Careers in machine learning, Data science, artificial intelligence, deep learning and many more are considered as one of the best choices to pursue. Now these technologies and the related jobs are considered one of the hottest and best jobs today. So, here are the list of top 5 languages prevalent in market for data science, machine learning etc.
1. Python
2. R
3. Java
4. Scala
5. C
Data security is becoming a matter of increasing concern. Guidelines to cover the security is data are needed. Data breaches are being detected but, still classified data are at the risk of being leaked with the current age of data access. Data-driven algorithms have made it easy to control drones from remote areas. Data governance is the need of the hour, in order to regulate the access to different data, and control the extent to which data can be shared. Regulations for data visibility is important as it allows accountability and legitimizes and government laws introduced. Read more at: https://www.theguardian.com/science/political-science/2017/dec/15/data-will-change-the-world-and-we-must-get-its-governance-right
A huge amount of digital data is getting piled up every day and to deal with that the technology recruiters are valuing the skills in data visualization, data science, machine learning and data analysis the most. These skills in data analysis help the companies to give more insight about the data and help to predict a better future. With the courses on data science people are now showing immense interests in machine learning and data visualization tools. Professionals are willing to upskill to keep pace with the automation. Read more at: http://economictimes.indiatimes.com/jobs/techies-reskill-to-log-on-to-big-data-deluge/articleshow/58103804.cms
A very little time is spent on thinking about how to deploy a data science model into production. As a result, many companies fail to earn the value that comes from their efforts and investments. In production environment data continuously comes, result are computed and models are frequently trained. The challenges faced by companies fall into four categories: Small Data Teams: They mostly use small data, often don’t retrain models and business team is involved in a development project.
Packagers: Often build their framework from scratch and practice informal A/B testing , generally not involved with the business team
Industrialization Maniacs: These teams are IT led and automated process for deployment and maintenance , business team are not involved in monitoring and development
The Big Data Lab : Uses more complex technologies , business teams are involved before and after deployment of data product
Companies should understand that working in production is different than working with SQL databases in development , moreover real time learning and multi-language environments will make your process complex. Also a strong collaboration between business and IT teams will increase your efficiency. Read more at : http://dataconomy.com/2017/02/value-from-data-science-production/
You can’t be a good data scientist unless you have a good hold on statistics and have a way around data. Here are some simple tips to be an effective data scientist:
Statistical Methods Should Enable Data to Answer Scientific Questions - Inexperienced data scientists tend to take for granted the link between data and scientific issues and hence often jump directly to a technique based on data structure rather than scientific goal.
Signals Always Come with Noise - Before working on data, it should be analysed and the actual usable data should be extracted from it.
Data Quality Matters - Many novice data scientists ignore this fact and tend to use any kind of data available to them, if always a good practice to set norms for quality of data.
Check Your Assumptions - The assumptions you make tend to affect your output equally as your data and hence you need to take special care while making any assumption as it will affect your whole model as well as results.
These are some of the things to keep in mind when working around with data. To know more you can read the full article by Vincent Granville athttp://www.datasciencecentral.com/profiles/blogs/ten-simple-rules-for-effective-statistical-practice
Recommender systems have become the backbone of the ecommerce sector. They have helped companies like Amazon and Netflix to increase their revenue to as much as 10% to 25%.
And hence the need of the hour is to optimize their performance.
So, what are recommenders? Recommenders are the applications which personalize your customer’s shopping experience by recommending next best options in light of their recent buying or browsing activity. Recent developments in analytics and machine learning have let to many state of the art recommender systems.
Types of Recommenders: There are broadly five types of recommender systems, which are as follow:
1. Most Popular Item
2. Association and Market Basket Models
3. Content Filtering
4. Collaborative Filtering
5. Hybrid Models
In coming years, recommender system will be used by almost every organisation, whether it's big or small, and will become an inseparable part of the ecommerce world.
