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Global Cloud-Based Database Market Analysis and Growth 2018 – 2023: Tencent, MongoDB …

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Posted on mongodb google news. Visit mongodb google news

The research report “Global Cloud-Based Database Market Analysis report 2018” provides market and technology analysis, and strategic forecasts for cloud-based database, sizing the market with the aid of product type, end-use sector, and geography. The report provides historical data from 2013 to 2018 alongside current market estimates for cloud-based database, and five-year forecasts to 2023. The report is the crucial planning tool for cloud-based database businesses, you can identify commercial enterprise opportunities using quantitative market forecasts and employ a detailed breakdown of market sizes to design future strategies with conviction.

The report considered various factors to examines the global market for cloud-based database to 2023, including developments relating to the cloud-based database products; technology advancement in this field; profiles of leading market players and suppliers; recent developments activities in the cloud-based database industries influence the market. The report additionally gives the info related to global cloud-based database market actual drivers, restraints, challenges, emerging markets, pricing structure, latest trends and industrial policies across the globe that will affect cloud-based database business in the future. The report also maintains the study of rapidly growing top most Manufacturers regions such as

  • North America
  • Europe
  • China
  • Japan
  • Middle East & Africa
  • India
  • South America

.

Get a Sample of Global Cloud-Based Database Market report from https://market.biz/report/global-cloud-based-database-market-2017-mr/143163/#request-sample

Global Cloud-Based Database Market: Manufacturers Analysis:-

The crucial part of the report provides a comparative study of top leading players in the cloud-based database market. The analysis phase begins with the company profile followed by product introduction and cost structure, cloud-based database manufacturing base and capacity,  market plans, recent development activities and future prospects of the cloud-based database company. In short, the competitive analysis provides you an authoritative and expert view of leading market players alongside technologies and market strategies that will affect the cloud-based database industry in the future.

Top Manufacturers cited in the report:-

  • MongoDB
  • Tencent
  • Teradata
  • Microsoft
  • Google
  • Amazon Web Services
  • Oracle
  • Couchbase
  • IBM
  • Cassandra
  • Rackspace Hosting
  • Alibaba
  • Salesforce
  • SAP

Global Cloud-Based Database Market Segmentation:-

Regions Product Types Applications
  1. North America
  2. Europe
  3. China
  4. Japan
  5. Middle East & Africa
  6. India
  7. South America
  1. NoSQL Database
  2. SQL Database
  1. Large Enterprises
  2. Small and Medium Business

For more Information or Any Query Visit https://market.biz/report/global-cloud-based-database-market-2017-mr/143163/#inquiry

The report provides exclusive insights into the cloud-based database market has broken down by product type, end-use sector, and regional market. The main objectives of this study are to identify market areas that give major impact on the growth of the global cloud-based database market.

How will this Market Intelligence Report Benefit You?

– The report offers statistical data in terms of value (US$) and Volume (units) for the global cloud-based database market today and to 2023.

– Exclusive insight into the key trends affecting the cloud-based database industry, although key threats, opportunities and disruptive technologies that could shape the global cloud-based database market supply and demand.

– The report tracks the leading market players that will shape and impact the global cloud-based database market most.

– The data analysis present in the cloud-based database report is based on the combination of both primary and secondary resources.

– The report helps you to understand the real effects of key market drivers or restrainers on cloud-based database business.

– The report offers a five-year strategic forecast for the global cloud-based database market, segmented by key product type, end use sector, and region and country worldwide.

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Article originally posted on mongodb google news. Visit mongodb google news

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Weekly Digest, July 30

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Article originally posted on Data Science Central. Visit Data Science Central

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week.

Featured Resources and Technical Contributions

Featured Articles

Picture of the Week

Source for picture: contribution marked with a +

  Hire a Data Scientist | Search DSC | Classifieds | Find a Job | Post a Blog | Ask a Question

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R Code for Cox & Stuart Test for Trend Analysis

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Article originally posted on Data Science Central. Visit Data Science Central

Below is an R code for Cox & Stuart Test for Trend Analysis. Simply, copy and paste the code into R workspace and use it. Unlike cox.stuart.test in R package named “randtests”, this version of the test does not return a p-value greater than one. This phenomenon occurs when the test statistic, T is half of the number of untied pairs, N.
Here is a simple example that reveals the situtaion:

> x
[1] 1 4 6 7 9 7 1 6

> cox.stuart.test(x)

Cox Stuart test

data: x
statistic = 2, n = 4, p-value = 1.375
alternative hypothesis: non randomness

> o.Cox.Stuart.test(x)

Cox & Stuart Test for Trend Analysis

data: x
Test Statistic = 2, Number of Untied Pairs = 4, p-value = 1
alternative hypothesis: any type of trend, either decreasing or increasing

