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Day: January 12, 2019

What is Scrum methodology?

Posted on by Imarticus Learning

The Scrum world provides the Scrum DevTeam decision-making capabilities instead of detailing how and when to do it. Scrum trusts the team to self-organize the issue presented to it and resolve criteria like Task definitions, Entry and Exit criteria, Validation criteria etc instead. Scrum is a strategy for software product development helping software developers to work as collaborative teams for the achievement of common business goals like the creation of a market-ready product.

Scrum methodology:

The Scrum framework relies on its team to be collaborative, cross-functional, Scrum organized and task-oriented. Every member of the team participates and contributes to goal realization for the desired outcome under the guidance of an Agile coach to resolve complexities and deliver on time.

As there are no leaders the team Manager is the Product Owner and is able to fully utilize capabilities when taking the idea from concept to a sellable product. It is definitely all about the team effort and each Sprint meeting helps prioritize the Product Owner needs and address issues based on them by team-effort that is both communicative and collaborative.

Scrum Development:

Scrum methodology encourages team planning at the beginning where the team decides on which items they will commit to creating in a sprint product backlog. Agile Scrum sprints then take the issues featured from coding to functionality and test to integrate its focus towards the evolving goal.

By sharing, communicating and collaborating in each Sprint, the product becomes flexible, defect-free, tested and emerges as a market-ready product during the project life-cycle itself. The daily Sprint reviews in a Scrum meet of about 15-minutes enables effective review, corrections, and transitions based on the progress achieved the previous day. The Scrum master is the coordinator who with a scrum certification course ensures team participation and goal focus.

Here is how a task is performed in Scrum.

  • The Scrum DevTeam collaborates to resolve complex issues.
  • The product backlog is discussed by the team to prioritize Product Owner needs and fixes deadlines for committing to produce a market-ready product resolution.
  • Each time-restricted Sprint is reviewed in daily Scrums of not more than 15 minutes.
  • Each daily Scrum reviews tests and corrects the previous day’s progress.
  • On completion of a sellable product, a new Sprint begins.
  • The process continues till the deadlines or budget is complete.

The Scrum process artifacts:

At the end of a Scrum sprint, the team as one delivers the primary product or artifact which should be sellable.
The product backlog artifact lists the functionalities, time chart, and features required to enhance the primary artifact.

The Product Owner’s onus of working the backlog allows the team to work the most value-based feature first. The Scrum Master ensures user stories or client perspectives serve as the basis of product development during Sprint reviews and backlog creation.

The Agile  Scrum methodology creates by team efforts, artifacts like the burndown sprint chart and a release chart listing work accomplished, tested and corrected to ensure timely delivery of a market-ready product.

Main Roles in Scrum methodology:

The Scrum framework works on three roles.

  • The Scrum Team who work in Sprints to produce market-ready products.
  • The Scrum Master ensures the team uses Scrum Agile practices.
  • The (PO)Product Owner or client prioritizes the backlog, manages and coordinates the team efforts.

Wrapping it up the scrum methodology is akin to winning in a car race where the interlocking roles of the driver being the PO, the mechanic being the Scrum Master, the team is responsible for winning the race in a sellable product the race car.

What is the Best Programming Language For Artificial Intelligence Projects?

Posted on by Imarticus Learning

Artificial Intelligence is the hot topic of the last couple of years and is all set to be the science of the future. It has already opened up a realm of possibilities for humans, and by taking advantage of a machine and deep learning, it is no doubt going to play a huge role in the future of humanity. You can do almost anything with this technology – even build apps which can hear, see and react accordingly.

A lot of newcomers are beginning to get into programming for AI, considering how important it is turning out to be. However, with the plethora of options available, it can be difficult to choose a particular language for programming. Let us consider the many languages which are currently being used for AI development.

Python
Currently rising in popularity, it is one of the main languages which come up in how to learn machine learning. Being extremely simple to use and learn, it is preferred by many beginners. Compared to other languages like C and Java, it takes extremely less time for implementation.

Another advantage is that with Python, you can opt for procedural, objective oriented or functional style of programming. There are also a lot of libraries which exist for Python, which make programming considerably easier.

Java
A comparatively older option, it first emerged in 1995 – however, it’s importance has only grown at an unparalleled rate since then. Highly portable, transparent and maintainable, this language also has a large number of libraries to make it easier for the user.

Java is incredibly user-friendly and easy to troubleshoot and debug, and the user can also write code that runs on different platforms with ease. The Virtual Machine Technology implemented in Java is key to this feature, actually. Many Big Data platforms like Apache Spark and Hadoop can be accessed using Java, making it a great all-around option for you.
Julia
Developed by MIT, this language is meant for mathematical analysis and numerical computing to be done in a high-performance fashion. These features make it an amazing choice for AI projects since it was designed keeping the needs of Artificial Intelligence in mind. Separate compilation is done away with, too – however, it is only growing, so it does not have the same number of libraries as the others.

