a plan which describes how to develop, maintain, alter, and improve the software.

Why we need SDLC

Basically, SDLC is a method with the process, which helps in creating high-quality software. By this you can understand the whole criteria of producing effective software, that’s why SDLC is important. Without SDLC you can’t create a standard software because it gives a standard way to produce an effective and efficient software that will run in the market with client expectations which will help him in managing his part of work. With time we update the software as per customer feedbacks to get a better result which is also a part of SDLC.

Benefits of the Software Development Lifecycle

• Forms the base for project planning.
• Helps to estimate cost and time.
• It gives the clarity of the project and the development process.
• Enhance the speed and accuracy of development progress.
• Minimizes the risks and maintenance during the project.
• Its given standard improves client relations.
• SDLC implement checks to ensure that the software is well tested before being installed in greater source code
• Developers can’t move to the next step until the prior one is completed by SDLC.

What are the SDLC phases

Software Development Life Cycle Models

Waterfall Model:-

A little too old and harsh model is the Waterfall model. It is one of the old-fashioned SDLC models that is not much preferred in the modern software development ecosystem.

The reason it is not favored much is that it runs on a very inflexible structure conditioning that the entire set of requirements should be laid down from the very beginning of a project. This limits the freedom and flexibility of the actual design and development of software.

After completing the development, the product goes through the test for meeting its initial requirements. If it is not good enough, it is to be restructured, which is a lot of work.

Usually, software development companies resist dealing with Waterfall though it still seems to be an effective model for the handful of projects.

RAD Model:-

The rapid Application Development (RAD) process is an adoption of the waterfall model. It aims to developing software in a short period. The RAD model is based on prototyping and iterative development with no specific planning involved. The process of writing the software itself involves the required planning for developing the product. The RAD model is based on the concept that a better system can be developed in less time by using focus groups to collect system requirements

• Business Modeling
• Data Modeling
• Process Modeling
• Application Generation
• Testing and Turnover

 Spiral Model:-

The spiral model is a risk-based process model. This SDLC model helps the group to adopt elements of one or more process models like waterfall, incremental, etc. The spiral technique is a combination of fast prototyping and concurrency in design and development activities. The following will explain the typical uses of a Spiral Model –

• When there is a budget compellable and risk evaluation is important.
• For intermediate to high-risk projects.
• Long-term project commitment because of probable changes to economic priorities as the requirements change with time.
• Customer is not sure of their requirements which is ordinarily the case.
• Requirements are complicated and need evaluation to get clarity.
• Some changes are expected in the product during the development cycle.

V-Model :-

In this model execution of processes happens in a sequential method in a ‘V-shape’. It is also known as ‘Verification and Validation model’. The V-Model is an Expansion of the waterfall model and is based on the association of a testing phase for each related development stage. That means for every single phase in the development cycle, there is a directly associated testing stage. This is a disciplined model and the next phase starts only after completion of the previous phase.

Incremental Model :-

The incremental model is not a distinct model. It is radically a series of waterfall cycles. The requirements are divided into groups at the initial stage of the project. For each group, the SDLC model is adhered to develop software. The SDLC process repeats with each release adding more functionality till all requirements are met.

use of  Incremental Model:-

• When the requirements are much superior.
• A project has a lengthy development program.
• When Software team are not well skilful or trained.
• When the customer demands an immediate release of the product.
• You can develop precedence requirements first.

Agile Model :-  

The agile model is a model which promotes continuous interaction of development and testing during the SDLC process of any project. The agile model is a combination of iterative and incremental process models with aims on process and customer satisfaction by continuous delivery of working software products. Agile Methods have divided the product into small incremental builds. These builds are issued in iterations. Each iteration lasts from typically one to three weeks. Every iteration involves cross-functional teams working together on various areas like

• Planning
• Requirements Analysis
• Design
• Coding
• Unit Testing and
• Acceptance Testing.

At the end of the iteration, a functional product is displayed to the customer.

Iterative Model :-

In the iterative model, the iterative process starts with the implementation of a small set of software requirements, makes  enhancements in the evolving versions till the complete system is implemented and ready to deploy on the market. In this model development of the life cycle doesn’t start with full requirements, instead, it begins with the implementation of just a part of the software, which will be reviewed to identify further requirements later. This process is repeated till the new version of the software is produced at the end.

Big bang model :- 

The big bang model comprises focusing all types of possible resources in software development and coding with little bit or no planning. This model works best for small projects with the smaller size development team who works together. It is useful in academic software projects as well. It is also an ideal model where requirements are either unknown or a final release date is not provided.

Advantages of the Big Bang Model

• This is very easy to use model
• Little bit or no planning required
• Easy to handle
• Very few resources are needed
• provides flexibility to developers

Disadvantages of the Big Bang Model

• Very High risk & uncertainty.
• Not a good model for difficult and object-oriented projects.
• Poor model for long-term and ongoing projects.
• Can become very expensive if requirements are not properly understood.

