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According to the U.S Bureau of Labor Statistics (BLS), data science is among the 10 fastest-growing jobs of the next decade and the expected growth rate through 2030 is 31%. Yet, data science talent is still scarce. That’s why businesses that lack data science talent may need to rely on data science consulting companies.
In this article, we explain how, when and why to choose a data science consultant.What is data science consulting?
Data science consulting is the activity to effect change by building up the client’s analytics skills, developing competencies, and understanding of the inner workings of their business.
Data science consulting firms provide 4 services to companies. These services are:
Validation of strategy
The strategy part of the consulting explores what’s possible with data and aims to create a plan. This part requires extensive knowledge regarding the use cases. Depending on the client’s industry, the data collection method, regulation, and objectives can be completely different.
For one case, the objective can be optimizing the energy consumption of a plant, which can be achieved through collecting the data through machinery and getting the necessary paperwork from the business owner itself. Whereas, for an FMCG firm, trying to create a data pipeline to maximize the sales, the data collection can be limited by red tape, consumer protection and personal data protection requires considering the legal side of the work.
Collaboration between different departments is the key to success. The nature of data science makes the process more interdisciplinary and interdepartmental.
The strategy usually answers the following questions:
What to do?
What to collect?
How to collect it?
Where to store it?
How to protect it?
How to implement the solution?Validation
The validation step is necessary to validate the identified strategy. While creating the strategy can be completed in hours in urgent cases, implementation can take months. Therefore, it is important to validate the strategy.
Validation is a natural step in finalizing the strategy. However, this may cause a conflict of interest if the validity of the strategy is evaluated by the same people providing the consultation.
In most consulting projects, in the interest of time, the same team builds and validates the strategy. Having another team for validation would require them to start the analysis from almost scratch, creating significant inefficiencies. Separation of strategy and its validation makes it easier to find and spot the problems in the strategy and clarify how the validation step improved the strategy.
Validation includes answering these questions:
What is the insight behind this strategy?
What is a low-cost way to test this strategy without fully implementing its findings?
What do tests tell about the validity of the strategy?Development
Development is the activity of designing and building a modern data product or internal tool. This is more like the IT part of data science consulting. Custom-tailored solutions for specific problems require a heavy emphasis on the development process.Training
Training provided by consultants boosts the data literacy of your teams. Continuous training ensures that your teams are aware of the data science development process built by consultants. This also ensures that internal teams capture the main points and provide a meaningful contribution to the continuous improvement of the entire data science process.
Recommendations for end users:
Ensure the data science consultancy team follows collaboration best practices and a process that is interdisciplinary and interdepartmental.
Choose a data science consultancy that separates strategy and its validation. This makes it easier to find and spot the problems in the strategy and clarify how the validation step improved the strategy.
Check developers’ domain expertise by interviewing and asking them domain specific questions.
Reach out to customer references of consultants and check the success of continuous improvement of data science initiatives started by consultants.How do data science consultants work?
Top management consultants like McKinsey have been putting significant effort into modernizing their data science project management approaches. Their frameworks are similar to the ones we outlined above, but it would be good to look at the areas they emphasize.Source of Value
Everything starts with the problem definition. The problem of most data science projects is finding a new opportunity that will enforce revenue growth and performance improvement. Consultants can also help in this step by identifying key value creation opportunities powered by analytics/data science. The most common use cases are improving customer-facing activities, optimizing internal processes with data-driven insights, and expanding clients’ portfolio of offerings.Data Ecosystem
Consultants look for data sources to use in the project to unlock the value of data sources.
Data sources that data science consultants can use are:Modeling Insights
Data science consultants either build new data models or select from existing models specific to the client’s problem. These models are tested on the client’s data to uncover insights. They can use tools such as AutoML to increase the efficiency in the modeling process.Turning Insights into Actions
With their models’ results, consultants create a feasible action plan that will include both process and technology changes. These steps can also include rolling out models built during the project to empower operational decisions.Adoption of Technology
Data science consultants should know that their clients may not have a data-driven culture and be ready to adapt to new data science tools. Consultants spend time on training clients’ employees, ensuring implementation of the prescribed actions, and enabling an effective change management.Optimization of Organization and Governance
Lastly, consultants help build data governance and IT infrastructure to ensure that organizations can have lasting performance improvement. Performance improvements that do not address governance aspects of change tend to be short-lived.Necessary Skills for Data Science Consultants
Below image from AltexSoft highlights what skills are required to be a data scientist consultant. Required and preferred skills can be categorized as follows:Required skills:
Data management skills
Knowledge of pre-existing ML algorithms and models
Business acumen and collaborationPreferred skills:
Knowledge of frameworks and libraries
TensorFlow for neural networks
Skicit-learn for machine learning
Experience in the industry
Enthusiasm for problem-solvingCases where hiring a data science consulting agency is a better option
Data science projects can be handled via the following approaches:
Companies can choose either option, yet, each approach has pros and cons depending on the business’ industry, objectives, and budget.