Haskell
Haskell, unlike Java, is a great choice for engaging and working with abstract mathematical concepts. You can create AI algorithms using the expressive and efficient libraries which come with the language, and the language is far more expressive compared to many others.

Probabilistic programming is also a cakewalk since developers are able to identify errors relatively quickly, even during the compile phase of iteration. However, you still cannot expect the same level of support that Java and Python offers.

You will need to learn some machine learning skills, if you are to have a long career in this field – in order to do that, you should check out the big data and machine learning courses on offer at Imarticus Learning.

What Does a Scrum Master Do?

Posted on by Imarticus Learning

The Scrum framework treats the scrum master role as a person with scrum master certification and hence an important coach of Scrum values. While without actual authority, the person in this role has to lead by example and influence the team using the servant-leader example. So powerful is the effect of the Scrum Master role that the role is also known and called as Team Coach, Agile Manager or Coach and iterative Coach or Manager.

Role of a Scrum Master
The team member responsible for implementing Agile principles, values, practices, and processes that the team follows and lives by, is denoted as the Scrum Master.

The Scrum Master is responsible for

  • Protecting team members from external distractions and interruptions.
  • Removal of team obstacles.
  • Ensuring team-dynamics and effectiveness.
  • Establishing excellent relationships between the product owner and team both inter and intra team.

Benefits of having a Scrum Master
The Scrum Master is an ace in the use of Scrum and Agile values having excellent command over team dynamics and able to provide a self-organized team who can use the Agile and Scrum practices to resolve their situations and achieve ongoing team communication and collaboration.

The team’s Scrum Master also has the onus of addressing any obstacles, distractions, disruptions etc. to ensure the team members can concentrate on output and production without any hindrances.

Also, present-day teams comprise of team members who are all experts in their own fields. There needs to be a binding agent and cushion for the team to achieve transparent communication leading to effective collaboration. That role is for the Scrum Master to effectively fulfill.

Common Scrum leadership issues 
With the benefits come to the drawbacks. Ineffective role appreciation of the Scrum Master can lead to hindrances that may include:

  • The assumption that project managers without an agile business analysis course can be effective Scrum Masters.  Most often the failure is in the leadership being a control with commands style very different from the servant-master role envisaged in Scrum practices.
  • Donning the role of Scrum Master without prior experience in prior Agile environs.
  • Expectations of performance and workloads from the Scrum Master with all teams. This fails the premise that teams new to Agile practices and principles perform just as well as the experienced teams. An experienced team needs far less Scrum Master inputs when compared to new teams who have to achieve an effective level of communication and collaboration in Agile roles, practices, and principles.

Scrum Master role-apportioning
There Scrum Framework does not define by skills the levels of mastery in Scrum practices. However, practically the following levels can be adapted based on experience levels.

  • Rotational scrum master: Team members accept scrum master administrative responsibilities on a rotational basis in a lap-by-lap style.
  • Partial scrum master: Where one team member also accepts the scrum master roles along with other team responsibilities.
  • Dedicated full-time scrum master: Teams learning Agile prefer to have one team member designated with the scrum master responsibility for a specified time.
  • Dedicated full-time scrum master with multiple teams: Is a realistic model of a single full-time Scrum Master working with different teams.
  • Agile Coach: This role envisages interventions on a need-basis and works across teams without any restrictions to an assigned team only.

Conclusion:
The Scrum Master is vital to the teams effective functioning. Through effective team management and implementation of Scrum values efficiency, productivity and desired outcomes are achievable.

What are The Scope and Benefits of Professional Agile Scrum Master Certification?

Posted on by Imarticus Learning

Scrum principles on an Agile framework aid CSMs and Scrum teams to deliver shorter production time-boxes, continued and instantaneous feedback used for further learning, and frequent testing and evaluation based corrective actions to achieve quicker release cycles, more market-viable products and successful projects.
Scrum best practices and Agile practices allow prioritization of the product backlog in aiding accomplishing complex tasks rapidly. The flexibility of the suite of principles can be applied across the board of personal, team and business processes to enhance productivity, efficiency and value by repeatedly delivering successful products quicker.
What can let you stand out in the Agile space making you a better choice for the job? The CSM certification from Scrum Alliance is the best way to get recognized as an elite Scrum specialists who can lead and guide Scrum and Agile project teams to project successes.

Completing agile business certification has numerous benefits and is a feather in the cap of any CSM. The certification of Scrum Master is the most transparent measure, appreciated and established by the Scrum Alliance and the entire industrial world.