Prototype Model :-

The prototype model starts with the gathering of required information to start the development process. In this the developer meets the client, understand the purpose of software and identify the actual requirement. Then a quick design is created, focused on each aspect of the software which will be visible to the user. Then it goes ahead with the development of prototype, customer checks and try to identify if any modification needs to be done. In this step, looping occurs and better versions of prototype are created. It continuously happens being in touch with client to show him if any further requirements needs to be done.  This process remains continue till the user is satisfied. Once the user is satisfied, the prototype is converted into the actual system to deploy in market.

DevOps:-  Let’s understand. In the agile Model, both Development and testing activities were concurrent, unlike the waterfall model. It was lost on practices that didn’t come up to speed with agile practices. Due to lack of collaborations between developers ad the operations team, slow down the development process and releases. Then software companies started realizing the need for better collaboration between teams and faster delivery of software. It gave birth to the DevOps approach. DevOps enabled fast software delivery with minimum problems to fix and faster resolution of problems. The term DevOps is deprived of two words development and operations. DevOps is a practice that allows a single team to manage the whole application development life cycle, i.e. development, testing, deployment, etc. The aim of DevOps is to shorten the development life cycle. DevOps is a software development approach that helps in producing high-quality software with reliability and in less time. DevOps is a software development method that aims at communication, integration, and collaboration between IT professionals to enable continuous deployment of products.

Which SDLC Model is Best

As far I have understood, DevOps is the best model in today’s software ecosystem which provides a better development life cycle with high effectiveness and efficiency in work progress.  But it doesn’t mean rest models are not useful, they are also useful and still getting used by some organizations who feel that model is best in their work. DevOps is a practice of bringing development and operation teams together whereas, Agile refers to the continuous iterative approach, which aims at collaboration, customer feedback, small, and continuous releases. DevOps’ purpose is to manage end-to-end engineering processes. It helps in increasing an organization’s speed to deliver applications and services.  The agile purpose is to manage difficult projects. The agile development process divides the product into smaller pieces and integrates them for final testing. It can be implemented in many ways, including Scrum, XP, etc.

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1| Natural Language Processing

About: This online course covers from the basic to advanced NLP and it is a part of the Advanced Machine Learning Specialisation from Coursera. You can enroll this course for free where you will learn about sentiment analysis, summarization, dialogue state tracking, etc. The topics you will learn such as introduction to text classification, language modelling and sequence tagging, vector space models of semantics, sequence to sequence tasks, etc. Upon completing, you will be able to build your own conversational chat-bot that will assist with search on StackOverflow website.

2| Natural Language Processing By Microsoft

About: This is a self-paced learning course which will give you a thorough introduction to the cutting-edge technologies applied to NLP. The duration of this course is 6 weeks where you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about statistical machine translation, deep reinforcement learning techniques applied in NLP, Vision-Language Multimodal language as well as Deep Semantic Similarity Models (DSSM) and their applications.

You will also learn how to apply deep learning models to solve machine translation and conversation problems, deep structured semantic models on information retrieval and natural language applications, deep reinforcement learning models on natural language applications and deep learning models on image captioning and visual question answering.

3| Natural Language Processing With Deep Learning

About: This is a lecture series on NLP provided by Stanford University where you will have an introduction to the cutting-edge research in deep learning applied to NLP. The minimum duration of the series is 1 hour and the topics included are NLP with deep learning, word vector representations, global vectors for word representation, word window classification and neural networks, backpropagation, dependency parsing, introduction to TensorFlow and other such related topics.

4| Natural Language Processing By Carnegie Mellon University

About: This course is provided by Carnegie Mellon University which covers a variety of ways to represent human languages (like English and Chinese) as computational systems and various ways to exploit those representations to write programs that do neat stuff with text and speech data, like translation, summarisation, extracting information, natural interfaces to databases, conversational agents, etc. The course includes some ideas central to Machine Learning and to Linguistics.

5| Deep Natural Language Processing

About: This is a GitHub repository which contains course on deep NLP by the University of Oxford in the form of lecture slides and videos. This course is focused on recent advances in analysing and generating speech and text using recurrent neural networks. You will be introduced with mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions.

6| Natural Language Processing With Python

About: This is an e-book version of the book Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper. This book is more of a practical approach which uses Python version 3 and you will learn various topics such as language processing, accessing text corpora and lexical resources, processing raw text, writing structured programs, classifying text, analysing sentence structure and much more.

7| NLP For Beginners Using NLTK

About: This is a video series where you will learn about the basics of NLP through NLTK. The video basically concentrates on to the very useful feature in NLP called frequency distribution. You will learn how to calculate, tabulate and plot frequency distribution of words.

8| Speech And Language Processing

About: This is an ebook by authors Dan Jurafsky and James H. Martin where you will learn from the basics to advance of language processing. The topics included here are text normalisation, edit distance, regular expressions, language modelling, logistic regression, vector semantics, neural networks, neural language models, and other such related topics.

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So, you want to build a data science team? Here’s some stuff to think about.

Before long, just like this stock photo, you’ll have a team of weird orange people with big bulbous heads, who can sit around a table looking at an enormous hologram of a simple bar chart.