There is no suitable off-the-shelf solution for your use case: If companies have specific needs and existing off-the-shelf solutions do not meet those expectations, consulting companies can help build customized products so that businesses eliminate or minimize off-the-shelf solution risks such as costly customization projects.
Budget is not enough to build an in-house team: A data science team includes roles such as Chief Data Officer, data analyst, business analyst, data scientist, data architect, data engineer, etc. Building such a team is an expensive approach considering an average salary of a single data scientist working in-house is $94,000.
Data science projects don’t require unique proprietary data: If your case and data are not unique, then consultants probably worked with similar data before. Their experience can help accelerate your projects faster.
Data set does not contain sensitive information: Companies must be careful before sharing data with third parties due to data privacy regulations. Methods such as synthetic data generation and data masking can help companies make their data ready for sharing.
Your company needs guidance on identifying the business aspects of data science projects: This is why consulting firms are still popular. Most companies are specialized in the market, and their knowledge of strategy and implementation of projects is limited. Consultants help identify business processes where data science projects can be implemented.
For more information on model development approaches, please check our guide on the ideal way to build AI projects.Data Science Consulting Industry
The industry players can be categorized into four types. These are
Historical Tech Companies,
For more on specific industry players, you can check our article on AI consulting landscape.3 Factors to Consider When Choosing a Data Science Consultant
3 criteria can help choose the right data consulting partner:
Duration of service they offer
Here are the questions you should be asking:Do they have enough domain and field experience?
It is important to see that the consultants experienced a project in a similar setting. This shows that the consultant can put meaningful insight and knows the practices in the specific industry. Organizations need to examine consultants’ previous projects to see that they have expertise in the following approaches:
Industry-specificDo they have analytics translators on the team?
A data scientist’s technical capabilities are important for consultants as long as they can turn insights into actionable decisions. Analytics translators work with the data science team and combine their findings with the business domain expertise to create actionable decisions.
Translators should be able to interpret and translate analytics insights into business benefits and guide the analytics work. These consultants should have domain knowledge, technical fluency, project management skills, and an entrepreneurial spirit to achieve this goal.Can they provide a long-term plan?
You need to make sure that the consultant’s plan is viable and can be upgraded regularly. Data science is a field experiencing constant improvement, so it would be important to see its potential. Think about it as a long-term investment, you may need consulting again, and updates so make sure they can provide the greater planning horizon.Salaries of data science consultants
Salaries of data science consultants vary based on experience and location. According to Neuvoo, these are the average data science consultant salaries by country. The top and bottom end of the ranges can help you understand how experience impacts salary:
CountryMedian Salary (per year)Lowest Salary (per year)Highest Salary (per year) United States$122,850$50,000$183,300 United Kingdom£65,000£21,100£90,000 Germany€80,000€27,384€95,000 France€31,992 €20,640€62,740 India₹ 1,287,500₹ 216,000₹ 1,750,000
If you have access to data which you would like to use to build a machine learning model:
If you are looking for a consultant for your data science project, feel free to check our regularly updated list of data science consultants or our list of AI consultants. We can also help you find data science consultants even if you haven’t identified your machine learning problem yet:
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Coronavirus has led to a very different working world than anything we have ever known. However, on the better part, the tech jobs are blooming as gloriously as May arrived, waiting to be picked. As noted by Digital Trends, tech jobs, especiallyBayer
Bayer is a Life Science company with a more than 150-year history and core competencies in the areas of health care and agriculture. With its innovative products, the company is contributing to finding solutions to some of the major challenges of the current time. Bayer is operating at the edge of innovation in healthcare, agriculture, and nutrition. Average Salary: US$113,000 Salary Range: US$74,000 – US$129,000Honeywell
Honeywell is a Fortune 100 company that invents and manufactures technologies to address tough challenges linked to global macrotrends such as safety, security, and energy. With approximately 110,000 employees worldwide, including more than 19,000 engineers and scientists, the company has an unrelenting focus on quality, delivery, value, and technology in everything it makes and does. Average Salary: US$92,046 Salary Range: US$68,000 – US$76,000Apple