R Code for Cox & Stuart Test:

o.Cox.Stuart.test <- function(x, alternative=c(“two.sided” ,”left.sided”, “right.sided”)){

dname <- deparse(substitute(x))

alternative <- match.arg(alternative)

stopifnot(is.numeric(x))

n0 <- length(x)

if (n0 < 2){
stop(“sample size must be greater than 1”)
}

n0 <- round(length(x)) %% 2

if (n0 == 1) {
remove <- (length(x)+1)/2
x <- x[ -remove ]
}

half <- length(x)/2
x1 <- x[1:half]
x2 <- x[(half+1):(length(x))]
n <- sum((x2-x1)!=0)
t <- sum(x1<x2)

if (alternative == “left.sided”) {
p.value <- pbinom(t, n, 0.5)
alternative <- “decreasing trend”
}

if (alternative == “right.sided”){
p.value <- 1-pbinom(t-1, n, 0.5)
alternative <- “increasing trend”
}

if (alternative == “two.sided”){
alternative <- “any type of trend, either decreasing or increasing”
if (1-pbinom(t-1, n, 0.5) == pbinom(t, n, 0.5)) {
pdist <- dbinom(0:n, n, 0.5)
p.value <- sum(pdist[pdist <= t+1])
}
else {
p.value <- 2*min( 1-pbinom(t-1, n, 0.5), pbinom(t, n, 0.5))
}
}

rval <- list(statistic=c(“Test Statistic”=t),
alternative=alternative,
p.value=p.value,
method=”Cox & Stuart Test for Trend Analysis”,
parameter=c(“Number of Untied Pairs”=n),
data.name=dname)

class(rval) <- “htest”
return(rval)
}

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Digital Marketing: Are you avoiding these common problems?

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Article originally posted on Data Science Central. Visit Data Science Central

Target audience: Marketers, analysts, campaign managers, and decision makers.

Preface: I teach multiple tools under Adobe’s experience cloud and I often get to have a look at the shape of digital marketing in multiple companies and across various business domains. This post is a summary of the most common problems and ways of resolving them at early stages before they become blunders.

1. The accuracy (and single view) of data: If you work in a medium or big size organisation or own one, you would know about the multiple platforms and tools required, they can range from Data collection systems or CRMs to campaigning and social listening tools. With multiple platforms comes the problems of integration and thus making sure that the quality of data is intact is a major challenge. A lot of ‘orgs’ only look at one face of data while ignoring the other aspects of it. For an example, if you’re an e-com company and you’re worried about cart abandonment, add your partners and offline data to create a 360* data view and maybe you’ll find out while customers look at the products online they tend to buy offline (or vice versa). By using a cross -channel data source I’ve seen a lot of companies saving big bucks on their marketing and especially retargeting. 

Best practice: Have a ‘quality check’ team and use a good DMP. Get a data guru to look at the complete picture of data in your org.

2. People moving around (or one-man show): This is one of the most common problems, here’s what happens: Person X (Data/marketing manager) takes the initiative of setting up a platform, onboards an agency, figures out the need and gap, makes a pitch and begins the implementation; usually by the time the platform is set, quality check is done and the tools are in place, it’s already two years and this person moves ahead in career and to a different org thinking of the excellent foundation stone that has been laid. Fast forward six months; The new person in place is adept and comfortable to the platform which was used in his/her previous organisation, a recommendation is made for it and the system which got implemented is said to be a wrong choice. The whole system restarts into the same loop costing the company a fortune.

Best practice: A decision team (can also be one person each from relative verticals) makes a decision based on proper market research and everything included the pitch and benefits are recorded and logged, this gives an answer to why was a decision made in a particular manner. 

3. Lack of training: Learning is a journey and most of the companies forget this fact, let’s face it; employees are like cattle, they’re invested in, are fed and are cared for in a way that enables them to produce as soon as they can, sometimes one can produce even though before they’re ready, hence most of the times, the product will be shit. Learning also needs to be of quality, a lot of times going for cheap training programs instead of the standard will do more bad than good.

Best practice: Enroll in a learning program that spans quarterly (or every six months depending on industry), gives your employee certification and assessment goals. 

4: Not having a precise KBR: A lot of times during the introduction I ask the participants what is it you want to measure and I see blank faces very often. In an opposite scenario, sometimes people will enthusiastically say ‘everything‘, which is equally bad. Knowing what is the Key Business Requirement of your company is the starting step and around that, you can measure ‘everything‘ that impacts it. Mostly KBR’s are of three types: Engagement, Conversion & Revenue. I’ve also seen a few orgs which not only map their goals well but also refine metrics over a period of time to measure their success by best means.

Best Practice: Map the contributing factors of your success in your online and offline journey, define and convey the metrics that measure your success. Also get stakeholders in one room during the year and brainstorm that whether your success metrics are still relevant and if not what can be the ways of improving them

5. Big data not actioned upon is bad data: Businesses often generate tons of data but not all data is information, analysts, and marketing folks generate dashboards and schedule them and then over time keep getting the same reports without ever logging back in again. Agencies copy paste the same reports and metrics for similar businesses promising them that they’re uniquely built for them. People get comfortable and stop questioning their data. I recently taught a class to the students of a university and I was amazed at the quality of questions thrown at me, most of the adults with ‘X’ years of experience don’t do that, in fact, they hardly ever question their data in the right way.

