Connect with us

data scientists

Julia On The Upswing: Why Data Scientists Are Choosing Julia

Published

on

Julia On The Upswing: Why Data Scientists Are Choosing Julia

In the ever-developing field of data science, the onus is on data scientists, to keep track of developments in algorithms, technology stacks, databases, and languages. One such development is a programming language called Julia, which has received a fair bit of attention in the past few years because of its high speed and ease of use.

 

What is Julia? 

Julia, a newcomer to the programming languages for data science, is a high-level, general-purpose programming language, that was developed specifically for scientific computing. The developers of Julia, Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B. Shah, while coming from different backgrounds, were interested in the collective power of all programming languages. They wanted Julia to have the best of all the languages.

In short, Julia would be open-source with a liberal license, as fast as C, as general-purpose as Python, as statistics-friendly as R, easy to learn, and a compiled language. With that vision in mind, Julia’s first version went live in 2012.  

Advertisement

 

Julia’s claim to fame

 

There are many reasons why Julia is preferable in the Computation and Machine learning (ML) world: 

  • Free and Open Source: The license is held by MIT and the code is hosted on Git where everyone can view and make changes to it.
  • Parallelism: Julia was designed for parallel processing and provides primitives for parallel computing unlike Python and any other programming languages.
  • High execution speed: Julia matches the speed of C and FORTRAN, which are among the fastest languages. 
  • Compatible with Jupyter: It is compatible with Jupyter and many other IDEs such as VS Code and Vim.
  • Tailored for ML: It does not require external packages (such as NumPy for Python) for ML calculations. ‘Vanilla’ Julia supports matrices and equations.

 

Julia for Data Science

 Julia compared to Python and R

Julia was built to provide the best of what pre-existing languages offered. Python and R are the most widely used languages for ML, statistical analytics, and data visualization. Together, they have been ruling the data world, casting a shadow on other similar languages. But Julia has distinguished itself from the pack and has slowly been moving towards the light. It’s important to understand how Julia compares to the language giants: 

Benchmark time normalized against the C implementation 

Advertisement

Source: https://julialang.org/benchmarks/ 

 

Speed and Performance:

Using C as the benchmark for the fastest language, Python is slower than C and, R is slower than Python. Julia’s execution time, however, is comparable to that of C’s. This is because Julia is a compiled language whereas R and Python are interpreted. 

 

Sources/Libraries:

 A vast number of libraries and APIs are available for Python, whereas a lesser number is available for R. Being one of the new languages, there are limited libraries and APIs available for Julia. 

Advertisement

 

Community Support:

Python has a very large developer community and community support, whereas R has a comparatively smaller developer community. Julia, being in the initial stages, has a much smaller but growing developer community.

 

Machine Learning Support in Julia

 

Common libraries

• GmmFlow.jl

Advertisement

•  Clustering.jl

•  QuickShiftClustering.jl (Hierarchical clustering)

•  MultivariateStats.jl (PCA)

 

Julia has vast support for a range of problems in Machine Learning such as supervised learning, classification, regression, unsupervised learning, cluster analysis, dimensionality reduction.

It also has support for Deep Learning algorithms – ConvNet, TextRNN and many more.

Advertisement

 

Pros and Cons of Julia

 

Pros:

1.Julia’s speed and ease of implementation certainly makes it a desirable programming language for data science.

2.It has an intuitive syntax just like Python.

3.It has multiple wrapper libraries on top of Python libraries and a functionality to call Python functions.

Advertisement

4.It has support for Machine Learning algorithms.

 

Cons:

1.While its community support is not great, it is developing steadily.

2.Some wrapper libraries such as Pandas have slow execution in local Jupyter.

3.It has high initial compile time for imported libraries, and sometimes requires multiple libraries to perform a single task. For e.g., reading a csv as dataframe requires 2 libraries: DataFrames and CSV.

Advertisement

4.Some deep learning functions don’t have the same flexibility in parameter tuning as that of Python counterparts.

 

Julia on the rise

Julia was developed specifically for scientific computing. Since it went live, it has seen a wide range of applications across multiple industries. NASA has been using it to model animal, plant, and human migration patterns and their responses to climate change. BlackRock, one of the largest asset management companies, has been using Julia for time series data analytics and big-data applications. Even MIT has used Julia to program robots to climb stairs and walk on hazardous, difficult, and uneven terrain. 