Apple Inc. designs, manufactures, and markets personal computers and related personal computing and mobile communication devices along with a variety of related software, services, peripherals, and networking solutions, noted Bloomberg. Apple sells its products worldwide through its online stores, its retail stores, its direct sales force, third-party wholesalers, and resellers. Average Salary: US$100,000 Salary Range: US$140,000 – US$158,000TrueAccord
TrueAccord is transforming the debt collection industry and helping consumers reach financial health. Its mission is to reinvent debt collection. By delivering a great user experience, the company empowers consumers to regain control of their financial future. TrueAccord makes debt collection empathetic and customer-focused. Average Salary: US$130,000 Salary Range: US$87,000 – US$173,000Google
Average Salary: US$62,000 Salary Range: US$53,000 – US$94,000Zoom
Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done. It’s an easy, reliable cloud platform for video, phone, content sharing, and chat runs across mobile devices, desktops, telephones, and room systems. The company’s mission is to develop a people-centric cloud service that transforms the real-time collaboration experience and improves the quality and effectiveness of communications forever. Average Salary: US$111,000 Salary Range: US$56,000 – US$120,000Jobot
Jobot is disrupting the recruiting and staffing space by using the latest AI technology to match jobs to job seekers; hiring experienced recruiters who believe in providing the best possible service to their clients and candidates; imagining a world where recruiters actually care about clients and candidates; and leveraging JAX, our proprietary recruiting platform to expedite and enrich the hiring process. Average Salary: US $77,000 Salary Range: US$60,000 – US$85,000MathWorks
MathWorks is the leading developer of mathematical computing software. Engineers and scientists worldwide rely on its products to accelerate the pace of discovery, innovation, and development. MATLAB by MathWorks is the language of technical computing, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. Average Salary: US$70,000 Salary Range: US$54,000 – US$91,000Snowflake
Snowflake’s mission is to enable every organization to be data-driven. Its cloud-built data platform makes that possible by delivering instant elasticity, secure data sharing, and per-second pricing, across multiple clouds. Snowflake combines the power of data warehousing, the flexibility of big data platforms, and the elasticity of the cloud at a fraction of the cost of traditional solutions. Average Salary: US$130,525 Salary Range: US$116,000 – US$205,000Conch Technologies, Inc
Conch teams work with customers to provide an array of services, which help them to drive their immediate goals and achieve long term vision. The company’s customers range from Fortune 1000 Clients to recent startups, who are providing cutting edge technology products and top-notch services. Conch’s Enterprise Service Delivery model allows the customer to increase ROI on their IT budgets. It is accrued in the form of – minimized execution times, improved quality of products, downward trending failure rates, and improve forecasting. Average Salary: US$79,000 Salary Range: US$43,000 – US$90,000
Data science comes closer to SEO every day.
Data science, and more exactly artificial intelligence, isn’t new, but it has become trendy in our industry over the past few years.
In this article, I will briefly introduce the main concepts of data science through machine learning and also answer the following questions:
When can data science be used in SEO?
Is data science just a buzzword in the industry?
How and why should it be used?A Brief Introduction to Data Science
Data science crosses paths with both big data and artificial intelligence when it comes to analyzing and processing data known as datasets.
Google Trends does a pretty good job of illustrating that data science, as a subject of intent, has been increasing over the years since 2004.
The user intent for “machine learning” has been increasing as well, and is one of the most popular search queries.
This is also one of the two ways for operating artificial intelligence and what this article will focus on.What Is the Concrete Relationship Between Artificial Intelligence & Google?
Back in 2011, Google created Google Brain, a team dedicated to artificial intelligence.
The main objective of Google Brain is to transform Google’s products from the inside and to use artificial intelligence to make them “faster, smarter and more useful.”
We easily understand that the search engine is their most powerful tool and considering its market share (95% of users use Google as their main search engine), it comes as no surprise that artificial intelligence is being used to improve the quality of the search engine.What Is Machine Learning?
Machine learning is one of the two types of learning that powers artificial intelligence.
Machine learning tends to solve a problem through a frame of reference and the output is checked by a human being, as it always comes with a certain percentage of error.
Google explains machine learning as follows:
“A program or system that builds (trains) a predictive model from input data. The system uses the learned model to make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model. Machine learning also refers to the field of study concerned with these programs or systems.”
More simply, machine learning algorithms receive training data.
In the example below, this training data is photos of cats and dogs.