Best practice: Get interns, they’re a cheap way of getting new eyes to look at your business, it helps them and it helps you. Run A/B tests on your website to see what works and experience targeting to continue the user journey where they left. All the data will only make sense if you ask the right questions and that’ll only happen if you’ve set the right KBR. 

Postface: In an ideal scenario, companies should invest in their data planning, strategy, and execution in early stages, sometimes budget is a constraint but most often not implementing a good solution at an early stage blocks your view of your audience maturity. Plan your marketing around the personalized journey and use cross-channel data segments, partner with good data providers and discover multiple dimensions of your existing audience. Acquisition and Retention still are and will be the two major aspects of your customer but there is always a scope of refining the metrics you use to measure the two.

Other articles:

https://www.linkedin.com/pulse/predictive-analytics-five-precautions-abhishek-srivastava/

https://www.linkedin.com/pulse/analytics-top-5-avoidable-mistakes-hamper-your-growth-srivastava/

Linkedin Profile: https://www.linkedin.com/in/abhisheksrivastava5/

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Google Updates Key Cloud Database Services

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Article originally posted on InfoQ. Visit InfoQ

Google unveiled new database capabilities and partnerships at the recently-concluded Google Cloud Next conference. This included expanded beta access for Cloud Firestore, new functionality for Cloud Bigtable, and highly-requested features for Cloud Spanner. There were also announcements around expanded support for SAP and Oracle workloads. 

Google Cloud has a broad portfolio of first-party database services. Customers get managed MySQL and PostgreSQL databases via Cloud SQL. Google Cloud Spanner, released last year, is Google’s modern take on relational databases. NoSQL column storage is available with Cloud Bigtable, while Cloud Datastore offers a NoSQL document database. Cloud Firestore represents the next generation of Cloud Datastore. Developers use Firebase Realtime Database for a variety of mobile-centric use cases. And BigQuery—which got machine learning-infused updates at the conference—provides a managed data warehouse. Add all this to the ninety-plus third-party databases in the Google Cloud marketplace, and users aren’t short of options.

Cloud Firestore has been in limited beta until now. It’s billed as “the next major version of Cloud Datastore and a re- branding of the product.” In a blog post, Google Cloud’s VP of Engineering Brad Calder announced that Cloud Firestore, while still in beta, is coming to more users. He also shared that Cloud Firestore will offer a compatibility mode for existing Cloud Datastore users. With this new “Datastore Mode”, the Datastore user doesn’t need to change their apps to use the performance and consistency of Firestore. The conference talk “Building Scalable Apps with Cloud Firestore” shared extra Firestore news. The speakers revealed that Cloud Firestore is coming to new locations in the Americas, Europe, and Asia-Pacific. They also shared a 99.999% availability SLA for multi-region instances.

Bigtable development started in 2004, and it underpins many of the core Google services. It shipped as a publicly-accessible service on Google Cloud in 2015. Last week, Google announced the availability of regional replication, in beta since May 2018. Users can instantiate linked clusters when provisioning Bigtable instances, or, add a replicated cluster to an existing instance. Google replicates more than just data; new or deleted tables, added or removed column families, and changes to a column’s garbage-collection policy all get replicated too. In addition, Google added a new “key visualizer” function that’s automatically enabled for Cloud Bigtable instances. This feature shows a heat map of access patterns for use in troubleshooting performance issues. Finally, Google introduced client libraries for Cloud Bigtable. C# and Node.js are in beta, with Python, C++, Ruby, Java, and PHP on the way.

Cloud Spanner is the CAP theorem-busting relational database that promises strong consistency, high availability, and automatic replication. Google recently added import/export functionality aimed at simplifying disaster recovery, among other things. At the conference, Google previewed a data manipulation language (DML) for Cloud Spanner that makes it easier to use standard drivers and ORM tools. Google presenters also shared that query stats were on the way.

The Google Cloud team also teased further partner integrations. Google, Intel, and SAP are teaming up on higher performing and cheaper SAP HANA workloads.

This week we announced our collaboration with Intel and SAP to offer Compute Engine virtual machines backed by the upcoming Intel Optane DC Persistent Memory for SAP HANA workloads. Google Compute Engine VMs with this Intel Optane DC persistent memory will offer higher overall memory capacity and lower cost compared to instances with only dynamic random-access memory (DRAM). Google Cloud instances on Intel Optane DC Persistent Memory for SAP HANA and other in-memory database workloads will soon be available through an early access program. 

Google shared plans for larger VM types to accomodate SAP HANA workloads. In addition to the 4TB machine types available today, they’re working on VMs with 12TB and 18TB of memory.

