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How to Start a Successful Data Science Career?

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How to Start a Successful Data Science Career?

The most common question we come across in DexLab Analytics HQ is how to take a step into the world of analytics and data science. Of course, grabbing a data science job isn’t easy, especially when there is so much hype going around. This is why we have put together top 5 ways to bag the hottest job in town. Follow these points and swerve towards your dream career.

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Enhance Your Skills

At present, LinkedIn in the US alone have 24,697 vacant data scientist positions. Python, SQL and R are the most common skills in demand followed by Tensorflow, Jupyter Notebooks and AWS. Gaining statistical literacy is the best way to grab these hot positions but for that, you need hands-on training from an expert institute.

If interested, you can check out analytics courses in Delhi NCR delivered by DexLab Analytics. They can help you stay ahead of the curve.

Create an Interesting Portfolio

A portfolio filled with machine learning projects is the best bet. Companies look for candidates who have prior work experience or are involved in data science projects. Your portfolio is the potential proof that you are capable enough to be hired. Thus, make it as attractive as possible.

Include projects that qualify you to be a successful data scientist. We would recommend including a programming language of your choice, your data visualization skill and your ability to employ SQL.

Get Yourself a Website

Want to standout from the rest? Build up your website, create a strong online presence and continuously add and update your Kaggle and GitHub profile to exhibit your skills and command over the language. Profile showcasing is of utmost importance to get recognized by the recruiters. A strong online presence will not only help you fetch the best jobs but also garner the attention of the leads of various freelance projects.

Be Confident and Apply for Jobs You Are Interested In

It doesn’t matter if you possess the skills or meet the job requirements mentioned on the post, don’t stop applying for the jobs that interest you. You might not know every skill given on a job description. Follow a general rule, if you qualify even half of the skills, you should apply.

However, while job hunting, make sure you contact recruiters, well-versed in data science and boost your networking skills. We would recommend you visit career fairs, approach family, friends or colleagues and scroll through company websites. These are the best ways to look for data science jobs. 

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Improve Your Communication Skills

One of the key skills of data scientists is to communicate insights to different users and stakeholders. Since data science projects run across numerous teams and insights are often shared across a large domain, hence superior communication skill is an absolute must-have.

Want more information on how to become a data scientist? Follow DexLab Analytics. We are a leading data analyst training institute in Delhi offering in-demand skill training courses at affordable prices.

 

The blog has been sourced from ― www.forbes.com/sites/louiscolumbus/2019/04/14/how-to-get-your-data-scientist-career-started/#67fdbc0e7e5c

 

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

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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 from ― www.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

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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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Basics of a Two-Variable Regression Model: Explained

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Basics of a Two-Variable Regression Model: Explained

In continuation of the previous Regression blog, here we are back again to discuss the basics of a two-variable regression model. To read the first blog from the Regression series, click here www.dexlabanalytics.com/blog/a-regression-line-is-the-best-fit-for-the-given-prf-if-the-parameters-are-ols-estimations-elucidate.

In Data Science, regression models are the major driver to interpret the model with necessary statistical methods, practically as well as theoretically. One, who works extensively with business data metrics, will be able to solve various tough problems with the help of a regression theory. The key insight of the regression models lies in interpreting the fitness of the models. But it differs from the standard machine learning techniques such that, for improvement in the performance of the model being predicted, the major interpretable coefficients are never sacrificed. Thus, a sense in regression models can be considered as the most important tool to be chosen for solving any practical problem.

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Let’s consider a simple example to understand regression analysis from scratch. Say, we want to predict the sales of a Softlines eCommerce company for this year during the festivals of Diwali. There are a lot of factors to generate impacts on the sales value, as there are hundreds of factors persisting within the model. We can consider our own judgement to get the impacting factors. Now, here in our model, the value of sales that we want to predict is the dependent variable, whereas the impacting factors are considered as the independent variables. To analyse this model in terms of regression, we need to gather all the information about the independent variables from the past few years, and then act on it according to the regression theory.

