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Want to Grow Quickly as a Data Scientist? Check Out 6 Ways

Want to Grow Quickly as a Data Scientist? Check Out 6 Ways

With the raging popularity of Data Science, only a few would be as unambitious as not choosing it as their field of work. Not only does Data Science open up a path long and promising for learning and attaining mastery but it also lets you get into the spotlight quicker than ever.

Most importantly, with the rising trend of Data Science, you can also shoot your career up.

Opting for Data Science, you can either be an employee in any of the distinguished IT sectors or you might also serve as a trainer, with your name all over the community.

But, as with all the other trades, marketing is important even when you seek for grounding your career in Data Science. But don’t worry because here we will give you some hacks to market yourself as a Data Scientist and grow as fast as feasible.

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Knowing the Inside Out of the Domain

Ensure that you have a deep knowledge of Data Science before starting to market yourself as a Data Scientist. This is because as more and more people are getting trained in Data Science and starting to pave their career in the same field, none but they with a steadfast knowledge would thrive. Furthermore, in this digital career, you shall also pledge to be always updated and Data Science Courses in Gurgaon can give you the edge.

So, it would prove to be indispensable if you invest a considerable amount of time to learn, on hands-on-experience, leading to chiselling your knowledge and skillset.

Delve into Social Media

When it comes to marketing, you shall never disregard Social Media. In fact, that is the platform which you must first target. Facebook, Twitter and LinkedIn is the trio that you must first address.

Navigate to your Social Media accounts as frequently as you can. There, try to make friends with the people of the same profession, interact with them, discuss various problems and highlight your feats.

Value your Content

As in marketing, the common phrase goes “Content is King”, the validity of this saying is never to be tested.

Like your friends from Media, Content Marketing and Digital Marketing, there is no alternative to create your content and build your own trust.

Note – Bad content and plagiarism are a strict no-no.

Speak Often

Data Science is a relatively new stream, meetings, conferences, discussions are happening almost all the time around the world. Hence, keep yourself aware of these events and try to participate in them both as a speaker as well as a diligent and inquisitive audience.

Grow this habit and you will be amazed at assessing the popularity of yourself incredibly fast.

Be Inclined to Help

Knowledge is always ought to be shared. If you discover that you have an irrefutable knowledge of something and someone is asking for help in your domain of expertise, then extend your helping hands to them. This way you will simply be recognised all the way more.

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Hackathons

For computer geeks and coders, Hackathons speak volumes. You should also try and participate in more such hackathons which are widely occurring. This will not only help you test your knowledge and understanding but will push you further and even help you extend the contacts in your professional field.

The points that we have highlighted here should surely help you be more marketable as a Data Scientist. So, keep these in mind and watch your career take a flight!

 

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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.

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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!

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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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Discover Top 5 Data Scientist Archetypes

Discover Top 5 Data Scientist Archetypes

Data science jobs are labelled as the hottest job of the 21st century. For the last few years, this job profile is indeed gaining accolades. And yes, that’s a good thing! Although much has been said about how to progress towards a successful career as a data scientist, little do we know about the types of data scientists you may come across in the industry! In this blog, we are going to explore the various kinds of data scientists or simply put – the data scientist archetypes found in every organization.

Generalist

This is the most common type of data scientists you find in every industry. The Generalist contains an exemplary mixture of skill and expertise in data modelling, technical engineering, data analysis and mechanics. These data scientists interact with researchers and experts in the team. They are the ones who climb up to the Tier-1 leadership teams, and we aren’t complaining!

Detective

He is the one who is prudent and puts enough emphasis on data analysis. This breed of data scientists knows how to play with the right data, incur insights and derive conclusions. The researchers say, with an absolute focus on analysis, a detective is familiar with numerous engineering and modelling techniques and methods.

Maker

The crop of data scientists who are obsessed with data engineering and architecture are known as the Makers. They know how to transform a petty idea into concrete machinery. The core attribute of a Maker is his knowledge in modelling and data mechanisms, and that’s what makes the project reach heights of success in relatively lesser time.

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Oracle

Having mastered the art and science of machine learning, the Oracle data scientist is rich in experience and full of expertise. Tackling the meat of the problem cracks the deal. Also called as data ninjas, these data scientists possess the right know how of how to deal with specific tools and techniques of analysis and solve crucial challenges. Elaborate experience in data modelling and engineering helps!

Unicorn

The one who runs the entire data science team and is the leader of the team is the Unicorn. A Unicorn data scientist is reckoned to be a data ninja or an expert in all aspects of data science domain and stays a toe ahead to nurture all the data science nuances and concepts. The term is basically a fusion version of all the archetypes mentioned above weaved together – the job responsibility of a data unicorn is impossible to suffice, but it’s a long road, peppered with various archetypes as a waypoint.