Google promised new support for Oracle workloads on Google Cloud. Calder says that Google is partnering with managed service providers that will offer managed Oracle workloads on Google Cloud. Customers can use their existing Oracle licenses, while getting the same response time for support as with other Google Cloud services. Details are light at this time.  The service is planned for Fall of 2018. 

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Finance basics for data scientists

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Article originally posted on Data Science Central. Visit Data Science Central

I picked up a little book called “Finance Basics” published by Harvard Business Review Press, for a short in-flight reading. This tiny book isn’t going to make someone a finance expert but I did find a few things useful for data scientists and business analysts whose background is not finance or economics. Data science is truly a multi-disciplinary area with people coming from many different background and areas of expertise, often with little to no exposure to finance and economics. So I am highlighting few things that can be valuable for anyone in the community.

Before going into those “basics” here’s what the book says about WHY understanding finance is necessary in the first place:

  • To get a sense of which products/services are performing well and where, and which are not doing well.
  • Are cost of production and marketing justified? Is there room for improvement?
  • If the company decides to invest $X more on a product will it generate at least $X in revenue?
  • How the company’s revenue, cost, profit and overall financial health look like looking into the future?

Anyone can Google to learn details, but here are my 4 (four) take aways that can make you feel good about finance even if you never took a finance course.

  1. Try to make sense of 3 major financial statements: income statement, balance sheet, cashflow statement. Income statement is where you get the famous “bottom line” of the company you work for.
  2. Can you prepare a budget for a project (or idea) that you are proposing? Learn at least some basics.
  3. Learn few things about benefit cost analysis if you are proposing a new idea or product. The following related terms may sound intimidating, but I promise you already know them from you everyday experience.
    • Return on Investment (ROI)
    • Benefit Cost Ratio (BCR)
    • Payback Period
    • Net Present Value (NPV),
    • Internal Rate or Return (IRR)
    • Break-even Analysis
  4. And finally, few other jargon that you might be interested in. Again I promise, most of them will sound familiar to you, nothing out of the world: Accounts payable (A/P); Accounts receivable (A/R); Assets; Asset turnover; Balance sheet; Book value; Break-even; Capital; Cost of capital; Debt; Dividend; Equity; Fixed and variable cost; Gross margin; Gross profit; Hurdle rate; Inventory; Liabilities; Valuation; Working capital.

About the authorAn economist and data scientist with multi-disciplinary academic and professional training. Website: Data2Decision;Twitter: @DataEnthus

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Don’t Let Data Science Become a Scam

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Article originally posted on Data Science Central. Visit Data Science Central

Guest blog by Seth Dobrin and Daniel Hernandez.

Companies have been sold on the alchemy of data science. They have been promised transformative results. They modeled their expectations after their favorite digital-born companies. They have piled a ton of money into hiring expensive data scientists and ML engineers. They invested heavily in software and hardware. They spend considerable time ideating. Yet despite all this effort and money, many of these companies are enjoying little to no meaningful benefit. This is primarily because they have spent all these resources on too much experimentation, projects with no clear business purpose, and activity that doesn’t align with organizational priorities.

When the music stops and the money dries up, the purse strings will tighten up and the resources that are funding this work will die. It’s then that data science will be accused of being a scam.

To turn data science from a scam to source of value, enterprises need to consider turning their data science programs from research endeavors into integral parts of their business and processes. At the same time, they need to consider laying down a true information architecture foundation. We frame this as the AI ladder: Data foundation, analytics, machine learning, AI/Cognitive:

To break the current pattern of investing in data science without realizing the returns, businesses can address key areas:

  1. Finding, retaining, and building the right talent and teams
  2. Formulating an enterprise strategy for data and data science
  3. Operationalizing data science
  4. Overcoming culture shock

Finding, retaining and building the right talent and teams

Our two previous VentureBeat articles cover the composition of a data science team and the skills we look for in a data scientist. To recap, great data science teams rely on four skillsets: Data Engineer, Machine Learning Engineer, Optimization Engineer, and Data Journalist. If you want to maximize the number of qualified applicants, try posting roles with those four titles and skill sets instead of seeking out generic “Data Scientists”.

Retaining talent requires attention on several fronts. First, the team needs to be connected to the value they’re driving: How is their project impacting the line of business and the enterprise? Second, they need to feel empowered and know you have their backs. Finally, when planning for your team, build in 20–25% of free time work on innovative, blue-sky projects, to jump into Kaggle-like competitions, and to learn new tools and skills. Carving out that much time might seem pricey in terms of productivity, but it provides an avenue for the team to build the skills that accelerate future use cases — and it’s far more efficient than hiring and training new talent.

Formulating an enterprise strategy for data and data science

Identify, Value, and Prioritize Decisions

Map out the decisions being made and align them to tangible value, specifically, cost avoidance, cost savings, or net new revenue. This is the most important step in this process and the first step in shifting data science from research to an integral part of your business. We’ve previously mapped out a process for doing this in Six Steps Ups, but briefly, it requires direct conversations with business owners (VPs or their delegates) about the decisions they’re making. Ask about the data they use to make those decisions, its integrity, whether there’s adequate data governance, and how likely the business is to use any of the models already developed.