To know more read the article by William Vorhies at: http://www.datasciencecentral.com/profiles/blogs/understanding-and-selecting-recommenders-1
2016 has been the year of deep learning, some big breakthrough were achieved in 2016 by Google and DeepMind.Some of the most significant achievements are as follow :
AlphaGo triumphs Go showdown : AlphaGo the google’s AI for the game Go to everyone’s surprise was able to beat Go champion Lee Sedol.
Bots kicking our butts in StarCraft : DeepMind AI bots were able to outperform some of the top rated StarCraft II players.
DIY deep learning for Tic Tac Toe : AlphaToe a AI bot was able to outperform most of the people that played with it.
Google’s Multilingual Neural Machine Translation : Google was able to make a model which is capable of translating text b/w languages, reaching a new milestone in linguistics and NLP.
Hence , in a nutshell , 2016 was the year for Deep Learning and a lot of unachievable milestone were conquered during the annual year.
To know more you can read the full article by Precy Kwan at http://www.datasciencecentral.com/profiles/blogs/year-in-review-deep-learning-2016
Machine Learning is the backbone of today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms. And hence algorithms are the next most important thing in data science after data.
Hence , the question which algorithm to use ? Some of the most used algorithms and their use cases are as follow :
1) Decision Trees - It’s output is easy to understand and can be used for Investment decision ,Customer churn ,Banks loan defaulters,etc.
2) Logistic Regression - It’s a powerful way of modeling a binomial outcome with one or more explanatory variables and can be used for Predicting the Customer Churn, Credit Scoring & Fraud Detection, Measuring the effectiveness of marketing campaigns, etc. ,
3) Support Vector Machines - It’s a supervised machine learning technique that is widely used in pattern recognition and classification problems and can be used for detecting persons with common diseases such as diabetes, hand-written character recognition, text categorization, etc. ,
4)Random Forest: It’s an ensemble of decision trees and can solve both regression and classification problems with large data sets and used in applications such as Predict patients for high risks, Predict parts failures in manufacturing, Predict loan defaulters, etc.
Hence based on your need and size of your dataset , you can use the algorithm that is best for your application or problem.
You can read the full article by Sandeep Raut at http://www.datasciencecentral.com/profiles/blogs/want-to-know-how-to-choose-machine-learning-algorithm
Analytics is one of the of the most hot topic of the 21st century and it’s starting to become the second currency to various organisation, but despite having so much knowledge we prone to create some blunders , they are broadly categorised as Data Visualization Errors (Erroneous Graphs) and Statistical Blunders.
Data Visualization Errors (Erroneous Graphs): This is one area that can give a nightmare to both the presenter as well as the audience. Incorrect data presentation can screw the intuition and can also lead to misinterpretation of data by the audience and can leave the organisation with results which are practically useless for them.
Statistical Blunders Galore: This is probably a “no blunders zone” where one would not want to make false assumptions or erroneous selections and is easily one of the most error prone section. Statistical errors can be a costly affair to both the organisations as well as the audience, if not checked or looked into it carefully and hence must.
To know more read the full article by Sunil Kappal (author) at :http://www.datasciencecentral.com/profiles/blogs/the-most-common-analytical-and-statistical-mistakes
Data Science has become an inevitable charter in our everyday lives where every action of ours is measured, plotted, classified and logged. Businesses have also realized that they should adopt and embrace these changes now or risk being left behind in this fast moving digital world. Data Monetization is the new paradigm for organizations and slowly but steadily data is becoming their currency of trade.
Data Science is more like an art of turning data into actionable insights. Though we consume data regularly, we never cared to look behind the scenes on the rigorous processes, data preparation and machine learning algorithms that give us accurate data to devour. And this looks like some deep mystery but in reality it’s not a mystery, it’s just an intelligent use of data and various resources available to so called wizards: Data Scientists. To know more read the complete article by Prakash Pasupathy at: http://www.datasciencecentral.com/profiles/blogs/solving-the-data-science-mystery
In today’s digital age the world has become smaller.Gone are the days, when organizations used to load data in their data warehouse overnight and take decision based on BI, next day. Today organizations need actionable insights faster than ever before to stay competitive.With real-time analytics, the main goal is to solve problems quickly as they happen, or even better, before they happen. The lead role in revolutionizing real-time analytics is played by Internet of Things(IoT) . Now, with sensor devices and the data streams they generate, companies have more insight into their assets than ever before.