The rise of data and data science has been exponential thereby increasing the importance of faster and simpler programming languages. Julia has a few more miles to go in developing its data science ecosystem i.e., documentation, community support, libraries, and packages but does great in terms of speed. Julia can potentially reduce time-to-market in places where code execution time is the major roadblock. It can also be experimented in places where simple ML algorithms are used, or complex ­­computations are performed as the community support is good for basic algorithms. Julia is evolving steadily and is a language to watch out for data science.

 

Advertisement

References

 

  1. https://julialang.org/blog/2012/02/why-we-created-julia
  2. https://juliacomputing.com/case-studies/
  3. https://www.section.io/engineering-education/why-julia-is-slowly-replacing-python-for-machine-learning-and-data-science/
  4. https://blog.goodaudience.com/10-reasons-why-you-should-learn-julia-d786ac29c6ca

 

Resources

1.Getting started – https://docs.julialang.org/en/v1/manual/getting-started/

2.ML Library – https://fluxml.ai/Flux.jl/stable/

3.Time series – https://discourse.julialang.org/t/simple-flux-lstm-for-time-series/35494

4.Sample Problems – https://github.com/FluxML/model-zoo

Advertisement

5.Julia docs – https://docs.julialang.org/en/v1/

 

Author:

Vedang Dalal, Lead Analyst, Merkle

 

The post Julia on the Upswing: Why Data Scientists are Choosing Julia appeared first on Analytics Insight.

Advertisement

Data Science

How To Get Your First Job As A Self-Taught Data Scientist In India?

Published

on

How To Get Your First Job As A Self-Taught Data Scientist In India?

Data scientists are gaining more prominence among major big tech companies than ever before

The 21st century is ruled by data and hence the demand for intelligent data scientists has been on the rise significantly. The domains of data science and machine learning have emerged as the most in-demand skills in the tech industry, but constant upskilling and specialization are also utterly important because the tech landscape is constantly evolving. The actual focus for tech aspirants lies in garnering the critical data science skills that are critical to be employable and excel in this profession. Enterprises are busy leveraging the utility of big data to generate insights that drive demand for data scientists across industry verticals at all enterprise skills. Coming to the scenario in India, data science has become equally important for Indian companies, hence, the demand for data scientists has grown impeccably over the past couple of years.

Understanding the basics of data science has become essential. However, as the popularity of data science grew, more and more professionals from different career paths have chosen to shift to data science, here is why the number of self-taught data scientists is rising significantly. The field of data science is full of potential and opportunities, and also offers lucrative financial packages. This is one of the major triggers why more and more self-taught data scientists are joining the ecosystem. In a nutshell, aspirations for being a data scientist have grown among tech professionals. However, the craze does not only stop at acquiring a data science career, several aspirants choose to learn data science skills to add value to their present roles, and gain an edge over the growing competition.

 

So, How do You Establish Yourself as Self-Taught Data Science in India?

Well, the answer to this question is pretty simple. The rules for gaining a foothold in the data science industry in India are quite similar to the ones in the global industry. To excel in this field an aspiring data scientist should primarily focus on deciding on a specialization. However, the difficulty of learning data science vastly depends on one’s background. Just like learning human languages, having an existing background in computer science and mathematics will help candidates take a leap in the self-learning process.

Advertisement

There are also several non-traditional approaches to learning data science, including online data science courses and programs, available on websites like edX, Coursera, and Udemy, to name a few. These online courses offer flexibility to the candidates. Besides, data science experts believe that the domain is about gaining practical years. Hence, candidates can initially start by downloading programs that explain programming languages, the different data science frameworks, and the tools that professionals use to gain insights from large datasets. Data scientists have to constantly explore the different resources that are available to gain proper knowledge about this evolving ecosystem.

 

Bottom Line

As mentioned earlier, to become a successful data scientist, upskilling, re-learning, or unlearning is quite important. The field has become a trend for aspiring tech professionals, it might be difficult and scary for self-learning data science aspirants, but determination and courage will help them go a long way. In fact, based on reports, several major big tech companies across the world are preferring to hire self-taught data scientists over college or university-graduated data professionals. It is mainly due to their courage and motivation to learn something completely new that is exciting modern business leaders to hire more self-taught data scientists.

The post How to Get Your First Job as a Self-Taught Data Scientist in India? appeared first on Analytics Insight.