Then, the algorithm trains itself in order to understand and identify the different patterns.
The more the algorithm is trained, the better the accuracy of the results will be.
Then, if you ask the model to classify a new picture, you will obtain the proper answer.
Google Images is certainly the best example to reproduce this explanation.What Is the Concrete Relationship Between Artificial Intelligence & SEO?
Back in 2023 – and to limit this discussion to the main algorithms – RankBrain was rolled out in order to improve the quality of the search results.
As about 15% of queries have never been searched for before, the aim was to automatically understand best the query in order to produce relevant results.
RankBrain was developed by Google Brain.
Then, in 2023, BERT was introduced to better understand search queries.
As SEO professionals, it is important to note that we can not optimize a website for either RankBrain or BERT as they are designed to better understand and answer search queries.
To resume, these algorithms are involved in processes that don’t affect how websites are evaluated or matched to queries. There is no way of optimizing for them.
Still, as Google uses machine learning, it is important to know more about this field and also to be able to use it: it can help run your daily SEO operations.What Is the Value of Machine Learning to SEO?
The following can be seen as valuable areas for applying machine learning to SEO according to my experience:
The above can help to save time on your daily operations and also convince the decision-makers in your organization.
From there, the rest of the article may convince you (as I am convinced) or leave you doubtful.
Either way, the following parts will certainly interest you.Prediction
Prediction algorithms can be helpful to prioritize your roadmap by highlighting keywords.
The above is available thanks to an open-source code written by Mark Edmonson.
The idea is to make the following assumption: if I were ranking first for these keywords, what would be my revenue?
It then gives you your current position and the potential revenue you could get by taking into account an error margin.
It can help convince your higher-ups to focus on some specific keywords but also can appeal to your client (if you’re working as a consultant or in an agency).Generation
Writing content is certainly one of the most time-consuming tasks in SEO.
Either you write the content yourself or you need, at a minimum, to write a brief.
In both cases, it is sometimes hard to find the inspiration to work efficiently.
This is why the automatic generation of content is valuable.
As I already said, machine learning comes with an error margin.
That is why this kind of content automation needs to be seen as producing an initial editorial framework.
I’ve shared some sample source code available here.
Also, getting a first automated draft of editorial content can help you semi-automate your internal linking by allowing you to highlight, manually, your top and secondary anchor tags.Automation
Automation is helpful to label images and eventually video by using an object detection algorithm as seen on TensorFlow.
This algorithm can help label images, so it can optimize alt attributes pretty easily.
Also, the automation process can be used for A/B testing as it is pretty simple to make some basic changes on a page.
In this case, the idea would be to automate A/B testing thanks to the content generation and update it based on the expected performance.
All screenshots taken by author, December 2023
7 lucrative vacancy alerts for aspiring data scientists to kick-start their career in May 2023
Data science is one of the ground-breaking fields for students who have a knack and a keen eye for details in the world of science and technology. Companies are in dire need of aspiring data scientists for proper usage of the continuous flow of real-time data to enhance the business in the competitive world. The future of a company is dependent on data science due to the upsurge of raw data in the tech-savvy era. So, what is the best way to kick-start your career in data science? Yes, lucrative data science internship experiences from reputed companies! Analytics Insight has made you a list of seven reputed companies that have vacancies for data science internships.Data Science Intern at Rolls-Royce
Location: ManyataTech Park, Bengaluru, Karnataka Company: Responsibilities: The candidate should assess and develop future capabilities of technology related to big data analytics. The intern has to contribute to the continuous development or implementation of applications, which demonstrate or illustrate the effect of big data analytics on the customer experience. He/she has to trial fresh approaches, new techniques, technologies, and also assess the impact of the recent trends. The intern has to demonstrate how data, algorithms, programming and data visualisation is to be employed to develop the future generation data service. Criteria:
The candidate should have an educational degree in one of these disciplines— Mathematics, Scientific, Computing and Engineering or relevant experience.
The intern should possess an ability to apply logical, analytical and creative thinking for certain technical issues with a particular bias towards the existing analytical methods, development of new methods as well as Software Engineering.
The candidate should have experiences in Python, Spark, Keras, TensorFlow, PyTorch, NLTK, SpaCy, TF-IDF, Gesim, Scikit-Learn, LDA, word2vec, doc2vec, CNN, RNN also Seq2Seq. It also includes experience in logistic regressions, SVM, Random Forest, Microsoft Azure and Auto ML.