Before getting into the core theory, there are some basic assumptions for such a two-variable regression model and they are as follows:

  • Variables are linearly related: The variables in a 2-variable Regression Model are linearly related, the linearity being in parameters, though not always in variables, i.e. the power in which the parameters appear should be of 1 only and should not be multiplied or divided by any other parameters. These linearly related variables are basically of two types (i) independent or explanatory variables & (ii) dependent or response variables.
  • Variables can be represented graphically: The idea behind this assumption guarantees that observations must be real numbers represented on graph papers.
  • Residual term and the estimated value of the variables are uncorrelated.
  • Residual terms and explanatory variables are uncorrelated.
  • Error variables are uncorrelated with mean 0 & common variance σ2

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Now, how can a PRF for expanding an economic relationship between 2 variables be specified?

Well, Population regression function, or more generally, the population regression curve, is defined as the locus of the conditional means of the dependent variables, for a fixed value of the explanatory variables. More simply, it is the curve connecting the means of the sub-populations of Y corresponding to the given values of the regressor X.

Formally, a PRF is the locus of all conditional means of the dependent variables for a given value of the explanatory variables. Thus; the PRF as economic theory would suggest would be:

Where 9(X) is expected to be an increasing function of X, if the conditional expectation is linear in X. then

Hence, for any ith observations:

However, the actual observation for the dependent variable is Yi. Therefore; Yi – E(Y/Xi) = ui, which is the disturbance term or the stochastic term of the Regression Model.

Thus,

…………………… (A)

  • is the population regression function and this form of specifying the population regression function is called the stochastic specification of the PRF.

Stochastic Specification of the Model:

Yi = α + βXi + ui is referred to as the stochastic specification of the Population Regression Function, where ui is the stochastic or the random disturbance term. It explains everything’s net influence other than X variable on the ith observation. Thus, ui is a surrogate or proxy for all omitted or neglected variables which may affect Y but is not included in the model. The random disturbance term is incorporated into the model with the following assumptions:-

Proof:

Taking conditional expectation as both sides, we get:

Hence; E(ui) = 0

cov(ui,uj) = E(ui uj ) = 0 ∀ i ≠ j i.e. the disturbance terms are distributed independently of each other.

Proof:

Two variables are said to be independently distributed, or stochastically independent; if the conditional distributions are equal to the corresponding marginal distributions.

Hence; cov(ui,uj )= E(ui uj ) = 0 Thus, no auto correction is present among ui,s i.e. ui,s. s are identically and independently distributed Random Variables. Hence, ui,s are all Random Samples.

Proof:

The conditional variance between two error terms can be given as given independence &

 

 

All these assumptions can be embodied in the simple statement: ui~N(0,σ2) where ui,s are iid’s ∀ I, Which heads “the ui are independently distributed identically distributed with mean 0 & variance σ2”.

Last Notes

The benefits of regression analysis are immense. Today’s business houses literally thrive on such analysis. For more information, follow us at DexLab Analytics. We are a leading data science training institute headquartered in Delhi NCR and our team of experts take pride in crafting the most insight-rich blogs. Currently, we are working on Regression Analysis. More blogs are to be followed on this model. Keep watching!

 

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A Regression Line Is the Best Fit for the Given PRF If the Parameters Are OLS Estimations – Elucidate

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A Regression Line Is the Best Fit for the Given PRF If the Parameters Are OLS Estimations - Elucidate

Regression analysis is extensively used in business applications. It’s one of the most integral statistical techniques that help in estimating the direction and strength between two or more (financial) variables – thus determining a company’s sales and profits over the past few years.

In this blog, we have explained how a regression line is the best fit for a given PRF if the parameters are all OLS estimations.

The OLS estimators for a given regression line has been obtained as: a = y ̅ – bx ̅ and b = (Cov(x,y))/(v(x)). The regression line on the basis of these OLS estimate has been given as: Y ̂_ i-Y ̅ = b(x_i-x ̅ )….. (1)

The regression line (1) constructed above is a function of the least square i.e. the parameters of the regression equation have been selected so that the residual sum of squares is minimized. Thus, the estimators ‘a’ & ‘b’ explains the population parameters, the best relative to any other parameters. Given, the linearity of the parameters, these estimators share the minimum variations with the population parameters, i.e. they explain the maximum variations in the model, in relation to the population parameters, as compared to any other estimators, in a class of unbiased estimators.

Thus, the regression line would be the ‘best fit’ for a given PRF. If ‘a’ & ‘b’ are best linear unbiased estimators for  respectively. Thus, to show ‘best fit’, we need to prove:

  1. To ‘b’ is Best unbiased estimator for :-

From the OLS estimation; we have ‘b’ as:

i.e.b is a linear combination of w’is & y’is.