Advertisement

Continue Reading

Data Science

Top 10 LinkedIn Groups Data Scientists Should Be A Part Of

Published

on

By

Top 10 LinkedIn Groups Data Scientists Should Be A Part Of

Here is a list of Top 10 LinkedIn groups for you to stay informed and up-to-date for data scientists.

Compared to other social networking platforms LinkedIn is the most professional network to gather, connect with each other, and share ideas. LinkedIn groups are a great place to pick up insights from experts and influencers who are data scientists around the world whom you can get connected with. Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. A data scientist requires large amounts of data to develop hypotheses, make inferences, and analyze customer and market trends. So here are the top 10 data science LinkedIn groups to consider joining.

Natural Language Processing People: This group is a job board for professionals in natural language processing, localization, data analysis, and machine learning. It offers information regarding employment opportunities in various areas of the language technology industry worldwide. Its mission is to turn a gap that currently exists in the job market for NLP into a useful tool for both job seekers and potential employers.

Data Scientists: This group aims to solve problems and create products for the scientific research community, providing assistance in enabling best practices in research, increasing the performance of new applications and data services, and decreasing the effort required in adopting new technologies.

Research Methods and Data Science: This group is dedicated to serving researchers, data scientists, and analysts worldwide. This group is for researchers, analysts, and practitioners to discuss research methodology, data science, research flow management, research automation, and augmentation.

Advertisement

Data Science Central: Data Science Central is a niche digital publishing and media company operating the leading and fast-growing Internet community for data science, machine learning, deep learning, big data, predictive, and business analytics practitioners.

Advanced Analytics, Predictive Modeling & Statistical Analyses Professionals Group: This group’s members are technology professionals with a common foundation of advanced quantitative education and experience in areas of advanced analytics, statistical modeling, data mining, and quantitative analyses. Group moderators encourage networking, collaboration, and sharing of career opportunities.

Advanced Analytics and Data Science: This group provides a resource for those who want to learn about and use advanced analytics and data science capabilities. And can meet people involved with predictive analytics, statistics, machine learning, and big data to have discussions and make connections with each other.

Data Science and Artificial Intelligence: This group was started with a vision to educate people who are in the space of analytics and big data. They disseminate their learning through announcements, group discussions, and postings. The group was developed with a vision to be the most active platform for networking, information sharing, and education.

KDnuggets Machine Learning, Data Science, Data Mining, Big Data, AI: This is a group for data analytics, data mining, and data science professionals and researchers who are interested in solving real-world problems. The forum has 18,000 members currently and remains active and engaging, with its users regularly posting some of the most informative content of any of the groups on this list.

Advertisement

Data Warehouse – Big Data – Hadoop – Cloud – Data Science – ETL: This is a group for people to connect with other professionals involved in data warehousing, big data, Hadoop, cloud computing, and data science. The group openly welcomes job recruiters as well, making it an interesting place for prospects to build their network.

Data Mining, Statistics, Big Data, Data Visualization, AI, Machine Learning, and Data Science: This is a group of data mining and statistical professionals who wish to expand their network of people and share ideas.

More Trending Stories 

The post Top 10 LinkedIn Groups Data Scientists Should Be a Part of appeared first on .

Advertisement
Continue Reading

Data Science

Why Do Self-Taught Data Scientists See Slow Progress In Their Career?

Published

on

Why Do Self-Taught Data Scientists See Slow Progress In Their Career?

Exploring the reasons behind the slow career progress of the self-taught data scientists.

The sexiest job title for data scientists is not a hoax at all! The field of data science is full of potential and opportunities. A general search on the platform of Indeed for “data scientist” returns over 15,000 data science jobs, many of which pay in the $90k to well over $100k salary range. Now, it’s only natural that people have their eyes set on honing the skills of data science as they used to for doctors and engineers back in the day. Data scientist is not the only job role, however, where data science skills are valuable. Experts believe that learning data science skills will help candidates add value to any role, giving job seekers with this skill set an edge over the competition. If you’re currently in a department like marketing or finance, for instance, studying data science could open new career doors for you. However, even though it leads a lot of people to self-learn data science skills, most of the time it turns out to be a failure in the real world. Here are the reasons why self-taught data scientists see slow progress in their careers.

