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.
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
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.
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.
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
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.
Pros and Cons of Julia
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.
4.It has support for Machine Learning algorithms.
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.
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.
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
5.Julia docs – https://docs.julialang.org/en/v1/
Vedang Dalal, Lead Analyst, Merkle
The post Julia on the Upswing: Why Data Scientists are Choosing Julia appeared first on Analytics Insight.
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.
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.
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.
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.
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.
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.
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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.
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.
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.
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