Organizations across the globe, including media, telecom, banking and financial institutions, market research companies, etc. are generating data of various types. These large volumes of data call for impeccable data analysis. For that, we have these data science experts – they are well-equipped with desirable data science skills and are in high demand throughout industry verticals.

Thinking of becoming a data ninja? Try data science courses in Delhi NCR: they are encompassing, on-point and industry-relevant.

 

The blog has been sourced fromwww.analyticsindiamag.com/see-the-6-data-scientist-archetypes-you-will-find-in-every-organisation

 

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How to Build and Maintain Successful Data Science Teams?

How to Build and Maintain Successful Data Science Teams?

Businesses are becoming smarter. They are unleashing a bigger impact. Driven by innovation and humongous volumes of data, organizations observe market trends and predict customer behavioral patterns – no wonder, this industry is the right place to incubate newer technologies and explore higher horizons.

Data science is the bull’s eye of this new-age industry. It is unabashedly predictive rather than being conclusive. As a result, garnering cross-team collaborations in this particular field of science may turn a bit challenging. A good data science team is a combination of talented professionals, high intellect, powerful body of knowledge and advanced data-tackling skills.

To give you a hand, we’ve rounded up top trends or tips to follow to get to the bottom of the art of running successful data science teams:

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Diversity is the Key

Diverse backgrounds, on-point technical expertise and voluminous domain knowledge is what makes a data science team high on diversity. A healthy concoction of machine learning skills, knowledge in mathematics and statistics and conversational skills is critical for a productive team. Just having one or two skills is simply not enough, anymore!

Structure and Prioritize

Once you have a team by your side, you need to start structuring an operating model. The data needs to be deconstructed into sizeable prioritized slices. After that, every data-related measure should be backed by needful communication – it helps in determining the bottlenecks and devise effective solutions.

Experimentation Helps

Experimentation is crucial as well as important. Unless you experiment, you can never scale new heights and this is equally applicable in data science. In the sprawling field of data science, every project starts with a challenge and a set of hypothesis that addresses it. However, you won’t find any particular roadmap to success. Hence, it opens a lot of room for innovation and experimentation.

Collective Responsibility

Yielding data science initiatives demand absolute cooperation, collaborative responsibilities and fine reporting structures. A healthy coordination between analytics and business teams, specifically IT, is extremely important for overall business success. Data science experts need to collaborate with each other and strike a tone of success.

Data Accuracy

Gain access to data bank and fine-tune the accuracy of your analysis. Business users leverage improved functional tools of analytics for overall business success. Data is the key, and data availability and quality are the pillars on which organizations stand. Therefore, we suggest practice data accuracy for improved data analytics and boost future business goals.

Today, online resources and libraries can help you almost everything. What they cannot do is feed you is the underlying intricacies of data science and how to devise an effective solution utilizing the base knowledge of mathematics, statistics and machine learning technology. For these, you need an expert Data Science Certification – it will help you discover the grey unknown territories of data and educate you on how to tame them.

Reach us at DexLab Analytics – we offer in-demand data science courses for students and professional, both.

 

The blog has been sourced fromwww.analyticsindiamag.com/the-art-of-running-successful-data-science-teams

 

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India and Big Data Analytics: The Statistics and Facts

India and Big Data Analytics: The Statistics and Facts

Data science, big data and analytics industry in India is expected to experience 8X growth hitting $16 billion by 2025 from the current $2 billion, experts say. Out of the terrific annual inflow to the analytics industry, nearly 11% can be ascribed to advanced analytics, data science and predictive analytics and a substantial 11% to big data.

In the next seven years, the Indian analytics industry will expand its horizons further and demand more analytics professionals to join the data bandwagon. Separately, the BI and analytics software market revenue in India will touch Rs 1980 crore in 2018, increasing at a rate of 18% per year. As a result, Indian companies and organizations are shifting their focus from traditional data reporting to augmented analytics tools that will not only enhance the process of data preparation and evaluation but will help predict the future outcomes, successfully.

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Trends in Analytics

Several sectors across the Indian industry of companies and startups have started embracing data analytics – no wonder, the data analytics landscape in India is growing rapidly, so is the revenue generation.

