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SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

Showcase your talent at a hackathon!

 

If you belong to the world of Data, hackathon is not a new word to you. Several organizations host hackathons online but how do you pick the right one for yourself? Especially if you are a beginner?

What you do in a hackathon is only an easier version of what your job as a data scientist would require. From personal experience, Kaggle community is a boon to budding aspirants! It does wonders in enhancing one’s skillset by providing a competitive exposure.

The dataset on Kaggle and other platforms is created for the purpose of competitions and giving the participants a taste of work that data scientists are expected to do. However, real world data is much messier than what you would work with on these platforms. Nevertheless, it’s a great way to polish and upgrade your skills.

Read more at: https://analyticsindiamag.com/how-much-is-kaggle-relevant-for-real-life-data-science/

 

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Random forests: a collection of Decision trees!

In literal sense, a forest is an area full of trees. Likewise, in technical sense, a Random Forest is essentially a collection of Decision Trees. Although both are classification algorithms which are supervised in nature, which one is better to use?

A Decision Tree is built on an entire data set, using all the features/variables while a Random forest randomly (as the name suggests) selects observations/rows and specific features/variables to build several decision trees and then average the results. Each tree “votes” or chooses the  class and the one receiving the most votes by majority is the “winner” or the predicted class.

A Decision tree is comparatively easier to interpret and visualize, works well on large datasets and can handle categorical as well as numerical data. However, choosing a comfortable algorithm for optimal choice at each node and decision trees are also vulnerable to over fitting.

Random Forests come to our rescue in such situations. Since they select samples and the results are aggregated and averaged, they are more robust than decision trees. Random Forests are a strong modelling technique than Decision Trees.

Read more at: https://www.analyticsvidhya.com/blog/2020/05/decision-tree-vs-random-forest-algorithm/

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Are we learning data science the right way?

In the words of Favio Vazquez, “Data Science is not just knowing some programming languages,math,statistics and having domain knowledge.’’Data science enables a person to evaluate the data and predict the present and future of the world. The solution to the optimization problems must add value to the business or organization. Mastering the craft is what it takes to be a successful data scientist. Having a good knowledge of statistics or math is never enough; practicing hard so that one can apply the tools to solve real business problem is the key. Interpretation is an important part of data science. Through modeling, we can decode the complex data and transform it into something useful and simple. Finally questioning and analyzing our understanding make us better learners.  Read more at: https://medium.com/swlh/the-3-biggest-mistakes-on-learning-data-science-f782e1a8abec

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ROLE OF MACHINE LEARNING IN BUSINESS ANALYTICS

    Considering how stubborn established companies are with their business operations, introducing Machine Learning which will bring a difference in business environments and in how people work, is extremely difficult. Data Science, AI and Machine Learning are different concepts where Machine Learning helps the other two prosper. It is a means of programming an algorithm wherein businesses feed in examples of real data. According to Maxim Scherbak, the author of this article, the new task for ML-driven organizations is preparing data for Machine Learning models. This implies that it will make Business Analysts different rather than making them redundant. For successful implementation of Machine Learning algorithms (which are mostly viewed as black boxes) they need to be backed by thorough testing. Leaving rule based approaches behind, businesses need to learn to trust these algorithms to shine in the era of AI. 

Read more at: https://towardsdatascience.com/when-business-analytics-meets-machine-learning-10ecaada9d8

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Programming Languages prevalent for Data Science

Tons of data is generated everyday in the industry. And making sense of this pile of data has become an important task for many businesses. To achieve this, they are turning into Big Data analytics and Data Science. Data Scientists have knowledge about various algorithms suitable for various types of data and these statistical algorithms are implemented in several programming language. Selection of the programming language depends on many factors. So here is the list of top 6 programming languages that are used by most of the data scientists and analysts.

1.  Python

2. R Programming

3. Matlab

4. Java

5. Julia

6. Scala

Read detailed review at https://www.technotification.com/2018/07/best-programming-languages-for-data-science.html

 

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Briefing Data Science

After Artificial Intelligence and Machine Learning, the next most emerging field in todays world is the field of Data Science. It is said to be the cousins of AI and ML and mainly deals with data. It intakes data, uses processes, algorithms and scientific methods to extract knowledge and valuable data from large data sets. This field is need of each and every type of organization. Whether it be business or an IT firm, every organization needs data for improvement. Thus, outcomes from the processing of data are further used for decision making and for improving current functioning.

People often gets confused between Data Science, Data Analytics and Big Data. The key difference between them is that Data Analytics and Big Data are components of Data Science. Data Science extract values from the output of Data Analytics and Big Data to solve problems.
The goal of Data Science is to extract business-focused insights from business. This could help organizations in many ways.

