data scientist

The field of artificial intelligence is booming with constant breakthroughs leading to ever-more sophisticated applications. This rapid growth translates directly to job creation. Thus, AI jobs are a promising career choice in today’s world.

As AI integrates into everything from healthcare to finance, new professions are emerging, demanding specialists to develop, manage, and maintain these intelligent systems. The future of AI is bright, and brimming with exciting job opportunities for those ready to embrace this transformative technology.

In this blog, we will explore the top 10 AI jobs and careers that are also the highest-paying opportunities for individuals in 2024.

Top 10 highest-paying AI jobs in 2024

Our list will serve as your one-stop guide to the 10 best AI jobs you can seek in 2024.

Let’s explore the leading roles with hefty paychecks within the exciting world of AI.

Machine learning (ML) engineer

Potential pay range – US$82,000 to 160,000/yr

Machine learning engineers are the bridge between data science and engineering. They are responsible for building intelligent machines that transform our world. Integrating the knowledge of data science with engineering skills, they can design, build, and deploy machine learning (ML) models.

Hence, their skillset is crucial to transform raw into algorithms that can make predictions, recognize patterns, and automate complex tasks. With growing reliance on AI-powered solutions and digital transformation with generative AI, it is a highly valued skill with its demand only expected to grow. They consistently rank among the highest-paid AI professionals.

AI product manager

Potential pay range – US$125,000 to 181,000/yr

They are the channel of communication between technical personnel and the upfront business stakeholders. They play a critical role in translating cutting-edge AI technology into real-world solutions. Similarly, they also transform a user’s needs into product roadmaps, ensuring AI features are effective, and aligned with the company’s goals.

The versatility of this role demands a background of technical knowledge with a flare for business understanding. The modern-day businesses thriving in the digital world marked by constantly evolving AI technology rely heavily on AI product managers, making it a lucrative role to ensure business growth and success.

Natural language processing (NLP) engineer

Potential pay range – US$164,000 to 267,000/yr

As the name suggests, these professionals specialize in building systems for processing human language, like large language models (LLMs). With tasks like translation, sentiment analysis, and content generation, NLP engineers enable ML models to understand and process human language.

With the rise of voice-activated technology and the increasing need for natural language interactions, it is a highly sought-after skillset in 2024. Chatbots and virtual assistants are some of the common applications developed by NLP engineers for modern businesses.

Big data engineer

Potential pay range – US$206,000 to 296,000/yr

They operate at the backend to build and maintain complex systems that store and process the vast amounts of data that fuel AI applications. They design and implement data pipelines, ensuring data security and integrity, and developing tools to analyze massive datasets.

This is an important role for rapidly developing AI models as robust big data infrastructures are crucial for their effective learning and functionality. With the growing amount of data for businesses, the demand for big data engineers is only bound to grow in 2024.

Data scientist

Potential pay range – US$118,000 to 206,000/yr

Their primary goal is to draw valuable insights from data. Hence, they collect, clean, and organize data to prepare it for analysis. Then they proceed to apply statistical methods and machine learning algorithms to uncover hidden patterns and trends. The final step is to use these analytic findings to tell a concise story of their findings to the audience.

Hence, the final goal becomes the extraction of meaning from data. Data scientists are the masterminds behind the algorithms that power everything from recommendation engines to fraud detection. They enable businesses to leverage AI to make informed decisions. With the growing AI trend, it is one of the sought-after AI jobs.

Here’s a guide to help you ace your data science interview as you explore this promising career choice in today’s market.

Computer vision engineer

Potential pay range – US$112,000 to 210,000/yr

These engineers specialize in working with and interpreting visual information. They focus on developing algorithms to analyze images and videos, enabling machines to perform tasks like object recognition, facial detection, and scene understanding. Some common applications of it include driving cars, and medical image analysis.

With AI expanding into new horizons and avenues, the role of computer vision engineers is one new position created out of the changing demands of the field. The demand for this role is only expected to grow, especially with the increasing use and engagement of visual data in our lives. Computer vision engineers play a crucial role in interpreting this huge chunk of visual data.

AI research scientist

Potential pay range – US$69,000 to 206,000/yr

The role revolves around developing new algorithms and refining existing ones to make AI systems more efficient, accurate, and capable. It requires both technical expertise and creativity to navigate through areas of machine learning, NLP, and other AI fields.

