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The first 2 of them may appear similar at first glance, but actually they make totally different statements. In the first one (Data Science Venn Diagram 2.0) for example, mathematics and statistics are a real subset of Data Science, whereas in the second one Data Science is a real subset of Mathematics, which is a completely different statement.

Looking at the discussions and posts in the Data Science community we could use a statement like “Everything needed to derive something out of your data, with whatever tool, algorithm, technique, method or programming language necessary or appropriate to achieve this.” as a starting point to outline Data Science and to try to answer the question, how do I become a Data Scientist?

What is out of the question is, that the theoretical language of data analysis is mathematics and the practical language is, at least for another few decades, programming in whatever dialect (programming language) and the execution platform is computers and the subject of analysis is data in whatever form and wherever and however it might be produced or coming from.

The theoretical or abstract description of problems and their solution is mathematics, the practical side, for example the usage of BLAS, is a computer program and the statistical aspect of it could be the algorithm which led you to the processing of the matrix and the subject matter expertise is the meaning of the algorithm and what the entries of the matrix stands for.

The reason for this is, that due to my opinion a scientist produces abstract knowledge, whereas we are mainly talking about people using this kind of knowledge to produce findings which we also could call content.

“The process we are interested in is the deployment of useful data driven models into production.” John Mount (April 19, 2013) The overlapping regions could be interpreted as the knowledge of the person regarding the related aspect and the Data Analyst represents the need to combine the aspects.

Next, it is important, to really comprehend what you can do with it. Means: Be able to formulate subject area problems also in an abstract way and be able to recognize if a specific problem can be solved using NMF, hence to map your abstract knowledge to your knowledge about the subject area and therefore to combine the aspects.

If you then can lay down this knowledge andwrite a computer program and visualize the problem as well as the findings and then also communicate and document what you found / have done, then all aspects finally came together.

From there you might expand your knowledge into the area of algorithms using data analytics tools, programming languages and more until you cover enough data analytic aspects to put your hands on the solution of real data analytics issues and questions.

So does you to learn how it brings value exactly to different internal clients.” Jeffrey Ng (December 26, 2014) Again looking at the “Data Science Venn Diagram v2.0” where Data Science covers, for example all of mathematics, the question is, do I have to cover all of the programming languages, algorithms etc?

“I prefer somebody who has done ten different things in ten different domains because they will have hopefully learned something new about data from each of different places and domains.” Claudia Perlich If we look at the aspects from a “Language” point of view and treat the languages as follows: Then you could say that a Data Scientist is a person able to speak at least one language in each of these language areas and to be able to translate the dialects he knows into one another.

Therefore there are the following daily posts to increase your knowledge base and inspire you for further investigation: Finally, because Data Analytics is crucial for the coming decades dominated by data, it is important that even those people who do not explicitly want to be Data Scientist have to understand the concepts.

Understanding the fundamental concepts, and having frameworks for organizing data-analytic thinking not only will allow one to interact competently, but will help to envision opportunities for improving data-driven decision-making, or to see data-oriented competitive threads.” Foster Provost &

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

Skills That LinkedIn Looks for in a Data Scientist Candidate

I’m sure everyone who has been following tech industry news knows about “big data” and “AI.” Although there is no industry-consistent definition for either term, most people tend to agree that both have been playing more and more important roles lately, and that we need to know and leverage them better in both our personal and professional lives.

We want every team member to have the capabilities to understand basic stats concepts (e.g., hypothesis testing, mean/median, variance, probability distributions, sample size calculation, power calculation, etc.), design and analyze experiments, and apply them in a business setting.

This includes instances like how to design success metrics, set up an experiment plan, and provide timely insights to guide ramping up a test from a small pilot to 100% member base, which often also requires iterations to get the product features right for member satisfaction and desired business outcomes.

For senior-level candidates, we’d expect them to have relevant industry experience and in-depth statistics knowledge to not only answer questions, but also drive towards solutions that create the optimized level of member experience and business impact.

The aspect we are looking at is the candidate’s ability to formalize a business problem into a machine learning problem, select the proper modeling algorithms, and build out the models following the right process of training, testing, and validation.

What is also really important is how the candidate picks the right machine learning algorithms (knowing the pros and cons from each, e.g., logistic regression, linear regression, decision-tree, deep learning, etc.) for the type of business problems he or she is solving for.