Making Your Own Curriculum

The concept of self-teaching means making your own curriculum and also finding out what to learn or what to read by yourself. At the beginning of any learning, it’s quite impossible for the student to fathom the vastness of the subject or the right kind of books and resources that are required for it. So, it takes them a long time to learn from their mistake which tends to slow down their progress in learning that particular subject.

Advertisement
A Plethora of Too Many Ideas

It’s very easy to get lost inside the maze of the internet. From youtube to many websites, there is so much information about the self-teaching of data science that newbies won’t be able to tell apart the real from the gibberish. There is no way to filter through the plethora of scattered information across the internet and this can be very misleading to someone with little knowledge of the subject.

Lack of Job-oriented Focus

When someone new is starting to self-learn data science, he or she does not always focus on which particular job to apply for. If anyone starts learning a programming language all of a sudden and on completion of it, he or she will not be fit for all the job roles that come with the name of data science. This scattered approach to learning makes the students slow in the path to getting any particular job in this field.

The Tutorial Trap

Another very common trait among self-taught data scientists is to attend online courses. These courses come with resources like books and also tests. However, the problem is similar to the one with the abundance of information on the internet. Even though taking courses is probably the smartest way to self-teaching a subject like data science, for beginners it might be difficult to recognize the right tutor. To keep the interest in the subject flowing, the tutor can easily teach some cool tricks and it might make the learners think that they have learned a lot and making progress, but most of the time, that is not the case. If anything, it hampers the self-taught learners to have consistency in their progress of career growth in data science.

Advertisement
Lack of practical experience

For the self-taught data scientists, there always seems to be a huge gap between learning and practical knowledge. Once they finish all of their learning, it’s only natural that they will forget most of it unless they start using that knowledge for practical use. So, when faced with data science problems, self-taught data scientists generally mess up the operations. It hinders them from proving themselves properly in the field of work.

Skipping the Fundamentals

Everything interesting and considered ‘cool’ attract more self-taught data science than anything else. A lack of patience in first learning the fundamentals and practice enough to become an expert in the basics often slows down the career of the self-taught data scientist.

Advertisement
More Trending Stories 

The post Why do Self-taught Data Scientists See Slow Progress in Their Career? appeared first on .

Continue Reading

Top posts

Massachusetts Senator Forwards Bill Aimed At Forcing Crypto Miners To Report Greenhouse Gas Emissions Massachusetts Senator Forwards Bill Aimed At Forcing Crypto Miners To Report Greenhouse Gas Emissions
Bitcoin59 mins ago

Massachusetts Senator Forwards Bill Aimed At Forcing Crypto Miners To Report Greenhouse Gas Emissions

On Dec. 8, 2022, three Democratic politicians from Massachusetts, Oregon, and California revealed legislation aimed at combatting “energy-intensive” cryptocurrency mining...

I Talked To 37 People Who Doubled Their Income. 5 Things They All Had In Common I Talked To 37 People Who Doubled Their Income. 5 Things They All Had In Common
Talked2 hours ago

I Talked To 37 People Who Doubled Their Income. 5 Things They All Had In Common

Courtesy of Robert Rodriguez Bringing more total monthly income into the picture is one of the best ways to accelerate...

Assessing what’s next for Yearn Finance (YFI) as we gear up to close Q4 Assessing what’s next for Yearn Finance (YFI) as we gear up to close Q4
Altcoins11 hours ago

Assessing what’s next for Yearn Finance (YFI) as we gear up to close Q4

YFI suffers a bearish divergence that indicates a decline in buying momentum. Sellers take control of the YFI market during...

CryptoSlate Wrapped Daily: Hopes for Celsius withdrawals to reopen, Do Kwon questions SBF involvement in LUNA crash CryptoSlate Wrapped Daily: Hopes for Celsius withdrawals to reopen, Do Kwon questions SBF involvement in LUNA crash
Wrapped13 hours ago

CryptoSlate Wrapped Daily: Hopes for Celsius withdrawals to reopen, Do Kwon questions SBF involvement in LUNA crash

CryptoSlate Wrapped Daily: Hopes for Celsius withdrawals to reopen, Do Kwon questions SBF involvement in LUNA crash Liam ‘Akiba’ Wright...

Uniswap NFT volume declines by a great margin, but here’s the catch Uniswap NFT volume declines by a great margin, but here’s the catch
Altcoins13 hours ago

Uniswap NFT volume declines by a great margin, but here’s the catch

Uniswap witnessed a decline in NFT volume lately. This decline affected other areas of the DEX, including the count of...