The interns must also have basic knowledge on one of these topics— Gas turbine engines, aircraft engineering, airline operations or aviation maintenance repair.Data Science Intern at Sony Research India
Location: Remote/ Mumbai/ Bengaluru, India Company: Responsibilities: With the head of R&D and data science teams, the candidate has to work on the R&d activities every day with a constant contribution to reporting, coding, testing and relevant models. Other duties include scraping data from the public domain and research on recommendation lists for OTT platforms and much more. Criteria:
The candidate must possess relevant skills in Python coding, Statistics, strong SQL theoretical knowledge and ML basics.
The person should have basic qualifications— Bachelors, Masters or Ph.D. in Computer Science as well as Advanced Machine Learning concepts and AWS or any other cloud service.Data Scientist Intern at Amazon
Location: Bengaluru, Karnataka Company: Responsibilities: The candidate has to use data analysis and statistical methods to develop solutions for improvements in customer experience and guidance in business decision-making. The intern needs to identify predictors and causes of business-related problems then implement different approaches for forecast and predictions. The candidate also is required to identify, develop, manage and execute analysis to attract opportunities as well as business recommendations. The duty also includes collaboration with multiple teams while utilising the best standards of analytical rigor and data integrity. Criteria: The candidate should be from academic/practical background from Computer Science, Engineering, Operations Research, Process Control or relevant field with experience in integration of model-based engineering tools, manipulating, transforming data, model training, deployment at scale, MXNet, TensorFlow, Caffe, PyTorch, Apache Spark, Hadoop, Python, R&SQL and familiarity with Amazon EMR, AWS Lambda, SageMaker, Amazon DynamoDB, Amazon s3, Amazon EC2 Container Service and many more.Data Science Intern at Roanuz
Location: Chennai, Tamil Nadu Company: Responsibilities: The candidate has to apply ML and DL algorithms on real-time data while working on AI-based app development. The candidate is required to work on using innovative and automated approaches for data annotation, labelling, data augmentations with active learning, engage in back-testing the trained patterns and algorithms. Criteria:
The candidate should possess a Bachelor’s Degree in Computer Science, Computer Engineering, Mathematics or any other relevant discipline with deep knowledge in Python, statistical modelling, ML, and data science domain.
The candidate should have good scripting and programming skills to solve critical problems in the company.Data Analytics Intern at Merkle
Location: Pune Company: Responsibilities: The candidate has to use statistical techniques to analyse data and generate business insights reports, client data with EDA to improve process. The intern has to utilise data to create AI and ML models to produce simple solutions for complex business issues, provide support for ad hoc requests from users, Analytics Process monitoring as well as troubleshooting. The candidate has to identify, evaluate and implement external services to support data validation and data cleansing for detailed business requirements. Criteria: The candidate should possess core knowledge on data science or data analytics, GA360, Adobe, Datorama, CDP and DMP with practical experience in building statistical models such as regressions, decision tree, random forests, AI and ML models with the use of R, Python, SAS, SQL queries, cohort analysis.Data Science Intern at XenonStack
Location: Chandigarh Company: Responsibilities: The candidate has to select appropriate features, building and optimising classifiers with ML techniques, data mining with state-of-the-art tools and extend data with third-party sources effectively and efficiently. The data science intern has to enhance the data collection process with proper processing, cleansing and doing ad hoc analysis in a transparent manner. The candidate also has to create an automated anomaly detection system for constant monitoring of the performance. Criteria:Data Analysis Intern at Milaap
Location: Bengaluru Company: is a popular online crowdfunding platform to raise funds for various sectors of the country— healthcare, education, sports, disaster and so on. The company has pioneered the development of a one-to-one helping hand in India while addressing certain challenges to create a massive impact on the lives of citizens. It became popular due to the surge in usage of social media platforms and online payments. Responsibilities: The candidate has to process, clean and verify the integrity of data by understanding data requirements to generate transparent reports for business insights. The intern needs to work constantly with product owners for primary and secondary sources for real-time data. The candidate should identify, analyse and interpret market trends through Facebook, third party APIs or GA. Criteria: The candidate should have hands-on experience with writing SQL queries, Excel sheets, Google Analytics, Facebook Ads, Python, AWS quick sight, AWS redshift and ETL.
Take your journey to the next level by joining these top Data Science Slack communities in 2023
Data science Slack communities act as a community that inspires thousands of people and aims to support student growth and entrepreneurial abilities. Taking part in a community is a fantastic way to learn. Particular attention in this article is given to Slack communities. Slack is a team collaboration tool that facilitates communication and teamwork. To stay up with the newest discussions on data science, we have compiled our top data science Slack communities for you to check out.Let us discuss some of the data science Slack communities to join in the year 2023.