Contemporary, architecture-oriented data analytics tools are the order of the day. Rightfully so, the companies and budding startups are replacing tactical and traditional data analytics programs for more strategic approaches. The current breed of fast followers is even seeking hefty investments in advanced analytical solutions powered by AI, ML and Deep Learning. It would lessen the time taken to market and sharpen analytics offerings. Focused data management is bringing forth a rapid shift to the hybrid and cloud data management scenario – through iPaaS (Integration Platform as a Service) tools. Data lakes and hubs are also emerging here and there. They are in demand for ingesting and administering multi-structured data. Nevertheless, a lack of talent pool will cost the industry immensely. It can be a major deterring factor towards their seamless adoption.

It’s about time to be data-smart with an excellent data analyst certification from the experts. Headquartered in Delhi, DexLab Analytics is one of the prime data analyst training institutes that will help you stay ahead of the curve, especially data curve!

Statistics of Data Analysis

Geographically speaking, more than 64% of revenue generated from data analytics in India comes from the USA. We are a leading exporter of data analytics to the US, taking figures to as high as $1.7 billion. In the FY18 alone, the revenue generation from the US has increased by 45%. Next, ranks the UK with 9.6% revenue generation. Technically, analytics revenue generation in India has almost doubled from last year – in terms of countries Poland, UAE, New Zealand, Belgium, Romania & Spain. Furthermore, Indian analytics firms are not left far behind in the data game – they contribute 4.7% of analytics revenues to Indian analytics market.

Well, it seems India is doing pretty good in terms of adopting cutting data analytics technology and reaping fetching benefits. If interested in data analytics, don’t stay behind. Reach us at DexLab Analytics and throw your queries right away.

 

The blog has been sourced from ― www.dqindia.com/india-analyzes-big-data-science-analytics-market-india

 

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How Students Select a Good Data Science Course?

How Students Select a Good Data Science Course

Data science and analytics are in hype. This time, we decided to know what students look for while arming themselves in this new age field of study. For that, we bring you Analytics India Magazine’s recent survey.

We are on an interesting endeavor to tap into the key areas that IT professionals and aspiring candidates look up to for lessening the learning gap. Ready to join us?

Disclaimer – the below opinions are from budding data scientists – from young IT employees to fresh graduates; we have compiled them and presented in a concise way. All thanks to AIM.

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What key element to consider in a data science or analytics course?

For students, there are many preconceived notions about a course’s curriculum, faculty, brand name and even fellow batch mates. No wonder, it’s always tricky to focus only on a single key element.

Nevertheless, going by the survey, the respondents voted the most for course content, only to be seconded by hands-on experience. Yes, course content is the life and soul of data science and analytics training program. But, it’s not enough, it has to be supplemented by good hands-on experience and placement opportunities.

For more,

What should be the duration of the data science or analytics course?

Short-term or long-term? This is a very common question plaguing the minds of interested candidates –in the recent survey, more than 66% of respondents said they would choose short-term programme over long-term, and almost 55% said that they would prefer part-time skill-training programme than full-time.

What format would you chose for data science training courses?

Always, course curriculum should be in an easy to learn format. When the expert guys at AIM asked the respondents what kind of format do they prefer for their educational course, this is what they revealed:

  • 47% or more voted for a hybrid format of education
  • 28% said they prefer online learning method
  • Less than 25% of the candidates said they would like to stick to the old-school classroom method of teaching

What about Capstone Projects and Placements?

Capstone Projects are important. 92% of respondents vouched for that.

Another 57% said that placements are crucial too if you are thinking of making a mark in the competitive tech industry. Up-skilling is the key in today’s world.

When is the best time to opt for a data science course?

There’s nothing like the best time to enroll in a data science and analytics course. Anytime, you can start learning. However, the 43% of respondents believe that it’s better to take up business analyst training course right after graduation or post graduation.

On the other hand, 33% think that gaining some work experience prior to start training would be helpful.

For more such updates, watch this space.

If you are looking for a decent data analyst training institute in Gurgaon, DexLab Analytics fits the bill right. Drop by their site and gather information.

 

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Top 5 Industry Use Cases of Predictive Analytics

Top 5 Industry Use Cases of Predictive Analytics

Predictive analytics is an effective in-hand tool crafted for data scientists. Thanks to its quick computing and on-point forecasting abilities! Not only data scientists, but also insurance claim analysts, retail managers and healthcare professionals enjoy the perks of predictive analytics modeling – want to know how?

Below, we’ve enumerated a few real-life use cases, existing across industries, threaded with the power of data science and predictive analytics. Ask us, if you have any queries for your next data science project! Our data science courses in Delhi might be of some help.

Customer Retention

Losing customers is awful. For businesses. They have to gain new customers to make up for the loss in revenue. But, it can cost more, winning new customers is usually hailed more costly than retaining older ones.