The types of questions we ask can include: how do you summarize your findings in a clear and succinct way, how do you handle the situation if the stakeholders are not convinced based on the analysis results, how do you respond to questions about the algorithms/methodology from people who are not technical, and how do you manage a project that isn’t going as planned and turn it around?

This requires good business domain knowledge, critical analytical thinking, familiarity with carrying out root cause analysis, and the ability to communicate results effectively to influence business decision-making.

The capabilities that we would like to assess here include the ability to solve a business case with the right analytical approach and reasonable data intuition, as well as the ability to make relevant and actionable recommendations based on data insights.

19 Data Science and Machine Learning Tools for people who Don’t Know Programming

This article was originally published on 5 May, 2016 and updated with the latest tools on May 16, 2018.

Among other things, it is acknowledged that a person who understands programming logic, loops and functions has a higher chance of becoming a successful data scientist.

There are tools that typically obviate the programming aspect and provide user-friendly GUI (Graphical User Interface) so that anyone with minimal knowledge of algorithms can simply use them to build high quality machine learning models.

The tool is open-source for old version (below v6) but the latest versions come in a 14-day trial period and licensed after that.

RM covers the entire life-cycle of prediction modeling, starting from data preparation to model building and finally validation and deployment.

You just have to connect them in the right manner and a large variety of algorithms can be run without a single line of code.

There current product offerings include the following: RM is currently being used in various industries including automotive, banking, insurance, life Sciences, manufacturing, oil and gas, retail, telecommunication and utilities.

BigML provides a good GUI which takes the user through 6 steps as following: These processes will obviously iterate in different orders. The BigML platform provides nice visualizations of results and has algorithms for solving classification, regression, clustering, anomaly detection and association discovery problems.

Cloud AutoML is part of Google’s Machine Learning suite offerings that enables people with limited ML expertise to build high quality models. The first product, as part of the Cloud AutoML portfolio, is Cloud AutoML Vision.

This service makes it simpler to train image recognition models. It has a drag-and-drop interface that let’s the user upload images, train the model, and then deploy those models directly on Google Cloud.

It also provides visual guidance making it easy to bring together data, find and fix dirty or missing data, and share and re-use data projects across teams.

Also, for each column it automatically recommends some transformations which can be selected using a single click. Various transformations can be performed on the data using some pre-defined functions which can be called easily in the interface.

Trifacta platform uses the following steps of data preparation: Trifacta is primarily used in the financial, life sciences and telecommunication industries.

The core idea behind this is to provide an easy solution for applying machine learning to large scale problems.

All you have to do is using simple dropdowns select the files for train, test and mention the metric using which you want to track model performance.

Sit back and watch as the platform with an intuitive interface trains on your dataset to give excellent results at par with a good solution an experienced data scientist can come up with.

It also comes with built-in integration with the Amazon Web Services (AWS) platform. Amazon Lex is a fully managed service so as your user engagement increases, you don’t need to worry about provisioning hardware and managing infrastructure to improve your bot experience.

You can interactively discover, clean and transform your data, use familiar open source tools with Jupyter notebooks and RStudio, access the most popular libraries, train deep neural networks, among a a vast array of other things.

It can take in various kinds of data and uses natural language processing at it’s core to generate a detailed report.

But these are excellent tools to assist organizations that are looking to start out with machine learning or are looking for alternate options to add to their existing catalogue.

Specifically, myself and my team have worked with industry leaders to identify a core set of eight data science competencies you should develop.

Programming SkillsNo matter what type of company or role you’re interviewing for, you’re likely going to be expected to know how to use the tools of the trade.

This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren’t) a valid approach.

Statistics is important at all company types, but especially data-driven companies where stakeholders will depend on your help to make decisions and design / evaluate experiments.

Machine LearningIf you’re at a large company with huge amounts of data, or working at a company where the product itself is especially data-driven (e.g.

Linear AlgebraUnderstanding these concepts is most important at companies where the product is defined by the data, and small improvements in predictive performance or algorithm optimization can lead to huge wins for the company.

This will be most important at small companies where you’re an early data hire, or data-driven companies where the product is not data-related (particularly because the latter has often grown quickly with not much attention to data cleanliness), but this skill is important for everyone to have.

CommunicationVisualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions.

It is important to not just be familiar with the tools necessary to visualize data, but also the principles behind visually encoding data and communicating information.

At some point during the interview process, you’ll probably be asked about some high level problem—for example, about a test the company may want to run, or a data-driven product it may want to develop.

Data Scientist: The Sexiest Job of the 21st Century

When Jonathan Goldman arrived for work in June 2006 at LinkedIn, the business networking site, the place still felt like a start-up.

For one thing, he had given Goldman a way to circumvent the traditional product release cycle by publishing small modules in the form of ads on the site’s most popular pages.

Through one such module, Goldman started to test what would happen if you presented users with names of people they hadn’t yet connected with but seemed likely to know—for example, people who had shared their tenures at schools and workplaces.

Goldman is a good example of a new key player in organizations: the “data scientist.” It’s a high-ranking professional with the training and curiosity to make discoveries in the world of big data.

If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a “mashup” of several analytical efforts, you’ve got a big data opportunity.

Much of the current enthusiasm for big data focuses on technologies that make taming it possible, including Hadoop (the most widely used framework for distributed file system processing) and related open-source tools, cloud computing, and data visualization.

Greylock Partners, an early-stage venture firm that has backed companies such as Facebook, LinkedIn, Palo Alto Networks, and Workday, is worried enough about the tight labor pool that it has built its own specialized recruiting team to channel talent to businesses in its portfolio.

“Once they have data,” says Dan Portillo, who leads that team, “they really need people who can manage it and find insights in it.” If capitalizing on big data depends on hiring scarce data scientists, then the challenge for managers is to learn how to identify that talent, attract it to an enterprise, and make it productive.

In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.

More enduring will be the need for data scientists to communicate in language that all their stakeholders understand—and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or—ideally—both.

But we would say the dominant trait among data scientists is an intense curiosity—a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested.

As Portillo told us, “The traditional backgrounds of people you saw 10 to 15 years ago just don’t cut it these days.” A quantitative analyst can be great at analyzing data but not at subduing a mass of unstructured data and getting it into a form in which it can be analyzed.

A data management expert might be great at generating and organizing data in structured form but not at turning unstructured data into structured data—and also not at actually analyzing the data.

Several universities are planning to launch data science programs, and existing programs in analytics, such as the Master of Science in Analytics program at North Carolina State, are busy adding big data exercises and coursework.

The Insight Data Science Fellows Program, a postdoctoral fellowship designed by Jake Klamka (a high-energy physicist by training), takes scientists from academia and in six weeks prepares them to succeed as data scientists.

As one of them commented, “If we wanted to work with structured data, we’d be on Wall Street.” Given that today’s most qualified prospects come from nonbusiness backgrounds, hiring managers may need to figure out how to paint an exciting picture of the potential for breakthroughs that their problems offer.

One described being a consultant as “the dead zone—all you get to do is tell someone else what the analyses say they should do.” By creating solutions that work, they can have more impact and leave their marks as pioneers of their profession.

As the story of Jonathan Goldman illustrates, their greatest opportunity to add value is not in creating reports or presentations for senior executives but in innovating with customer-facing products and processes.

At Intuit data scientists are asked to develop insights for small-business customers and consumers and report to a new senior vice president of big data, social design, and marketing.

New conferences and informal associations are springing up to support collaboration and technology sharing, and companies should encourage scientists to become involved in them with the understanding that “more water in the harbor floats all boats.” Data scientists tend to be more motivated, too, when more is expected of them.

The challenges of accessing and structuring big data sometimes leave little time or energy for sophisticated analytics involving prediction or optimization.

People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?” If “sexy” means having rare qualities that are much in demand, data scientists are already there.

In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies.

One question raised by this is whether some firms would be wise to wait until that second generation of data scientists emerges, and the candidates are more numerous, less expensive, and easier to vet and assimilate in a business setting.

Why not leave the trouble of hunting down and domesticating exotic talent to the big data start-ups and to firms like GE and Walmart, whose aggressive strategies require them to be at the forefront?

If companies sit out this trend’s early days for lack of talent, they risk falling behind as competitors and channel partners gain nearly unassailable advantages.

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