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5 Full-Stack Data Science Projects You Need to Add to Your Resume Now

5 Full-Stack Data Science Projects You Need to Add to Your Resume Now

Small or big, most of the organizations seek aspiring data scientists. The reason being this new breed of data experts helps them stay ahead of the curve and churns out industry-relevant insights.

It hardly matters if you are a fresher or a college dropout, with the right skill-set and basic understanding of nuanced concepts of machine learning, you are good to go and pursue a lucrative career in data science with a decent pay scale.

However, whenever a company hires a new data scientist, the former expects that the candidate had some prior work experience or at least have been a part in a few data science-related projects. Projects are the gateway to hone your skills and expertise in any realm.  In such projects, a budding data scientist not only learns how to develop a successful machine learning model but also solves an array of critical tasks, which needs to be fulfilled single-handedly. The tasks include preparing a problem sheet, crafting a suitable solution to the problem, collect and clean data and finally evaluate the quality of the model.

Below, we have charted down top 5 full-stack data science projects that will boost your efforts of preparing an interesting resume.

Deep Learning and AI using Python

Face Detection

In the last decade, face detection gained prominence and popularity across myriad industry domains. From smartphones to digitally unlocking your house door, this robust technology is being used at homes, offices and everywhere.

Project: Real-Time Face Recognition

Tools: OpenCV, Python

Algorithms: Convolution Neural Network and other facial detection algorithms

Spam Detection

Today, the internet plays a crucial role in our lives. Nevertheless, sharing information across the internet is no mean feat. Communication systems, such as emails, at times, contain spam, which results in decreased employee productivity and needs to be avoided.

Project: Spam Classification

Tools: Python, Matplotlib

Algorithm: NLTK

Sentiment Analysis

If you are from the Natural Language Processing and Machine Learning domain, sentiment analysis must have been the hot-trend topic. All kinds of organizations use this technology to understand customer behaviors and frame strategies. It works by combining NLP and suave machine learning technologies.

Project: Twitter Sentiment Analysis

Tools: NLTK, Python

Algorithms: Sentiment Analysis 

Time Series Prediction

Making predictions regarding the future is known as extrapolation in the classical handling of time series data. Modern researchers, however, prefer to call it time series forecasting. It is a revolutionary phenomenon of taking models perfect on historical data and using them for future prediction of observations.

Project: Web Traffic Time Series Forecasting

Tools: GCP

Algorithms: Long short-term memory (LSTM), Recurrent Neural Networks (RNN) and ARIMA-based techniques

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Recommender Systems

Bigwigs, such as Netflix, Pandora, Amazon and LinkedIn rely on recommender systems. The latter helps users find out new and relevant content and items. In simple terms, recommender systems are algorithms that suggest users meaningful items based on his preferences and requirements.

Project: Youtube Video Recommendation System

Tools: Python, sklearn

Algorithms: Deep Neural Networks, classification algorithms

If you are a budding data scientist, follow DexLab Analytics. We are a premier data science training platform specialized in a wide array of in-demand skill training courses. For more information on data science courses in Gurgaon, feel free to drop by our website today.

 

The blog has been sourced fromwww.analyticsindiamag.com/5-simple-full-stack-data-science-projects-to-put-on-your-resume

 

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Top 6 Data Science Interview Red Flags

Top 6 Data Science Interview Red Flags

Excited to face your first data science interview? Probably, you must have double-checked your practical skills and theoretical knowledge. Technical interviews are tough yet interesting. Cracking them and bagging your dream job is no mean feat.

Thus, to lend you a helping hand, we’ve compiled a nifty list of some common red flags that plague data science interviews. Go through them and decide how to handle them well!

Boring Portfolio

Having a monotonous portfolio is not a crime. Nevertheless, it’s the most common allegation against data scientists by the recruiters. Given the scope, you should always exhibit your organizational and communication abilities in an interesting way to the hiring company. A well-crafted portfolio will give you instant recognition, so why not try it!

Deep Learning and AI using Python

Sloppy Code

Of course, your analytical skills, including coding is going to be put to test during any data science interview. A quick algorithm coding test will bring out the technical value you would add to the company. In such circumstances, writing a clumsy code or a code with too many bugs would be the last thing you want to do. Improving the quality of coding will accelerate your hiring process for sure.