Predictive analytics is the answer. It can prevent reduction in the customer base. How? By foretelling you the signs of customer dissatisfaction and identifying the customers that are most likely to leave. In this way, you would know how to keep your customers satisfied and content, and control revenue slip offs.

Customer Lifetime Value

Marketing a product is the crux of the matter. Identifying customers willing to spend a large part of their money, consistently for a long period of time is difficult to find. But once cracked, it helps companies optimize their marketing efforts and enhance their customer lifetime value.

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Quality Control

Quality Control is significant. Over time, shoddy quality control measures will affect customer satisfaction ratio, purchasing behavior, thus impacting revenue generation and market share.

Further, low quality control results in more customer support expenses, repairs and warranty challenges and less systematic manufacturing. Predictive analytics help provide insights on potential quality issues, before they turn into crucial company growth hindrances.

Risk Modeling

Risk can originate from a plethora of source, and it can be any form. Predictive analytics can address critical aspects of risk – it collects a huge number of data points from many organizations and sort through them to determine the potential areas of concern.

What’s more, the trends in the data hint towards unfavorable circumstances that might impact businesses and bottom line in an adverse way. A concoction of these analytics and a sound risk management approach is what companies truly need to quantify the risk challenges and devise a perfect course of action that’s indeed the need of the hour.

Sentiment Analysis

It’s impossible to be everywhere, especially when being online. Similarly, it’s very difficult to oversee everything that’s said about your company.

Nevertheless, if you amalgamate web search and a few crawling tools with customer feedback and posts, you’d be able to develop analytics that’d present you an overview of the organization’s reputation along with its key market demographics and more. Recommendation system helps!

All hail Predictive Analytics! Now, maneuver beyond fuss-free reactive operations and let predictive analytics help you plan for a successful future, evaluating newer areas of business scopes and capabilities.

Interested in data science certification? Look up to the experts at DexLab Analytics.

The blog has been sourced fromxmpro.com/10-predictive-analytics-use-cases-by-industry

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World’s Biggest Tech Companies 2018: A Comprehensive List

World’s Biggest Tech Companies 2018: A Comprehensive List

Talking of world’s biggest and most-valued companies, people instinctively turn their gaze to technology sector. Ever since the phenomenal dotcom boom and onset of WorldWideWeb, the tech firms have been garnering accolades owing to their huge market caps and power to disrupt conventional industries.

FYI: A public company’s market cap refers to market capitalization, which is a measurement of the value of its current outstanding shares. To calculate the market cap, you need to just multiply the current stock price with the outstanding number of shares. Talking about today’s market condition that would mean a lot of numbers.

To evaluate the top notch tech companies across the globe, Howmuch.net took into consideration the market cap ranking given by Forbes and split it in an unique way. Obviously, the US and China houses some of the wealthiest companies, worth hundreds of billions of dollars.

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Below we’ve 10 most high-valued tech companies on the planet, according to their market caps as of October 2018:

  • Apple: $1.1T
  • com: $962B
  • Microsoft: $883B
  • Alphabet: $839B
  • Facebook: $460B
  • Alibaba: $412B
  • Tencent Holdings: $383B
  • Samsung Electronics: $297B
  • Cisco Systems: $224B
  • Intel: $222B

(Give credits)

“At first glance, retailing and media appear to be much more evenly distributed than they actually are,” the report indicated. “Consider how Amazon has so dominated the market that its North American competitors are so small, they don’t even make it onto the list of top 50 companies. Amazon is so big, there is literally no other company in sight.”

Key Takeaways:

  • As always, Apple tops the list of tech companies, not only as the biggest tech company but it’s also the eighth largest company in the world according to Forbes’ Global 2000 list. The company saw $247.5 billion in sales, $53 billion in profit, $367.5 billion in assets and a market cap of $927 billion for the past year.
  • The AntiTrust Regulations and growth of 5G wireless can bring forth major changes in the modern tech market, and we are eagerly waiting for such shift in focus.

As parting thoughts, we would like to say that though the current market setup has been quite steady for a while, a surge of change may soon be here. Interestingly, Chinese tech bigwig, Alibaba is mostly likely to expand its scopes and capabilities, while 5G connectivity may appear fetching. Moreover, the speculation says antitrust regulation could disrupt functionalities of some of these companies.

To stay updated about technology-related news and innovations, follow DexLab Analytics. It’s a premier institution famous for state of the art data science courses in Delhi. For more, check out their homepage: an army of data science related courses are on offer.

 
The blog has been sourced from — www.techrepublic.com/article/the-10-most-valuable-tech-companies-in-the-world
 

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