Since an AI research scientist lays the groundwork for developing next-generation AI applications, the role is not only important for the present times but will remain central to the growth of AI. It’s a challenging yet rewarding career path for those passionate about pushing the frontiers of AI and shaping the future of technology.

Curious about how AI is reshaping the world? Tune in to our Future of Data and AI Podcast now!

Business development manager (BDM)

Potential pay range – US$36,000 to 149,000/yr

They identify and cultivate new business opportunities for AI technologies by understanding the technical capabilities of AI and the specific needs of potential clients across various industries. They act as strategic storytellers who build narratives that showcase how AI can solve real-world problems, ensuring a positive return on investment.

Among the different AI jobs, they play a crucial role in the growth of AI. Their job description is primarily focused on getting businesses to see the potential of AI and invest in its growth, benefiting themselves and society as a whole. Keeping AI growth in view, it is a lucrative career path at the forefront of technological innovation.

Software engineer

Potential pay range – US$66,000 to 168,000/yr

Software engineers have been around the job market for a long time, designing, developing, testing, and maintaining software applications. However, with AI’s growth spurt in modern-day businesses, their role has just gotten more complex and important in the market.

Their ability to bridge the gap between theory and application is crucial for bringing AI products to life. In 2024, this expertise is well-compensated, with software engineers specializing in AI to create systems that are scalable, reliable, and user-friendly. As the demand for AI solutions continues to grow, so too will the need for skilled software engineers to build and maintain them.

Prompt engineer

Potential pay range – US$32,000 to 95,000/yr

They belong under the banner of AI jobs that took shape with the growth and development of AI. Acting as the bridge between humans and large language models (LLMs), prompt engineers bring a unique blend of creativity and technical understanding to create clear instructions for the AI-powered ML models.

As LLMs are becoming more ingrained in various industries, prompt engineering has become a rapidly evolving AI job and its demand is expected to rise significantly in 2024. It’s a fascinating career path at the forefront of human-AI collaboration.

The potential and future of AI jobs

The world of AI is brimming with exciting career opportunities. From the strategic vision of AI product managers to the groundbreaking research of AI scientists, each role plays a vital part in shaping the future of this transformative technology. Some key factors that are expected to mark the future of AI jobs include:

• a rapid increase in demand
• growing need for specialization for deeper expertise to tackle new challenges
• human-AI collaboration to unleash the full potential
• increasing focus on upskilling and reskilling to stay relevant and competitive

If you’re looking for a high-paying and intellectually stimulating career path, the AI field offers a wealth of options. This blog has just scratched the surface – consider this your launchpad for further exploration. With the right skills and dedication, you can be a part of the revolution and help unlock the immense potential of AI.

Kaggle is a website where people who are interested in data science and machine learning can compete with each other, learn, and share their work. It’s kind of like a big playground for data nerds! Here are some of the main things you can do on Kaggle:

1. Join competitions: Companies and organizations post challenges on Kaggle, and you can use your data skills to try to solve them. The winners often get prizes or recognition, so it’s a great way to test your skills and see how you stack up against other data scientists.
2. Learn new skills: Kaggle has a lot of free courses and tutorials that can teach you about data science, machine learning, and other related topics. It’s a great way to learn new things and stay up-to-date on the latest trends.
3. Find and use datasets: Kaggle has a huge collection of public datasets that you can use for your own projects. This is a great way to get your hands on real-world data and practice your data analysis skills.
4. Connect with other data scientists: Kaggle has a large community of data scientists from all over the world. You can connect with other members, ask questions, and share your work. This is a great way to learn from others and build your network.

Growing community of Kaggle

Kaggle is a platform for data scientists to share their work, compete in challenges, and learn from each other. In recent years, there has been a growing trend of data scientists joining Kaggle. This is due to a number of factors, including the following: 

The increasing availability of data

The amount of data available to businesses and individuals is growing exponentially. This data can be used to improve decision-making, develop new products and services, and gain a competitive advantage. Data scientists are needed to help businesses make sense of this data and use it to their advantage. 

Learn more about Kaggle competitions

Growing demand for data-driven solutions

Businesses are increasingly looking for data-driven solutions to their problems. This is because data can provide insights that would otherwise be unavailable. Data scientists are needed to help businesses develop and implement data-driven solutions. 