You can drive decisions using a dashboard that’s integrated directly into processes and applications. However, beware of situations where data simply supports preconceived notions. Instead, look for chances to influence truly foundational decisions:

“Where should we position product for optimal availability at minimal cost?”

“What are our most likely opportunities for cross-sell/up-sell for specific customers?”

“Which are my top-performing teams? Bottom-performing teams?”

“How can I cut costs from my supply chain by optimizing x given y constraints?”

Value each decision. Making decisions more quickly and with greater efficacy avoids costs, saves costs, or creates additional revenue. Express this value using whatever methodologies and terms your CFO advocates.

Prioritize the decision portfolio. This exercise creates a decision portfolio, which can serve as the basis for a data science backlog. Prioritize the backlog by assessing the likelihood of success, the ease of implementation, and the value (based on the scoring metric in the table above). We’ve developed a framework for building and prioritizing the portfolio by going through this exercise ourselves.

Discrete Deliverables. Next, take your top decisions and break them into manageable chunks that you can deliver in small sprints. This starts by identifying the minimal viable product (MVP) and then working back from there. Consider three-week sprints that can start delivering value (however small) after two sprints.

Operationalizing data science

Moving data from a research project to an integral part of your company requires operationalizing your data science program. In addition to building the team and setting the strategy, it requires integrating the models into processes, applications, and dashboards. Also plan for continual monitoring and retraining of model deployments.

Truly integrating the models means they can’t be deployed as csv files sent by email or code tossed over the wall to a development team. They need to be deployable as reusable and trusted services: versioned RESTful APIs output directly from the data science platform. Delivering models as csv files severs the connection to the process — and the feedback that comes from the implementation. Tossing R or Python code to a development team to convert it into an API is inefficient at best. But be prepared for some work. Setting up a robust process can often take three to six months and needs to be configured as a feedback-loop that easily allows your team to retrain and redeploy the models.

Applying predictive or prescriptive analytics to your business inevitably requires you to retrain the models to stay current with the accelerated rate of change they are driving and based on the feedback to the models from the outcomes themselves. We’ve seen instances where a team develops more than one hundred models to drive a single decision over the course of a year only to develop zero models the following year because the team is now focused entirely on monitoring and retraining of their existing models. It’s important to recognize that this isn’t a defect in their approach. They needed to build that many models to solve the problem. The issue is that in the course of operationalizing the model deployments, they didn’t automate the monitoring and retraining of those models.

Unless you’ve already executed a large number of data science projects for the enterprise, the challenges of operationalizing can come as a surprise — but they are very real.

Derived data products. We can often overlook the fact that our engineered features are valuable data in and of themselves. As part of model building and engineering, consider deploying this new data as APIs and integrating them into the appropriate data assets rather than letting them remain proprietary. For example, if a data science team engineers a feature that combines customer data, product data, and finance data, deploy the new feature as an API and have the corresponding model consume that new API.

Overcoming culture shock

Among the various reasons that data science becomes a scam at so many enterprises, one reason in particular looms large: cultural resistance. To break through resistance from management, focus on any of their peers who are excited to engage. Once they start applying the data and models in their processes and applications, the advocates may start to outperform the resistors. At some point, managers will ask what they are doing differently, and the resistors may feel pressure to shift their positions. Think of this as leading through shame. The value you demonstrate to managers is often about out-performing their peers by avoiding costs, saving money, or creating net new value

Individual contributors might resist the shift for a few different reasons. They might be worried they’ll be replaced by the machine or that the people who built it don’t fully understand the process or environment. Both are valid concerns. Buy credibility by being honest and addressing concerns head-on. However, in most cases you won’t actually be automating anyone out of a job, but rather making each job safer or more efficient. Help the team to see this directly. For the concern that the data science team doesn’t really understand what they do, consider pull one of the hold-outs off the floor and asking them work directly on the project as a product owner or subject matter expert. That provides other resisters an advocate that is “one of us”. When that team member returns to his regular job, you’ll have an advocate for the current data science approach, as well as an advocate for future implementations and deployments.

Finally, you can overcome the culture shock by raw mass. Identify a use case and build a related hack-a-thon that’s sponsored by senior executives. The hack-a-thon should include basic presentations on machine learning, cloud, and APIs, as well as more advanced presentations and conversations on the same topics. Let the teams work hands-on with the use case and allow individuals across the company to participate, independent of their training and background.

To turn the alchemy of data science into gold, enterprises must align their data science efforts to business outcomes with real and tangible value. They must stop focusing on experimentation and shift their efforts to data science as an integral part of their business models and align these with corporate priorities. If you follow the methodology above, the music will keep on playing, the funding will keep flowing, and data science will not be a scam in your enterprise.