Benefits Of Agile Scrum Master Certification

Scrum agile certification validates skills and certifies aspirants who are enthusiastic, confident, and open to assuming servile responsibilities in completing various tasks

  • Builds the basics with a strong conceptual foundation:
    Aspirants quickly learn concepts and application of practical situations to produce effective results.

 

  • Organizational benefits:
    These are many and those with Agile and Scrum training are fast proving to be company assets. The rather new application ensures better yields, enhanced resource management, timely insights, lesser times to the market, sellable products after each Sprint and much more. All these enhance and improve profits and productivity with much better ROI figures.

 

  • Employability performance indicator:
    Certification helps you stay marketable and relevant through continuous learning. Certified professionals outscore their peers and the experienced non-certified practitioner since the learning and certification process is practically and implementation oriented.

 

  • Project allocations rise dramatically:
    CSMs are excellent Scrum professionals who make a practice out of ensuring winning teams. While clients love fast delivery, flexibility, and higher quality standards coupled with quick market-ready products release, the CSM and Agile coach learn and gain through helping transform teams to achieve repeated project success.

 

  • Scrum work is a fun living style and is profitable:
    All Scrum team members and stakeholders including the project manager, product owner, CSM and team members adapt to this new flexible method of Scrum work where contribution as a team, communication and collaboration are the main tenets. The Scrum framework is simple to use and enhances personal life outlook and attitudes in life. A CSM is self-enabling and encourages others to follow Scrum to promote efficiency and productivity through desirable outcomes, efficient collaboration, communication and teamwork. Scrum’s different perspective and enabling work-strategy taught at agile certification improves traditional work processes.

 

How Will the Scrum Master Certification Enable You?

1. The agile certification from a bankable training academy like Imarticus Learning will help land better jobs, improve career prospects and enhance payouts besides providing for the option for developmental continued learning.
2. Large multi-nationals and corporations are updating to improve inter-departmental communication, modernize processes, act on user-feedback and transition Lean teams to functional work processes. A CSM with gainful insights, perception and knowledge lead teams to organizational success.
3. The training enables you to use the latest technologies, tools, and resources to have business processes with better-organized teams that cost less time and money. This makes the Certified Scrum professionals be constantly in high demand with the supply of CSMs never being enough. That’s why the industry pays well and ensures it fuels the demand and scope for emerging Scrum professionals.
4. The CSM credential sets you apart as a leader who is able to provide knowledge and expertise far beyond what a typical project manager could contribute, using powerful agile practices.
Designations and evolution of CSMs:

Career Path Of Scrum Master 

  • Scrum Master Entry Level:
    At the entry level expect smaller responsibilities for a single team to implement and deal while using the guiding principles and framework of Scrum Agile practices.

 

  • CSM/Scrum Master:
    The full-fledged CSM can expect to play the roles of a coach, facilitator, motivator, and more who removes hurdles and perceived obstacles from team functioning and thereby improves the production. With just over a years experience you can undertake the CSM role in implementation projects effectively. Besides a higher salary, you can continue adding to your Scrum Score Card with 2 days/year Scrum Master continuing education.

 

  • Senior CSM:
    Multi teams and cross-functional teams may be added responsibilities you get. You can showcase your in-depth knowledge and increased Scrum experience effectively while working in this role either full or part-time and as a fully dedicated or partially dedicated CSM.

 

  • Coach CSM:
    As the CSM coach, you work full-time training other CSMs, transitioning teams, across groups of teams and sites while implementing Agile Scrum practices. Evolving best-practices, updated techniques and teaching the latest editions of the Scrum Guide framework will be added tags.

 

  • Project Manager/Product-Owner:
    Many CSMs who prefer the technicalities of a PO or Manager will find the transition to these roles easy as they already have team leading experiences, Scrum and Agile knowledge and are adept at handling production and organizational timelines and pressures.

Top Reasons to Be a Certified Scrum Master

Certifications enhance and boost your resume and experience. What are the top reasons for the boom in aspirants for CSM certification? Here is a list.
1. Learn Agile and Scrum.
2. Change to the Agile Scrum mindset.
3. Garner Scrum Artifacts knowledge to change roles or become more efficient.
4. Be marketable and improve career prospects.
5. Enable organizational growth.
6. Improve teamwork
7. Flaunt and use Scrum knowledge on peer forums, support groups and team members.
8. Enable project success through efficient Scrum practice.
9. Add a qualification to your career chart.
9. The coveted certification has high-value in terms of esteem and honour.
10. Stand out as a great servant-leader through Scrum practice.
In parting, Scrum is an excellent career choice especially with doing a course at Imarticus and earning your CSM agile certification. What are your set of reasons and why delay?

What are The Different Fields in Data Analytics?

Posted on by Imarticus Learning

One of the most popular technology-empowered jobs out there, data analytics consists of various disciplines in the field of data science. There are plenty of different areas in which data analytics is applied, with the banking sector being the foremost. As the world starts adopting data analytics techniques, there are different jobs that are present in the field of data analytics.