But it is so great as it looks , indeed it is as it helps getting the right products in front of the people looking for them, or offering the right promotions to the people most likely to buy using the real time recommender system.
are the days of waiting long hours to know the analytics of your data , now is the time to move beyond just collecting, storing & managing the data to take rapid actions on the continuous streaming data – Real-Time!! You can read the full article at http://www.datasciencecentral.com/profiles/blogs/do-you-know-what-is-powerful-real-time-analytics
A traditional data science department is comprised of Data Scientists, Data Engineers and Infrastructure Engineers. This model has a drawback that one role is always dependent on other and likely to criticize them for task failures because they didn't do their job well. These conflicts may reflect in the quality of final data product. So, what went wrong? You probably don't have big data. Jeff Magnusson (Director of Algorithms Platform at Stitch Fix) suggested a clever approach of forming a "High Functioning Data Science Department" which involves building an environment which allows autonomy, ownership, and focus for everyone involved yet at the same time clearly distinguishing the roles of Data Scientists and Data Engineers. Data scientist can't suddenly become talented engineers nor is that engineers will be ignorant of all business logic, the partnership is inherent to the success of this model. You can read more at: http://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/
Customers love personal touch and feeling special, whether it’s being greeted by name when we walk into the store, a shop owner remembering our birthday It make them feel like they are your single most important customer. But in an online world, you can’t guide them through the product they may like. This is where recommendation engines do a fantastic job.
With personalized product recommendations, you can suggest highly relevant products to your customers at multiple touch points of the shopping process. Intuitive recommendations make them feel like your shop was created just for them and hence they become your regular customers.
Application of Data Science to analyze the behavior of customers to make predictions about what future customers will like and understanding the shopper’s behavior on different channels can increase the sale by over 30%.Ultimately most important goal for any organisation is to convert visitors into paying customers and hence product recommendations are extremely important in digital age.You can read the full article on Product recommendations in Digital Age by Sandeep Raut (Author) at: http://www.datasciencecentral.com/profiles/blogs/product-recommendations-in-digital-age
In a short span of five years the world of analytics has changed immeasurably. Now we see fast analytics, interactive experimentation with data and exploratory analysis of data.
But why ? The answer to this question can be summed in three simple points. First, with fast analytics, it’s easier to keep up in an ever-changing world and keep pace with customers and market forces and businesses can see a measurable value from running advanced analytics on their data. Second, due to low prices of analytics businesses must meet customers’ expectations or risk losing them to a competitor. Third, it has the ability to elevate a company to the next level and provide it with a competitive edge over its rivals through the real-time insights it can achieve.
And , hence every one in this competitive market is shifting to advance analytics. To know more you can read the article by Aaron Auld (CEO of EXASOL) at: http://www.datasciencecentral.com/profiles/blogs/the-rise-of-advanced-analytics .
Robo-advisers are automated advisers with provide financial advisory as low cost, so it’s available to everyone. The costs are as low as 1 euro. They open the door to the financial markets and give you the possibility to invest in stocks, bonds and other securities and keep their costs low by trading Exchange-Traded Funds.
But, how do they exactly work ?? Robo-advisors use algorithms based on mean-variance optimization, a mathematical framework to create a portfolio of assets such that the expected return is maximized for a given level of risk. Financial market data is used to estimate expected return, standard deviation and correlation for every asset class. On opening an account, you are asked simple questions about your age, income, savings and willingness to take risk. This data is collected to estimate your risk tolerance and fit their model to your current situation and preferences and give you the best advice to invest in the market. To know more read this article http://www.datasciencecentral.com/profiles/blogs/robo-advisers-and-the-future-of-financial-advice by Stefan.