Hence; ‘b’ is a linear estimator for β. Therefore, the regression line would be linear in parameters as far as ‘b’ is concerned.

Now,

Let us test for the prevalence of this conditions:

For unbiasedness, we must have :- E(b)=β. To test this, we take expectation on both sides of (3) & get:

From (1) & (4) we can say that ‘b’ is a linear unbiased estimator for β.

To check whether ‘b’ is the best estimator or not we need to check whether it has the minimum variance in a class of linear unbiased estimator.

Thus, we need to calculate the variance for ‘b’ & show that it is the minimum in a class of unbiased estimations. But, first, we need to calculate the variance for ‘b’.

Now; we need to construct another linear unbiased estimator and find its variance.

Let another estimator be: b^*=∑ci yi….(6)  For unbiasedness ∑ci =0,∑cixi =1.

Now; from (6) we get:

∴b* is an unbiased estimator for  Now; the variance for  can be calculated as:-

Now;

Hence; from (9) we can say V(b) is the least among a class of unbiasedness estimators.

Therefore, ‘b’ is the best linear unbiased estimator for  in a class of linear unbiased estimators.

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  1. To prove ‘a’ is the best linear unbiased estimator for α:-

Form the OLS estimation we have ‘a’ as:-

Here; ‘b’ is a function of Y and Y is a linear function of X(orUi).

‘a’ is also a linear function of Y. i.e. has linearity.

There, ‘a’ is a linear estimator for   ……. (11)

Now, for ‘a’ to be an unbiased estimator; we must have From (10) we have:-

Taking expectations on both sides of the equation; we get:

Therefore, ‘a’ is an unbiased estimator for  ……… (12)

From (11) & (12) ‘a’ is a linear unbiased estimator for

Now, if ‘a’ is to be the best estimator for then is most have the minimum variance. Thus; we first need to calculate the variance of ‘a’.

Now, 

Now; let us consider an estimator in the class of linear unbiased estimator.

Further we know,

Now;

Hence;

Now;

Therefore;

Hence; from (16) we can say that is the Min Variance Unbiased estimator in a class of unbiased estimator.

Hence; we can now safely conclude that a regression line composed of OLS estimators is the ‘best fit’ line for a given PRF, compared to any other estimator.

Thus, the best-fit regression line can be depicted as

Thus, a regression line is the best fit for a given PRF if the estimators are OLS.

End Notes

The beauty and efficiency of Regression method of forecasting never fail to amaze us. The way it crunches the data to help make better decisions and improve the current position of the business is incredible. If you are interested in the same, follow us at DexLab Analytics. A continues blog series on regression model and analysis is upcoming. Watch this space for more.

DexLab Analytics offers premium data science courses in Gurgaon crafted by the experts. After thorough research, each course is prepared keeping student’s needs and industry demands in mind. You can check out our course offerings here.

 

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Upskill and Upgrade: The Mantra for Budding Data Scientists

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Upskill and Upgrade: The Mantra for Budding Data Scientists

Have the right skills? Then the hottest jobs of the millennium might be waiting for you! The job profiles of data analysts, data scientists, data managers and statisticians harbour great potentials.

However, the biggest challenge in today’s age lies in preparing novice graduates for Industry 4.0 jobs. Although no one has yet cleared which roles will cease to exist and which new roles will be created, the consultants have started advising students to imbibe necessary skills and up-skill in domains that are likely to influence and carve the future jobs. Becoming adaptive is the best way to sail high in the looming technology-dominated future.

Data Science and Future

In this context, data science has proved to be one of the most promising fields of technology and science that exhibits a wide gap between demand and supply yet an absolute imperative across disciplines. “Today there is no shortage of data or computing abilities but there is a shortage of workforce equipped with the right skill set that can interpret data and get valuable insights,” revealed James Abdey, assistant professorial lecturer Statistics, London School of Economics and Political Science (LSE).

He further added that data science is a multidisciplinary field – drawing collectives from Economics, Mathematics, Finance, Statistics and more.