Confusion about Job Role

No wonder if you walk up to your interviewer having no idea about your job responsibilities, your expertise and competence will be questionable. The domain of data science includes a lot of closely related job profiles. But, they differ widely in terms of skills and duties. This is why it’s very important to know your field of expertise and the skills your hiring company is looking for.

Zero Hands-on Experience

A decent, if not rich, hands-on experience in Machine Learning or Data Science projects is a requisite. Organizations prefer candidates who have some experience. The latter may include data cleaning projects, data-storytelling projects or even end-to-end data projects. So, keep this in mind. It will help you score well in the upcoming data science interview.

Lack of Knowledge over Data Science Technicalities

Data analytics, data science, machine learning and AI – are all closely associated with one another. To excel in each of these fields you need to possess high technical expertise. Being technically sound is the key. An interview can go wrong if the recruiter feels you lack command over data science technicalities, even though you have presented an excellent portfolio of projects.

Therefore, you have to be excellent in coding and harbor a vast pool of technical knowledge. Also, be updated with the latest industry trends and robust set of algorithms.

Ignoring the Basics

It happens. At times, we fumble while answering some very fundamental questions regarding our particular domain of work. However, once we come out of the interview venue, we tend to know everything. Reason: lack of presence of mind. Therefore, the key is to be confident. Don’t lose your presence of mind in the stifling interview room.

Thus, beware of these drooping gaps; being a victim of these critical objections might keep you away from bagging that dream data analyst job. Instead, work on them and win a certain edge over others while cracking the toughest data science interview session.

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Note:

If interested in Data Science Courses in Gurgaon, check out DexLab Analytics. We are a premier training platform specialized in in-demand skills, including machine learning using Python, Alteryx and customer analytics. All our courses are industry-relevant and crafted by experts.

 

The blog has been sourced from upxacademy.com/eleven-most-common-objections-in-data-science-interviews-and-how-to-handle-them

 

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Harnessing Big Data for Water Management

World Water Day: Save Water with Big Data

Appalling forces are re-establishing the relationship between humans and water.

In the past, communities developed slowly, while the weather remained constant. Water sources never depleted at tumultuous rates as it has today. Water is no longer a dependable resource. That’s why many countries and cities are embracing smart technologies to manage water efficiently and preserve it for the coming generations.

As we observe the United Nations World Water Day on Wednesday, 22nd March, it is apt to assess the development being made in conserving this diminishing resource.

World-Water-Day-Save-Water-Save-Water-Save-Nature

 Today, the Internet of Things (IoT) – a blooming worldwide network of devices and appliances linked to the internet – has materialized as a propitious solution to save water and protect clean drinking water, especially in cities. 

To begin our discussion, Netherlands is on its way to develop a pioneering program to address the relevant problems of increasing sea levels, surging number of droughts and the effect of extreme weather changes on its trains, bus networks and roadways, and the efficiency with which the entire nation tackles situations like this. The ambitious project, Digital Delta draws in local and regional water jurisdictions, top-notch scientists and proliferating businesses to implement Big Data technology for upgrading the systems of its €7 billion water management, while restricting the costs of preserving water by 15%.

Prophecies about Urban Centres
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Plummeting freshwater resources: a serious challenge faced by the global population is now at its apex. An overwhelming 89 percent of the world population thrives on enhanced water supply systems, which results in a loss of more than 32 billion cubic meters of fresh water, through physical leakage. Thereby, more than 50 percent of world population will be vulnerable in water-stressed regions by 2025. And by 2040, the figures will further push the energy demand by 56%, making US the second highest energy consumer across the globe.

Saving Water Globally

In the meantime, most of the world cities should re-invent and re-structure their assets to pull together advanced functions encompassing different complex systems and to associate with new powerful allies. Urbanization comes with its own costs. Day by day, these networks are growing more complicated and even more expensive. By delving deeper into the interconnections of systems, the societies will be in a better position to grasp how to run them more efficiently.

Water has never grabbed eyeballs, as it has today. Many countries are not at all prepared to manage such burgeoning complexities of water management. Besides, water management authorities are constantly under pressure to harness their power for flood protection and drinking water standards.