See what IBM is up to at https://datascience.ibm.com/

About the Author

Seth Dobrin, PhD, is VP & CDO, IBM Analytics. Exponential Change Leader and Life-Long Learner with a proven track record of transforming businesses via data science, information technology, automation and molecular techniques. Highly skilled at leading thru influence across complex corporate organizations as demonstrated by having built, developed and executed corporate wide strategies.

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Which Programming Language Is Considered To Be Best For Machine Learning?

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Article originally posted on Data Science Central. Visit Data Science Central

These are a bulk of people pondering the same question and exploring different answers but at a hanging stage not knowing which one is correct and which one should they follow. The answers are very conflicting some share it on the basis of the research while some share their personal experience which confuses the newbies as hell. Well, the answer to this question depends on what you are trying to develop and why have you indulged in developing a machine learning app in the first place. I know the answer is a diplomatic one.

But, let me tell you one thing most of the popular development languages are adding their support to simplify the machine learning. This is because of its high adaptability. When we think of machine learning, four basic languages comes to the mind:

  • Python

  • R-Programming

  • MatLab

  • Octave

Let’s conduct a comparative study on the factors affecting the machine learning.

1) Speed

While choosing the best programming language, speed is an essential thing to consider. R was basically built as the statistical language. This means that it has higher data-analysis and statistical support. On a contrary, Python depends on the packages. Hence, when it comes to tasks related to statistics, R has an upper hand compared to the Python and is a bit faster. So, if your machine learning project has to be rapid, R programming is your choice.

2) Learning Curve

When it comes to functional perspective, R is the programming language. Whereas when it comes to being object-oriented, Python is the language. If you belong to a functional programming background, learning Python would be a lot easier as compared to R. Coming to the Octave and Matlab both are similar to writing some mathematical equations and yes again easy to learn and implement.

You can’t really pick any one programming language for machine learning. It depends on your technical background and experience as to which language will be easy to learn for you.

3) Costing

The only language which is paid and needs a license for its use is Matlab. The other three preferred languages for Machine Learning are open source and is totally free for the use. Hence, when you have free resources available why would anyone opt for the paid? This is why Matlab lags a bit back 9in comparison with other languages.

4) Support From Community

Coming to the popularity standards, Octave is not that prominent in the public. The other programming languages are highly popular in the market and have massive community support. Also, the adaptability rate of the all these three is quite high as compared to octave for machine learning.

5) Production Ready

When it is about the statistical analysis, R is the suitable programming language. If it is about the computer vision related task, Octave and Matlab are the preferred choices for the programming language. If it is about general tasks like data processing and result processing, Python is a more suitable programming language. Well, coming to picking one for Machine learning programming, Python will be more suitable. The generic nature will make integration of machine learning easy with other software.

6) DNN Framework Support

Language

Python

R

Matlab

Octave

Speed

0

1

1

1

Learning curve

1

1

1

1

Costing

1

1

0

1

Community support

1

1

1

Production ready

1

0

0

0

DNN framework

1

0

0

0

Total

5

4

3

3

Caffe and Tensorflow are the two most popular frameworks in the current time. Caffe has support for Matlab and Python while Tensorflow has support for Python and R. Now, for the lesser popular frameworks like Theano, Python is the single language that has the support. Python is the only language which has a universal support for every framework. The result is clear, Python has an upper hand over every other language.

Conclusion :

Well, it is quite evident from the above statistical representation which programming language is the best. The highest score gaining language is Python and probably the one you should use for Machine learning. But, again it depends on the type of work and the reason why have you thought about it in the first place. If you are thinking to develop something for the long term, prefer python and if you are looking for developing just a prototype for short-term, R is the right way.

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Podcast: Jeff Dalton on Teaching Leaders How to Teach

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In this podcast Shane Hastie, Lead Editor for Culture & Methods, spoke to Jeff Dalton about the challenges of agile adoption in large organizations and the need to teach agile leaders how to teach so they can lead the cultural shift that is needed

Key Takeaways

  • The marketing of agility is going far better than the actual on the ground adoption 
  • When process-centric, low trust organisations adopt agile they bring that approach to their agile practices
  • The link between the soft skills and the hard technical practices of agile is what enables high quality and real agility, but many senior managers haven’t made the connection for themselves
  • There is no need for a new framework – the need is to enable leaders to leverage agility and teach that to other leaders and to their teams
  • The re-emergence of a focus on craftsmanship is a great thing, but it is not enough