Here are four of the main fields in the data analytics sector:
1.Data analyst:
Some companies use the terms “data scientist” and “data analyst” interchangeably. Data analysts generally work with SQL databases and pull data out of the same. The job also entails becoming a master of Tableau and Excel and occasionally analyze results of A/B testing and leading the Google Analytics account. Other roles can also include reporting dashboard data and producing data visualizations.
2.Data Engineer:
Data engineers are generally bought in when companies start getting a lot of traffic and need someone to set up the infrastructure to move forward. There’s also a need for somebody to provide constant analysis and this job can generally be posted under “Data Scientists” or “Data Engineers” as well.
Data engineers require a decent knowledge of machine learning, and heavy statistics as these are one of the main assets companies look for when they’re starting out themselves. Software engineering skills are seen as more of a secondary requirement during the initial phase. Data engineers generally get to own all their work but won’t have much guidance and could reach a point of stagnation.
3.Machine Learning Engineer:
There are many companies where data ends up being their main product. Data analysts or machine learning will be a huge part of their internal processes here. A machine learning engineer who has an education in statistics, physics or mathematics will have a bigger role in these situations. If they’re looking at continuing in an academic path even afterward, then this is a great role to fulfill.
Most companies which look out for machine learning engineers are consumer-facing and have huge data which they offer out to other companies.
4.Data science generalist:
Companies look for data science generalists to join other data scientists internally. Companies that take interview care about data but aren’t necessarily a data company themselves. They will be on the lookout for individuals who can work on a wide variety of hats, including touch production code, analysis, data visualization and more.
Data science generalists are sought after to fulfill any specific niche which a company feels their team lacks. This can include areas such as machine learning or data visualization for example.
Thus, it’s important that you’re always on the lookout for a job that satisfied your skill set the best. There are so many options available for those interested, and with data analytics shaping the world we live in, it will serve you well if you can find your own niche.
Join Imarticus to get the best in big data analytics courses and fast forward your career graph in the field of data science. We offer data analytics at our centers in Thane, Pune, Bangalore, Chennai, Hyderabad, Coimbatore, Delhi.

What are The Best Machine Learning Prediction Models for Stocks?

Posted on by Imarticus Learning

Predicting stock prices has been at the focus for a long time due to monetary benefits it can yield. Prediction of the future stock price is trying to determine the future value of a company stock which is traded on a stock exchange. Traditionally investors have relied upon fundamental research and technical analysis to predict the stock price movements.  Fundamental analysis is concerned with the performance of the company and its business environment. Investors mainly consider the current price and likely future performance of the company while picking the stocks.
Technical Analysis is concerned with past patterns of the stock price movements and predicting future trends. Lately,  machine learning models are also used in technical analysis to process the historical and current data of public companies to predict their stock prices. Mathematical models can be developed which process historical data about quarterly financials, trading data, latest announcements, and news flow etc and machine learning techniques can identify patterns and insights that can be used to make predictions for stocks. Trading signals can be generated and because correlation based on which the trading call is given is often weak, the time window in which profit can be made by the execution of the trade is usually very small.  Therefore, firms that specialize in ‘quant’ trading keep their machine learning algorithms simple and secretive so their trading strategies can be optimized for speed and reliability.
Now, we take a brief look at some of the machine learning models for prediction of stock prices.
Moving Average – Moving average is average of past ‘n’ values and is considered widely in technical analysis.  20 day, 50 days and 200-day moving averages of stock prices and indices are critical data points in predicting future trends.
Exponential Moving Average (EMA) differs from simple moving average in that it gives greater weightage to the most recent values compared to the older values.
Linear Regression is another commonly used statistical approach to model the relationship between a scalar response and one or more independent variables.
Support Vector Machines (SVM) is a machine learning technique based on binary classification, which is now greatly used in predicting whether the price of a stock will be higher or lower after a specific amount of time-based on certain parameters.
There are also a few non-statistical models that are being used to forecast stock price movements. A textual analysis of financial news articles is one such method. In this method, a crawler is trained to scan all the financial news articles and look for the patterns that are likely to have an impact on prices of specific stocks. Text mining of historical news articles with concurrent time series analysis can be done to figure out the impact of various types of news articles. Different weightage for articles based on the credibility of their sources can be given.
Thus, Machine learning can be applied to stock data and mathematical models can be developed to predict stock prices. Trading strategies can be optimized for speed relying on these models while simultaneously eliminating human sentiments from decision making.
There is a lot to explore with regards to stock predictions and machine learning models that need further explanation cannot be expatiated in a concise article like this.  The machine learning future in India is very bright.  If you need to pursue machine learning courses, learn from pioneers like Imarticus.