As a matter of fact, anyone, who has the right skill and expertise, can become a data scientist. The required skills are analytical thinking, problem-solving and decision-making aptitude. “As everything becomes data-driven, acquiring analytical and statistical skill sets will soon be imperative for all students, including those pursuing Social Sciences or Liberal Arts and also for professionals,” said Jitin Chadha, founder and director, Indian School of Business and Finance (ISBF).

DexLab Analytics is one of the most prominent deep learning training institutes seated in the heart of Delhi. We offer state-of-the-art in-demand skill training courses to all the interested candidates.

The Challenges Ahead

The dearth of expert training faculty and obsolete curriculum acts as major roadblocks to the success of data science training. Such hindrances cause difficulty in preparing graduates for Industry 4.0. In this regard, Chiraag Mehta from ISBF shared that by increasing international collaborations and intensifying industry-academia connect, they can formulate an effective solution and bring forth the best practices to the classrooms. “With international collaborations, higher education institutes can bring in the latest curriculum while a deeper industry-academia connect including, guest lecturers from industry players and internships will help students relate the theory to real-world applications, ” shared Mehta during an interview with Education Times.

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Industry 4.0: A Brief Overview

The concept Industry 4.0 encompasses the potential of a new industrial revolution – where gathering and analyzing data across machines will become the order of the day. The rise of this new digital industrial revolution is expected to facilitate faster, more flexible and efficient processes to manufacture high-quality products at reduced costs – thus, increasing productivity, switch economies, stimulate industrial growth and reform workforce profile.

Want to know more about data science courses in Gurgaon? Feel free to reach us at DexLab Analytics.

 

The blog has been sourced from ― timesofindia.indiatimes.com/home/education/news/learn-to-upskill-and-be-adaptive/articleshow/68989949.cms

 

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Study: The Demand for Data Scientists is Likely to Rise Sharply

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Study: The Demand for Data Scientists is Likely to Rise Sharply

Data is like the new oil. A large number of companies are leveraging artificial intelligence and big data to mine these vast volumes of data in today’s time. Data science is a promising landmine of job opportunities – and it’s high time to consider it as a successful career avenue.

The prospect of data science is skyrocketing. Today, it is estimated that more than 50000 data science and machine learning jobs are lying vacant. Plus, nearly 40000 new jobs are to be generated in India alone by 2020. If you follow the global trends, the role of data scientist has expanded over 650% since 2012 yet only 35000 people in the US are skilled enough.

Data scientists are like the platform that connects the dots between programming and implementation of data to solve challenging business intricacies – says Pankaj Muthe, Academic Program Manager (APAC), Company Spokesperson, QlikTech. The company delivers intuitive platform solutions for embedded analytics, self-service data visualizations and guided analytics and reporting across the globe.

According to a pool of experts, data science is the hottest job trend of this century and is the second most popular degree to have at the master level next to MBA. No wonder, this new breed of science and technology is believed to be driving a new wave of innovation! Data scientists and front-end developers attracted the highest remuneration across Indian startups throughout 2017.

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Eligibility Criteria

To become a professional data scientist, a degree in computer science/engineering or mathematics is a must. Most of the data scientists have a knack for intricate tasks and aptitude to learn challenging programming languages. Any good organization seeks interested and intelligent candidates with the zeal to learn more. The subjects in which they need to be proficient are mathematics, statistics and programming. Moreover, data science jobs need a very sound base in machine learning algorithms, statistical modeling and neural networks as well as incredible communication skills.

Today, a lot of institutes offer state-of-the-art data science online courses that prove extremely beneficial for career growth and expansion. Combining theoretical knowledge and technical aspects of data science training, these institutes provide skill and assistance to develop real-world applications. DexLab Analytics is one such institute that is located in the heart of Delhi NCR. For more, feel free to reach us at <www.dexlabanalytics.com>

Future Prospects

After land, labour and capital, data ranks as the fourth factor of production. According to the US Department of Statistics, the demand for data engineers is likely to grow by 40% by 2020. If you are looking for a flourishing career option, this is the place to be: an entry-level engineer begins their career as a business analyst and then proceeds towards becoming a project manager. Later, after years of experience, these virgin business analysts further get promoted to become chief data officers.

 

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Know All about Usage-Driven Grouping of Programming Languages Used in Data Science

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Know All about Usage-Driven Grouping of Programming Languages Used in Data Science

Programming skills are indispensable for data science professionals. The main job of machine learning engineers and data scientists is drawing insights from data, and their expertise in programming languages enable them to do this crucial task properly. Research has shown that professionals of the data science field typically work with three languages simultaneously. So, which ones are the most popular? Are some languages more likely to be used together?