It is everything data, as the name implies. This may come from machine learning, data science, or data analytics. There are several Slack channels, including #ai-memes-for-ai-peeps, #book-of-the-week, #career, #datascience, #events, and more. There are free weekly events you can attend as well as a podcast with up to 12 seasons.
Data Reliability Engineering Community
This Slack channel is more narrowly focused on a particular Data Science issue. Many different data engineers and scientist network and discuss in-depth issues with data dependability and the best methods for solving them. This will be a helpful slack channel if you wish to focus on this area of data science or need further guidance.
A group that lectures about data science, data warehousing, business intelligence-related subjects, and other things. By networking with others in the industry, you may both learn from each other’s and your failures.
AI-ML-Data Science Lovers
The AI-ML-Data Science Lovers slack group is for you if you’re searching for something a little more relaxed and peaceful. There are many people in this group talking informally about artificial intelligence, machine learning, and data science.
It is a great method to stay informed about other people’s viewpoints and broaden your knowledge.
Papers with Code
Papers with Code is a free and open-source website that offers papers, code, datasets, algorithms, and assessment charts related to machine learning. You will have access to excellent materials through the community that will aid your study. You will progress from studying Data Science theory to using and refining your abilities.
Data Science Salon
A team of senior data scientists, machine learning engineers, and other professionals make up the eclectic community that is the Data Science Salon, a unique gathering. They want to connect IT experts so they may network, develop, and learn from one another about potential new approaches.
Open Data Science Community
a group that concentrates on all things Data Science. The top Data Science publications, tutorials that will accelerate your learning, code sharing, and general guidance will all be made available to you. aimed at bringing together data science experts from across the globe.
Data with Danny
Here, you may complete difficult tasks as part of a unique data apprenticeship while learning data analytics, data science, and machine learning. Danny Ma, a well-known data science specialist, started this group. On this channel, you may discuss any data-related subject and, more importantly, you can ask Danny any questions.
Riga DS Club
In today’s highly competitive market, which is anticipated to intensify further, the data science aspirants are left with no solution but to upskill and upgrade themselves as per the industry demands. Prevailing situation odes the mismatch between demand and supply ratio of data scientists and other data professionals in the market, which makes up a great age to grab better and progressive opportunities. The knowledge and application of programming languages that better amplify the data science industry, are must to have. Therefore, here we have compiled the list of top 10 data science programming languages for 2023 that aspirants need to learn to improve their career.Python
Python holds a special place among all other programming languages. It is an object-oriented, open-source, flexible and easy to learn a programming language and has a rich set of libraries and tools designed for data science. Also, Python has a huge community base where developers and data scientists can ask their queries and answer queries of others. Data science has been using Python for a long time and it is expected to continue to be the top choice for data scientists and developers.R
R is a very unique language and has some really interesting features which aren’t present in other languages. These features are very important for data science applications. Being a vector language, R can do many things at once, functions can be added to a single vector without putting it in a loop. As the power of R is being realized, it is finding use in a variety of other places, starting from financial studies to genetics and biology and medicine.SQL
SQL (Structured Query Language) is a domain-specific language used in programming and designed for managing data held in a relational database management system. As the role of a data scientist is to turn raw data into actionable insights, therefore they primarily use SQL for data retrieval. To be an effective data scientist, they must know how to wrangle and extract data from the databases using SQL language.C (C++)
C++ has found itself an irreplaceable spot in any data scientist’s toolkit. On top of all modern data science frameworks is a layer of a low-level programming language known as C++ as it is responsible for actually executing the high-level code fed to the framework. This language is simple and extremely powerful and is one of the fastest languages out there. Being a low-level language, C++ allows data scientists to have a much broader command of their applications.Java
Scala which is also known as Scalable language is an extension of Java language. It runs on Java Virtual Machine (JVM) and is one of the de facto languages when it comes to playing practically with Big Data. Scala serves as an important tool for the data scientists because it supports both anonymous functions as well as higher-order functions.Swift
Swift is a fast programming language and is as close to C as possible. It possesses very simple and readable syntax which is very similar to Python. As compared to Python, Swift is a more efficient, stable and secure programming language. It also works as a good language to build for mobile. For a matter of fact, it is the official language for developing iOS applications for the iPhone. The language is supported by Google, Apple, and FastAI.Julia
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