Reality Check: Water demand is set to rise by 30% by 2030. Ever increasing population and swelling urbanization are the reasons behind such calamitous figures.

Smart City Technology – The Key to Urban Sustainability

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New Jersey Institute of Technology (NJIT) revealed that by 2025 smart city technologies would multiply to an industry estimating $27.5 billion. Moreover, nearly 88 smart cities will develop by the end of 2025. Smart cities whirl around the concept of using improved, interconnecting technologies to make environment safe, lives easier and urban living cost-effective and more efficient.

Societies are enduring new weather extremes. It is the high time to use big data and analytical science to cure the growing complexities in managing our water systems. Smart technology is the only viable option that can take future generations towards a sustainable future.

Seeking data science courses online? Visit us at DexLab Analytics. We offer a wide array of highly interactive online courses in data science.

 

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The Worst Techniques To Build A Predictive Model

While some of these techniques may be a little out of date and most of them have evolved over time greatly, for the past 10 years rendering most of these tools completely different and much more efficient to use. But here are few bad techniques in predictive modelling that are still widely in use in the industry:

 

Predictive Model

 

1. Using traditional decision trees: usually too large decision trees are usually really complex to handle and almost impossible to analyze for even the most knowledgeable data scientist. They are also prone to over-fitting which is why they are best avoided. Instead we recommend that you combine multiple small decision trees into one than using a single large decision tree to avoid unnecessary complexity.

Continue reading “The Worst Techniques To Build A Predictive Model”

Why the Job Market is Going Gaga over Big Data

We will start off this post with a little bit of trivia.

  • The advertised median salary on offer for technically inclined professionals with expertise in Big Data, which today is a highly sought-after skill is no less than $124,000 inclusive of compensation and bonuses.
  • Cisco, IBM and Oracle together had 26,488 positions that were open during the previous year which required expertise in Big Data.
  • EMC or Dell required 25.1% of all positions in Big Data to have analytics tracks.
  • Data Warehousing, VMWare and developing programming expertise in Python are the fastest growing skill sets that are in demand by companies that are on an expansion of their development teams in Big Data.

why the job market is going gaga over big data

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Decades On, SAS is Still the Market Leader

In the 2016 February report by Gartner, SAS bagged the top slot in its execution ability and was once again placed in the quadrant of leaders in the Magic Quadrant for Advanced Analytics Platforms.According to the description, as provided by Gartner, advanced analytics involves various sorts of data analysis through the use of quantitative methods of great sophistication like machine learning, statistics, simulation, data mining in its both predictive and descriptive forms as well as optimization.

 

Decades On , SAS Still The Leader

 

The goal is come up with insights that are unlikely to be discovered through approaching business intelligence traditionally like query and reporting.

Continue reading “Decades On, SAS is Still the Market Leader”

Infographic: How Big Data Analytics Can Help To Boost Company Sales?

Infographic: How Big Data Analytics Can Help To Boost Company Sales?

Following a massive explosion in the world of data has made the slow paced statisticians into the most in-demand people in the job market right now. But why are all companies whether big or small out for data analysts and scientists?

Companies are collecting data from all possible sources, through PCs, smart phones, RFID sensors, gaming devices and even automotive sensors. However, just the volume of data is not the main factor that needs to be tackled efficiently, because that is not the only factor that is changing the business environment, but there is the velocity as well as variety of data as well which is increasing at light speed and must be managed with efficacy.

Why data is the new frontier to boost your sales figures?

Earlier the sales personnel were the only people from whom the customers gathered data about the products but today there are various sources from where customers can gather data so people are no longer that heavily reliant on the availability of data.

Continue reading “Infographic: How Big Data Analytics Can Help To Boost Company Sales?”

Historians Make Use of Predictive Modeling

Predictive Modelling

Predictive modeling figures at the top of the list of new techniques put in to use by researchers in order to make out key archeological sites. The methodology used is not that complex. It makes predictions on the location of archeological sites having for its basis the qualities that are common to the sites already known. And the best news is that it works like a charm. A group of archeologists working in the company Logan Simpson which operates out of Utah discovered no less than 19 individual archeological sites containing many biface blades as well as stone points in addition to other artifacts that belong to the Paleoarchaic Period which ranges from 7,000 to 12,000 years ago.