Yes, if companies need anything in 2017 they are Data Scientists.
But why , what is so special about them? And the answer is :
Data scientists tracks millions of data sets and provides concrete information for organizations looking to break their data into meaningful information that can be used at all levels in the organization.
As this is the data century , every company wants to recommend its users what they are most likely to choose and hence the need of Data Scientists to study the data and extract various pattern from it and hence creating a 360-degree view of their customers. This not only impresses the customers but also helps the companies in understanding their customers better and hence improving their services according to the customers.
So , in a nutshell yaa companies do need Data Scientists.
You can read more at http://www.datasciencecentral.com/profiles/blogs/why-large-companies-need-data-science-experts-like-you
What is dark data ? The large amount of data collected by companies that goes useless due to lack of analysis(39%) or structure(25%) or even sometimes due to lack of proper tools(13%) is known as the dark data. If used/analyzed properly gives a new dimension to the companies.
But how can we harness the dark data. Well it’s no rocket science , just some simple measures and you have a whole new dimension of data.Here are just a few of them :-
Keep a track of user logins and various checkout at different locations, this helps in creating a 360-degree view of the user.
Mobile phone data, this will help to illuminate an array of new product and marketing opportunities, and hence improve marketing effectiveness.
Free text input, such as feedback can be analysed to determine if general sentiment of the feedback is positive or negative.
Data when properly harnessed can be a powerful and can serve as a gateway to new insights, developing new opportunities and boosting your business into the data-driven century.
You can read more at http://www.datasciencecentral.com/profiles/blogs/dark-data-the-billion-dollar-opportunity
Data Science is one of the hottest job of the 21st century. But is it easy to be a data scientist , and the answer is it’s not hard. But here are some of the things that one need to avoid to be a bad data scientist :
Focus on tools rather than business problems :
Yaa tools do matter, but what is even more important is the problem you are working on, it should be the basis of all your decisions.
Planning communication last :
Communication helps in getting various insights about our ideas and hence help in improving our approach to handle a particular problem.
Data analysis without a question / plan :
Data without a plan or motive is useless and we often end with more of the things we don’t need than the things we really need.
Don’t read enough :
This is a mistake that everyone makes, to be updated with the recent development tends helps in constantly improving our skill set and hence benefits the organization.
Fail to simplify :
Data Scientist generally fail to maintain the core idea behind their product and hence end upwith something which is much complicated for the end users.
Don’t sell well :
The job of data scientist doesn’t end with creating the product , he must know how to sell it, how to reach to the end users.
You can read more at http://ucanalytics.com/blogs/6-worst-mistakes-for-data-scientists-and-how-to-avoid-them-explained-with-quotable-quotes/
Many a times we have observed companies quote data to support an argument to a statement. This however has been detrimental to the rise of data in general. How? Let us give an example. According to research it has been found that 2.5 quintillion bytes of data are created on a daily basis. Though that is a quintessential amount of data generation, but it is all the more astonishing to learn that 90% of this existing data has been already created in the last two years. The point is Big Data might be a huge hype in the industry, but the intelligent leaders should understand, that it is not the end-word. Big Data should be intelligently crafted with business strategies to provide desirable results.
To know and learn more, check out: http://www.business2community.com/big-data/biggest-mistake-companies-make-big-data-01645333#lClpgY4wJI7yodbO.97
In the Healthcare industry, what we really need? Better healthcare results to improve our lives. Healthcare industry generally takes decision using data- like case histories. Well the good news is, now we have lots and lots of data. This is the era of big data, where millions of bytes of data are generated every second. And the bad news is we can't keep up with this fast rate of generation of data. Big data is unstructured which makes it difficult to hunt down relevant helpful information. Using data science with health care we can predict epidemics, advance cures and can provide better, safer and more pleasant experience for patients. Read full article here - http://www.cio.com/article/3001216/analytics/4-big-reasons-why-healthcare-needs-data-science.html