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  • 0:28 Introduction & background
  • 2:10 Assessing companies on their cultural adoption of agility since the early 2000’s
  • 2:40 Focusing on research around leadership
  • 3:10 The marketing of agility is going far better than the actual on the ground adoption
  • 3:30 The early adopters of agile were small companies and subsets of large organisations and they had great success in their niches
  • 3:40 As large companies try to adopt these approaches culture becomes a limiting factor 
  • 4:10 Examples of organisations where the culture often prevents collaboration across teams
  • 4:46 The Capability Maturity Model has a reputation of being heavyweight and process burdened, but the reality is that the model isn’t inherently like that; early adopter companies were process heavy and they applied their way of working to the model
  • 5:05 When those same process-centric, low trust organisations adopt agile they bring the same mindset behaviours to their approach
  • 5:32 Most large companies bring their existing structures and roles into an agile adoption and are not applying the practices as they are intended to be
  • 5:58 There is an organizational type-mismatch when the values and philosophy of the company are at odds with the high-trust, high-collaboration values and philosophies of agility
  • 6:37 Many senior executives in large organizations are keen on becoming “more agile” however they are not often keen to change the corporate culture to align with the agile philosophy
  • 7:03 The perceived benefits of agility are “get it fast and get it cheap”
  • 7:14 Many of the leaders in large organisations have followed a long “climb the greasy pole” path to achieve their positions and the lessons learned getting there are decidedly anti-agile
  • 7:50 The real benefits of agility are related to the “soft” areas of satisfaction, collaboration, motivation and engagement
  • 8:08 Harnessing the power of the soft skills is what makes teams successful
  • 8:32 The link between the soft skills and the hard technical practices of agile is what enables high quality and real agility, but many senior managers haven’t made the connection for themselves
  • 8:48 Examples of what happens at some large organisations
  • 9:32 The problem is the culture, it’s not the skills and competency of the people
  • 9:50 One approach to helping make the cultural change is teaching leaders at every level to teach their teams the key values and principles of the new ways of working
  • 11:04 Introducing a model The Agile Performance Holarchy
  • 11:14 Describing the six categories of teaching in the model
  • 11:42 There is no need for a new framework – the need is to enable leaders to leverage agility and teach that to other leaders and to their teams
  • 11:48 An example of using sprint planning across all levels of the organisation
  • 13:25 Ways to ensure the mindset shift is part of the teaching/learning rather than just practice compliance
  • 13:55 Adding peer observation to the process to ensure the truth does cascade up the organisation without being filtered because the people who are involved at the lover levels are the ones bringing the message up the hierarchy
  • 14:28 Rating groups practice areas at three levels – adopting, transforming and mastering
  • 14:44 Craftsmanship is one of the performance circles of the model
  • 14:50 The re-emergence of a focus on craftsmanship is a great thing, but it is not enough
  • 15:50 Many software organizations are dropping the ball on technical craftsmanship
  • 16:04 The envisioning circle is another aspect of the model – crafting a clear vision is something that the industry is generally pretty bad at
  • 16:40 Out of 300 organizations examined, 270 had a surrogate product owner
  • 17:04 Additional performance circles include teaming (the practices and approaches needed to form effective teams), affirming (ensuring quality) and providing (ensuring the teams have the physical environment they need to be successful)
  • 17:50 Procurement is something that is often overlooked when adopting agile and the procurement process for many organizations is broken
  • 18:10 The way contracts are written is often completely contradictory to the agile mindset
  • 19:05 Consultants can be helpful, but the real responsibility for teaching has to belong to the leaders
  • 19:24 Consultants are good to get you unstuck, but don’t keep them around for too long
  • 21:28 Things that leaders need to become competent in include unlearning old behaviours and “truths”
  • 21:46 A really important change is learning to not affix blame to a problem – blame causes fear
  • 22:19 You’re probably going to fail – when would you like to fail, early or late?
  • 23:05 Assignment of blame drives negative behaviour throughout the whole organization
  • 23:40 Another important skill is the ability to build an infrastructure to let people self-organize
  • 24:25 Strong leadership is required for self-organization to be effective – but this is a different type of leadership
  • 24:40 The elements that are needed for this new style of leadership to be effective
  • 25:26 Many of the things in the infrastructure for a self-organizing environment don’t exist in the typical environment
  • 27:05 This is a complete re-engineering of the way organizations work and it’s really simple, it’s just hard
  • 28:15 This is not happening in organizations because the leaders don’t know how to make it happen.  The model is a “how-ability” guide on how to make the changes needed
  • 28:52 There is an assessment model available which leaders can use to assess themselves and their organizations
  • 29:41 Mastering agility means extending the ideas and approaches beyond development teams into the whole organization

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Eich and Crockford on the Future of JavaScript: Insight from the Creators of JavaScript and JSON

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Article originally posted on InfoQ. Visit InfoQ

At the recent FullStack conference in London, Brendan Eich, JavaScript creator, and Douglas Crockford, JSON creator and author of “JavaScript: The Good Parts” spoke on the future of JavaScript. Eich’s focus during the conference talk was primarily around features to iterate and improve the language. Crockford’s focus on JavaScript lead us to the next paradigm in software engineering, which he describes as globally distributed secure asynchronous programming.

Crockford notes that this is unnecessarily difficult currently as all programming languages, including JavaScript, are stuck in the old architecture of a single machine in a single address space. However, because JavaScript does such interesting things with functions and objects, Crockford believes that JavaScript helps with this transition more than other languages.