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The location of the site is about 160 km or 100 miles from Las Vegas, Nevada. The group of researchers also came across lakes and streams that disappeared long before. According to archeologists the sites were perhaps put into used by a number of groups of gatherers and hunters in the ancient times. The sites are scattered widely and also are scarce and could herald an understanding of the human activity that took place throughout the length and breadth of the Great Basin as a warmer climate prevailed after the end of the Ice Age. Their remoteness ensured that they remain unfound when traditional methods are employed.

How Predictive Analysis Could Have Saved the World from Ransomware – @Dexlabanalytics.

In Nevada’s Dry Lake Valley, Delamar Valley and Kane Springs archeologists have discovered sites like Clovis, Lake Mojave and also Silver Lake that contains some stone tools constructed according to styles prevalent as far as 12,000 years back.The project was funded by the Lincoln County Archeological Initiative from the Bureau of Land Management. It made use of GIS or geographic information system technology in order to make predictions about activity belonging to the Pleistocene-Holocene period.

 
Read Also: How Data Preparation Changed Post Predictive Analytics Model Implementation

 

The predictive modeling put into use took in to account the fact that the Great Basin was way more wet and cool at the end period of the Pleistocene than the climate prevalent today and in all probability had attracted the attention of gatherers and hunters for several centuries. The process of mapping with GIS and aerial pictures amongst others was followed by pinpointing and ranking the various locations that hold the most promise.

How Predictive Analysis Works With Data Mining – @Dexlabanalytics.

Apart from the Paleoarchaic era, artifacts belonging to relatively more recent periods in History were also found which bear out that the sites at the lakeside had been used over the course of several millennia.

But the most important discovery was the proof that that Predictive Modeling on the basis of GIS works well and should be included in the arsenal of tools of archeologists trying to discover prehistoric sites .

 
Read Also: Predictive Analytics: In conversation with Adam Bataran, Managing Director of GTM Global Salesforce Platforms at Bluewolf
 

Make predictive analytics your best friend for life and career with easy and comprehensive SAS training courses in Delhi by DexLab Analytics. For more information about this premier SAS training institute, log into their website.

 

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Role of R In Business Intelligence

To put it simply Business Intelligence is the action of extracting and to derive information that may be of use from the available data. As might be evident the process is a broad one where the quality and the source of the data structure is variable. Transformations like this might in technical terms be described as ETL or extract, transform and load in addition to the presentation of information that is of use.

 

role of r in business intelligence

R Programming in Business Intelligence

Some R Programming Experts hold that R is fully able to take on the role of the engine for processes related to BI. Here we will focus only on the BI function of R i.e. to extract, transform load and present information and data. The following packages correspond to indicated processes in Business Intelligence.

 

Extract

 

Extraction

 

  •  RODBC
  • DBI
  • data.table’s fread
  • RJDBC

 


 

In addition to these, there are several other packages that support data in a variety of formats.

 

Transform

 

  • data.table
  • dplyr

 

Load

 

  • DBI
  • RODBC
  • RJDBC

 

Let’s Take Your Data Dreams to the Next Level

 

Prsentation

 

Presenting data is a wholly different ball game than the previously mentioned process of ETL. Never fear, it may be outsourced with ease to tools of BI dashboard with ease by populating the structure of data according to the expectations of the particular data tool. R is able to create a dashboard of a web app directly from within itself through packages like:

 

  •  shiny
  • httpuv
  • opencpu
  • rook

 

These packages let you play host to interactive web apps. They have the ability to query the data in an interactive manner and generate interactive plots. The basis for all of these is an R session engine and is able to execute all functions of R and may leverage the capabilities of statistics of all packages in R.

 

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Extras

 

The above mentioned packages serve as the core whose functionality may be simplified through the use of the packages mentioned below:

 

  • db.r
  • ETLUtils
  • Sqldf
  • Dplyr
  •  shinyBI
  • dwtools

 


 

The following factors are critical while R is adopted by businesses:

 

  • Extraction / Loading
  • Performance and scalability
  • Presentation
  • Support and licensing

 

For more details on R Programming, get yourself enrolled in superior R programming courses in Pune. R programming certification in Pune by DexLab Analytics is extremely popular.

 

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