Huobi predicts crypto market bottom in early 2023 Huobi predicts crypto market bottom in early 2023
Analysis13 hours ago

Huobi predicts crypto market bottom in early 2023

Huobi predicts crypto market bottom in early 2023 Christian Nwobodo · 15 mins ago · 2 min read According to...

Forex Trading in Portugal Forex Trading in Portugal
Uncategorized13 hours ago

Forex Trading in Portugal

European traders also have massive potential for gains when trading forex. If you’re a trader living in Portugal and thinking...

Is Bitcoin Bottom In? This On-Chain Condition Hasn’t Been Met Yet Is Bitcoin Bottom In? This On-Chain Condition Hasn’t Been Met Yet
Bitcoin13 hours ago

Is Bitcoin Bottom In? This On-Chain Condition Hasn’t Been Met Yet

A Bitcoin on-chain metric still hasn’t formed the same condition as in the previous bottom, suggesting that the current low...

Kevin O’Leary paid $15 million as a FTX spokesperson, but lost it all Kevin O’Leary paid $15 million as a FTX spokesperson, but lost it all
Bankruptcy13 hours ago

Kevin O’Leary paid $15 million as a FTX spokesperson, but lost it all

Kevin O’Leary paid $15 million as a FTX spokesperson, but lost it all Soumen Datta · 2 hours ago ·...

Simply Business Insurance Review Simply Business Insurance Review
Uncategorized14 hours ago

Simply Business Insurance Review

Simply Business Insurance Ratings at a Glance Product Offerings Simply Business Customer Service Simply Business Claims Support Simply Business Pricing...

Best Cheap Seasonal Motorcycle Insurance Best Cheap Seasonal Motorcycle Insurance
Uncategorized14 hours ago

Best Cheap Seasonal Motorcycle Insurance

All riders will agree that there is nothing like hitting the open road on your motorcycle. It is an indescribable...

Hiscox Insurance Review Hiscox Insurance Review
Uncategorized14 hours ago

Hiscox Insurance Review

If you own a small business, you likely want to do all you can to ensure that it is protected...

Binance Labs leads funding round for GoPlus Security to advance web3 security infrastructure Binance Labs leads funding round for GoPlus Security to advance web3 security infrastructure
Exchanges14 hours ago

Binance Labs leads funding round for GoPlus Security to advance web3 security infrastructure

Binance Labs leads funding round for GoPlus Security to advance web3 security infrastructure Josh O’Sullivan · 3 hours ago ·...

MATIC Price Battles Under $1 Despite Bullish Signals MATIC Price Battles Under $1 Despite Bullish Signals
Cryptocurrency news15 hours ago

MATIC Price Battles Under $1 Despite Bullish Signals

The Polygon (MATIC) price momentum registered a positive shift over the last 24 hours. The altcoin gained close to 3%....

Layer 2 networks see jump in dApp migration; the reason might surprise you Layer 2 networks see jump in dApp migration; the reason might surprise you
Altcoins15 hours ago

Layer 2 networks see jump in dApp migration; the reason might surprise you

dApps have increased migration to layer 2 networks. Arbitrum continues to lead as the layer 2 network with the highest...

TRON [TRX] is ready for a further rally; investors can profit from this level TRON [TRX] is ready for a further rally; investors can profit from this level
Altcoins16 hours ago

TRON [TRX] is ready for a further rally; investors can profit from this level

TRX was in a bullish market structure. A break of the current support resistance at the 38.2% Fib level ($0.5345)...

Waves down 12% as Upbit issues investment warning Waves down 12% as Upbit issues investment warning
Stablecoins17 hours ago

Waves down 12% as Upbit issues investment warning

Waves down 12% as Upbit issues investment warning Christian Nwobodo · 5 hours ago · 1 min read Upbit has...

Biggest Movers: LTC, ATOM Extend Declines, Hitting 10-Day Lows  Biggest Movers: LTC, ATOM Extend Declines, Hitting 10-Day Lows 
Analysis17 hours ago

Biggest Movers: LTC, ATOM Extend Declines, Hitting 10-Day Lows 

Litecoin fell for a third consecutive session on Thursday, as the token continued to move away from recent highs. Cryptocurrencies...

Trending

Free Bitcoin MiningEarn from $50 to $75 daily with just your phone