Read more about this topic at: https://www.cio.com/article/3285108/data-science/what-is-data-science-a-method-for-turning-data-into-value.html

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Prevention in Data Sciences

The buzzwords in technology are no new to someone. Whether it be Artificial Intelligence, Machine Learning, Data Sciences or Analytics, each of these are invading in our lives promising us better future. However, it is believed that expertise interested in data sciences are not widely spread. Data Sciences is a field that can improve business, can help in other technological fields, can help in decision making and more. 

It is rightly said that prevention is better than cure. A wrong step in data sciences can affect the decisions and the results. One should avoid the following mistakes while dealing with data:

  1. Assuming your data is ready to use and all you need
  2. Not exploring your data set before starting work
  3. Not using control group to test your new data model in action
  4. Starting with targets rather than hypotheses
  5. Automating without monitoring the final outcome

To study mistakes like these read https://www.cio.com/article/3271127/data-science/12-data-science-mistakes-to-avoid.html?nsdr=true

 

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Working with Machine Learning

Artificial Intelligence, Machine Learning and Deep Learning are relatively newer technologies invading the fields of information technology, business etc. Though developers are walking towards this era, currently the number of experts is relatively less. The company often makes mistakes by starting up with the technologies instead of focusing on business needs. They often make mistakes by assigning out of domain work to some. For e.g. Hiring data scientists and asking them to build something interested from given database. Rather than a team must be formed of product managers, data engineers, data scientist and DevOps engineers.A team of four will be a kick start to improve our process and giving better results. Now everybody has an opportunity to improve the models, optimise the deployment and scale the business. 

Talking about ML, many projects fail due to complex structures. This could occur because of working on wrong problem, to having wrong data, failing to build a model or failing to deploy it correctly. Read more at: https://medium.com/@guyernest/the-flywheel-of-machine-learning-systems-50aa6d992382

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The Ten C’s of A Data Scientist

Data Science is a new field of interest and used in every sector. Whether it is a business, production line or a tech company, each of them wants someone to analyse their data. This would further help them to make decisions. Even though there is so much need of data scientist, still the number of data scientist is low. There are many characteristics that could define a good data scientist. 

Few of them starting with C are: Curious, Careful, Clever, Confident, Creative, Capable, Communicative, Considerate, Candid and Collaborative. 

To know further about these words visit: https://medium.com/@tableaucoach/characteristics-of-a-data-scientist-ten-cs-4e3b185cc7cd

 

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Data Science in Advertising

In the recent years, advertising has become more accurate and particularly with the support of better information. This expansion in accuracy is due to the development of computerized advertising. 5 reasons how data science could be the advertising wave of the future are: (i) it is a requirement for marketers, it gives advertisers the capacity to set up client profiles and design coordinated strategies, (ii) portable applications can give knowledge about client’s conduct on the web and their interests, (iii) it helps in streamlining and enunciation of the client travel, (iv) it helps in increasing brand income and also increases advertising spending plans, (v) cloud capabilities helps advertisers run investigations on an assortment of data. Read more at: http://www.datasciencecentral.com/profiles/blogs/5-reasons-why-data-science-could-be-the-advertising-wave-of-the

 

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Principle Experiences of Data Science

Three principles of data science are: (i) the system built should perform well on future data sets and not just the current data set. Conclusions made on the basis of the current scenario are not always true for future cases, (ii) feature extraction is important, i.e. specifically finding the information that is required, by finding the correct elements, (iii) understanding and developing the correct model is the most important task. These are a few principle experiences which are not stated anywhere. Read more at: http://www.datasciencecentral.com/profiles/blogs/three-things-about-data-science-you-won-t-find-in-the-books

 

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Universal Usage of Analytics

From gyms to the front desks of Medical practice center, analytics are used everywhere. Most of the sectors have been semi-automated. Some small businesses, however, have failed to use analytics. Except these exceptions, most of the businesses have been successful in combining data science and cloud technology. Data analytics are essential for medium and small scale companies as well to be successful and data centric transformations are now trending. Read more at: http://www.zdnet.com/article/using-analytics-for-health-commerce-and-more/

 

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Data Scientists and Mathematics

Data science and mathematics together makes learning more interesting. Passionate data scientists towards mathematics solve many modern math problems using data science. This article gives selection of 12 interesting articles, about mathematical problems, math-free algorithms and statistical theory. Most of them can be understood by the layman. Some of them include R code and some include processing vast amounts of data. Some of the articles are: Simple Proof of the Prime Number Theorem, Fascinating Facts and Conjectures about Primes and Other Special Number,Factoring Massive Numbers: Machine Learning Approach.

Read more at: http://www.analyticbridge.datasciencecentral.com/profiles/blogs/10-interesting-reads-for-math-geeks

 

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