Recent studies explain that certain programming languages are used jointly besides other programming languages that are used independently. With the survey data collected from Kaggle’s 2018 Machine Learning and Data Science study, usage patterns of over 18,000 data science experts working with 16 programming languages were analyzed. The research revealed that these languages can actually be categorized into smaller sets, resulting in 5 main groupings. The nature of the groupings is indicative of specific roles or applications that individual groups support, like analytics, front-end work and general-purpose tasks.

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Principal Component Analysis for Dimension Reduction

In this article, we will explain how Bob E. Hayes, PhD holder, scientist, blogger and data science writer has used principal component analysis, a type of data reduction method, to categorize 16 different programming languages. Herein, the relationship among various languages is inspected before putting them in particular groups. Basically, principal component analysis looks into statistical associations like covariance within a large collection of variables, and then justifies these correlations with the help of a few variables, called components.

Principal component matrix presents the results of this analysis. The matrix is an nXm table, where:

n= total no. of original variables, which in this case are the number of programming languages

m= number of main components

The strength of relationship between each language and underlying components is represented by the elements of the matrix. Overall, the principal component analysis of programming language usage gives us two important insights:

  • How many underlying components (groupings of programming languages) describe the preliminary set of languages
  • The languages that go best with each programming language grouping

Result of Principal Component Analysis:

The nature of this analysis is exploratory, meaning no pre-defined structure was imposed on the data. The result was primarily driven by the type of relationship shared by the 16 languages. The aim was to explain the relationships with as less components as possible. In addition, few rules of thumb were used to establish the number of components. One was to find the number of eigen values with value greater than 1 – that number determines the number of components. Another method is to identify the breaking point in the scree plot, which is a plot of the 16 eigen values.

businessoverbroadway.com

 

5-factor solution was chosen to describe the relationships. This is owing to two reasons – firstly, 5 eigen values were greater than one and secondly, the scree plot showed a breaking point around 6th eigen value.

Following are two key interpretations from the principal component matrix:

  • Values greater than equal to .45 have been made bold
  • The headings of different components are named on the basis of tools that loaded highly on that component. For example, component 4 has been labeled as Python, Bash, Scala because these languages loaded highest on this component, implying respondents are likely to use Bash and Scala if they work with Python. Other 4 components were labeled in a similar manner.

Groupings of Programming Languages

The given data set is appropriately described by 5 tool grouping. Below are given 5 groupings, including the particular languages that fall within the group, meaning they are likely to be used together.

  1. Java, Javascript/Typescript, C#/.NET, PHP
  2. R, SQL, Visual Basic/VBA, SAS/STATA
  3. C/C++, MATLAB
  4. Python, Bash, Scala
  5. Julia, Go, Ruby

One programming language didn’t properly load into any of the components: SQL. However, SQL is used moderately with three programming languages, namely Java (component 1), R (component 2) and Python (component 4).

It is further understood that the groupings are determined by the functionality of different languages in the group. General-purpose programming languages, Python, Scala and Bash, got grouped under a single component, whereas languages used for analytical studies, like R and the other languages under comp. 2, got grouped together. Web applications and front-end work are supported by Java and other tools under component 1.

Conclusion:

Data science enthusiasts can succeed better in their projects and boost their chances of landing specific jobs by choosing correct languages that are suited for the job role they want. Being skilled in a single programming language doesn’t cut it in today’s competitive industry. Seasoned data professionals use a set of languages for their projects. Hence, the result of the principal component analysis implies that it’s wise for data pros to skill up in a few related programming languages rather than a single language, and focus on a specific part of data science.

For more help with your data science learning, get in touch with DexLab Analytics, a leading data analyst training institute in Delhi. Also check our Machine learning courses in Delhi to be trained in the essential and latest skills in the field.

 
Reference: http://customerthink.com/usage-driven-groupings-of-data-science-and-machine-learning-programming-languages
 

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Discover Top 5 Data Scientist Archetypes

Posted on by Dexlab

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 from  ― www.analyticsindiamag.com/see-the-6-data-scientist-archetypes-you-will-find-in-every-organisation

 

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