Eich’s biggest regret with JavaScript was giving users what they asked for shortly after the initial release of the language, rather than thinking through better solutions to their problems. For example, type coercion with the == equality operator, e.g., making 12 == ’12’, to ease handling of the pushing of data over HTTP to the server.

Crockford notes that languages face a problem in that they potentially become too focused on “bad plastic surgery” additions instead of having a clear view of what is coming next.

Compared to a decade ago, I think less of the language is good, but I think the good part is better.

Eich spoke about the progression of how things change over the years, and how old becomes new again. For example, E4X never got accepted into the language, but React users love JSX, which is “E4X without the bad XML parts”.

A significant portion of Eich’s talk emphasized the value of asm and WebAssembly. Starting with a statically typed subset of JavaScript, asm.js source code can run near native speed. This performance improvement occurs because asm supports switching from just in time (JIT) to ahead of time (AOT) compilation, going straight from an abstract syntax tree (AST) to machine code generation.

WebAssembly is a faster loading and more generalized version of asm.js. WebAssembly runs in a separate process so it can have a more expressive syntax than JavaScript, but is still part of the same JavaScript virtual machine.

Eich focused on some of the features he is most excited to see land in the language as early as next year:

  • TC39 BigInt – New value type to handle arbitrary precision integers, with support for TypedArrays
  • Dynamic import() – ES Module imports by default are static, and this also allows for prefetching
  • Array.prototype.flat / flatMap – arbitrary flattening of an array, and the solution to flatten and smooshgate
  • More destructuring – for arbitrary objects and arrays
  • Private methods and accessors in Classes – per Eich, the # symbol prefix is a bit controversial and may not happen
  • Async iteration – for await of syntax
  • Several RegExp improvements

See the full list of TC39 proposals for information on these proposals and more.

Crockford spends his time questioning our deep assumptions and how they impact our progress. For example, should we start counting with zero or one, and explains why he believes everything should begin with zero.

Crockford notes that the biggest disappointment thus far with ES2015 implementations is the lack of proper tail calls or tail call optimization, with only Safari having implemented this portion of the specification thus far. According to Crockford, tail calls are the most important feature added to ES2015, as this feature helps support tail recursion, the functional alternative to iteration, as well as continuation passing.

Crockford believes that pure functions are the most critical thing in the language going forward and that anything that impacts pure functions should not get considered for the language.

There are many things that Crockford dislikes in JavaScript. For example, he dislikes async/await because it blocks developers from understanding async programming by making it look sync in nature.

Crockford explained his perspective on simplicity, “The Simpler the standard, the less there is to agree on, and the more likely is it to make interoperability possible.” He gave JSON as an example, explaining that JSON is a syntax; it says nothing about behavior, and it is simple to understand and use. The entire grammar of JSON fits on the back of a business card.

Crockford is working on a new language and transpiler called neo that delivers just Crockford’s new good parts, in a new language, that transpiles to his definition of good JavaScript, which he expects to release soon along with a forthcoming book titled “How JavaScript Works.”

During the question and answer portion of the talk, Eich got asked about the binary-ast proposal, which is something between traditional JavaScript and WebAssembly in that it provides a binary syntax tree for all of JavaScript.

Eich considers binary-ast to be a potentially pragmatic approach but is waiting to get convinced of its benefits through an initial implementation and subsequent performance metrics. Mozilla is working on an initial implementation. Potential risks would include it being another security attack vector, and it just encouraging developers to write larger JavaScript codebases, rather than relying on tools and discipline to optimize their codebase.

When asked about type systems such as TypeScript, Eich remarked that the convenience of dynamic programming is convenient, and a type system that doesn’t get in the way is a good idea. Eich notes that “People are used to type systems that don’t pay their way.”

TypeScript was brilliant as it’s a strict superset of JS. Every JS program is a TS program. Anders Hejlsberg is top notch.

Crockford was also asked about TypeScript, but has a much less favorable opinion: “It doesn’t solve problems that I have.” This dismissal of TypeScript was somewhat contradictory with his earlier statement that “interfaces between things are where errors typically happen”, as one of the main benefits of TypeScript is to help define interfaces to prevent these errors.

Eich got asked about possibly standardizing things like React. He noted how early libraries like Dojo and Prototype had concepts that made their way into later versions of the language, but that “We never just standardize a library like React.” Instead, efforts are made to lift the essential primitives out of the libraries into the core language. Eich noted that we could get into trouble by standardizing early, especially for tool time solutions like type systems.

Eich got asked for his opinion on the Observables proposal based on RxJS Observables. He notes that the specification has stalled and that it’s a challenge to get what should get built into the language, and he is not sure if Observables should get added to the language. He did not mention that the WHATWG specification for streams is a superset of Observables, and perhaps the competition between these two proposals may be blocking progress on the Observables proposal.

Finally, Eich was asked about machine learning, and noted that while deep learning is useful, it does not tell us anything about causality, and today’s efforts are just an advanced form of curve fitting algorithms.

The full videos of these conference talks are available with free registration:

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