In this article we will cover:

• Definitions of data science
• The purpose of data science
• How data science teams should integrate into the organisation
• Recruiting for data science
• Team roles

Though Econsultancy is marketing focused, there’s plenty in here to appeal more broadly.

First, an attempt at a definition

It seems trite to say that data science’s applications are broad, but they are. And data science teams come in different forms, within different organisational structures and under different names.

There’s a pretty good Venn diagram developed by Drew Conway which gets to the heart of the ambiguous phrase ‘data science’.

Data science Venn diagram by Drew Conway

In Conway’s words, “The difficulty in defining these skills is that the split between substance and methodology is ambiguous, and as such it is unclear how to distinguish among hackers, statisticians, subject matter experts, their overlaps and where data science fits.”

I recommend heading over to Conway’s article to read more of his thoughts. But the basic takeaway for a layman like me is – there’s a hell of a lot to learn and many different skillsets that can be brought to bear on data.

Whilst data science has many grey edges, it’s probably worth including some fairly dry definitions of two common teams – ‘Big data analytics’ teams and ‘data product’ teams. The former looks for predictive patterns in data without necessarily having a preconceived notion of what they are looking for, and the latter works to implement automated systems that are data-driven.

Data products – Ben Chamberlain, senior data scientist at ASOS, describes a data product as “an automated system that generates derived information about our customers such as predicting their lifetime value. This information is then used to automatically take actions like sending marketing messages or it gets sent to another team who use it for insight.”

If you don’t have any statistical knowledge and you fancy a challenge, you can read one of Chamberlain’s papers about this very ASOS CLV data product.

Big data analytics – IBM gives us a serviceable definition of big data analytics: “..a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency.

“..it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media – much of it generated in real time and in a very large scale.”

Remember, data science must tackle a problem (duh!)

As I read in a Harvard Business Review article, economist and Harvard professor Theodore Levitt once said that “People don’t want to buy a quarter-inch drill, they want a quarter-inch hole.”

The same applies to data science – the business needs to see a solution. It’s another obvious thing to say, but I’m writing it because new(ish) and complicated disciplines such as cognitive computing can temporarily blind marketers to the fact that normal rules of business apply – what is the problem that needs solving? What data can be brought to bear, and how can the data be used to create most value?

This is something summed up very nicely with another trusty Venn diagram on a Juice Analytics article. (The intersection of the three circles is where successful data products live.)

Parry Malm, co-founder of Phrasee (email marketing language generation software), takes a pragmatic tone and warns about employing a data science team before you know exactly what you want to achieve.

“The first step,” he says, “is to really, really, really clearly define what problem you’re trying to solve… only then consider whether or not an analytics team or whatever is the right approach. What you DON’T want to do is to hire 10 ‘data scientists’ or something, and then have a huge working capital hit for an undefined outcome, when the money could potentially be spent better somewhere else.”

How data science should interact with the wider org

Before we move on to all the roles in a data science team and the challenges involved in setting one up, it’s worthwhile considering how the team will interact with the rest of the organisation.

Simply parachuting data scientists into a company ignores the differences in culture and skills between marketing and finance teams, and these statisticians and programmers.

To get full value out of your data science team you need to consider what peripheral roles and processes are needed.

1) Transparency and a customer service culture

The danger is that data products or big data analytics will either be implemented and deliver no business benefit or will be underutilised / underprioritised by a business which fails to recognise their value.

Writing in Harvard Business Review, various members of McKinsey’s analytics teams say there is a need for data teams to operate in a customer service culture.

Again, this all feels pretty obvious but will be integral to success. Is the business ready to accept suggestions from a data-led team? If not, what education is needed in the first instance or how can stakeholders be more involved in the effort?

2) Data-science communication

Science communication generally is a noble cause. In an article in The Guardian in 2016, Richard Holliman reports that it is an undervalued vocation. He writes that “For too long, research has shown that science communication is seen as a second-class option for academics.”

Holliman continues, adding that though science communication has improved, “There is still work to be done to ensure that excellence rather than acceptability becomes the hallmark of these activities. The introduction of new ways to discuss and publish the outputs from research, and alternative mechanisms for reward and recognition suggest that a shift in this direction is underway.”

I’m going off topic here, but there’s a corollary with how data science teamwork is translated within businesses and to the end consumer. There needs to be a surrounding network of skillful communicators.

These communicators can include:

• Data visualisation specialists – To make outcomes more readable and accessible.
• Data strategists – In a recent interview with Econsultancy, Channel 4’s director of consumer insights Sarah Rose described this role as “the bridging point between the data science team, who work on the models that we put into our products, and the rest of the business.” Their knowledge may include some data science and some industry expertise.
• Campaign experts – With knowledge of tech and marketing (could be a developer).
• T-shaped leaders – The leader of the data science team must absolutely be all about data science; it’s integral they be an expert in the field. But if you can also find one with business skills, then all the better.

Idrees Kahloon, data journalist at The Economist says that “Often, the best way to present data is the simplest: people readily understand means, medians and sums. Fancier statistical models appeal to wonks, but are harder to explain to a general audience.”

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