The growing popularity of Kaggle. Kaggle has become a popular platform for data scientists to share their work, compete in challenges, and learn from each other. This has made Kaggle a valuable resource for data scientists and has helped to attract more data scientists to the platform. 

Benefits of using Kaggle for data scientists

There are a number of benefits to data scientists joining Kaggle. These benefits include the following:   

1. Opportunity to share their work

Kaggle provides a platform for data scientists to share their work with other data scientists and with the wider community. This can help data scientists get feedback on their work, build a reputation, and find new opportunities. 

2. Opportunity to compete in challenges

Kaggle hosts a number of challenges that data scientists can participate in. These challenges can help data scientists improve their skills, learn new techniques, and win prizes. 

3. Opportunity to learn from others

Kaggle is a great place to learn from other data scientists. There are a number of resources available on Kaggle, such as forums, discussions, and blogs. These resources can help data scientists learn new techniques, stay up-to-date on the latest trends, and network with other data scientists. 

If you are a data scientist, I encourage you to join Kaggle. Kaggle is a valuable resource for data scientists, and it can help you improve your skills, to learn new techniques, and build your career. 

 
Why data scientists must use Kaggle

In addition to the benefits listed above, there are a few other reasons why data scientists might join Kaggle. These reasons include:

1. To gain exposure to new data sets

Kaggle hosts a wide variety of data sets, many of which are not available elsewhere. This can be a great way for data scientists to gain exposure to new data sets and learn new ways of working with data. 

2. To collaborate with other data scientists

Kaggle is a great place to collaborate with other data scientists. This can be a great way to learn from others, to share ideas, and to work on challenging problems. 

3. To stay up-to-date on the latest trends

Kaggle is a great place to stay up-to-date on the latest trends in data science. This can be helpful for data scientists who want to stay ahead of the curve and who want to be able to offer their clients the latest and greatest services. 

If you are a data scientist, I encourage you to consider joining Kaggle. Kaggle is a great place to learn, to collaborate, and to grow your career. 

Navigating the realm of data science careers is no longer a tedious task. In the current landscape, data science has emerged as the lifeblood of organizations seeking to gain a competitive edge. As the volume and complexity of data continue to surge, the demand for skilled professionals who can derive meaningful insights from this wealth of information has skyrocketed.

Enter the realm of data science careers—a domain that harnesses the power of advanced analytics, cutting-edge technologies, and domain expertise to unravel the untapped potential hidden within data.

Importance of data science in today’s world 

Data science is being used to solve complex problems, improve decision-making, and drive innovation in various fields. It has transformed the way organizations operate and compete, allowing them to make data-driven decisions that improve efficiency, productivity, and profitability. Moreover, the insights and knowledge extracted from data science are used to solve some of the world’s most pressing problems, including healthcare, climate change, and global inequality. 

Top 10 Data Science careers: 

Below, we provide a list of the top data science careers along with their corresponding salary ranges:

1. Data Scientist

Data scientists are responsible for designing and implementing data models, analyzing and interpreting data, and communicating insights to stakeholders. They require strong programming skills, knowledge of statistical analysis, and expertise in machine learning. 

Salary Trends – The average salary for data scientists ranges from $100,000 to $150,000 per year, with senior-level positions earning even higher salaries.

Read the most common Data Science interview questions and succeed as a data scientist today

2. Data Analyst

Data analysts are responsible for collecting, analyzing, and interpreting large sets of data to identify patterns and trends. They require strong analytical skills, knowledge of statistical analysis, and expertise in data visualization. 

Salary Trends – Data analysts can expect an average salary range of $60,000 to $90,000 per year, depending on experience and industry.

3. Machine Learning Engineer

Machine learning engineers are responsible for designing and building machine learning systems. They require strong programming skills, expertise in machine learning algorithms, and knowledge of data processing. 

Salary Trends – Salaries for machine learning engineers typically range from $100,000 to $150,000 per year, with highly experienced professionals earning salaries exceeding $200,000.

4. Business Intelligence Analyst

Business intelligence analysts are responsible for gathering and analyzing data to drive strategic decision-making. They require strong analytical skills, knowledge of data modeling, and expertise in business intelligence tools. 

Salary Trends – The average salary for business intelligence analysts falls within the range of $70,000 to $100,000 per year.

5. Data Engineer

Data engineers are responsible for building, maintaining, and optimizing data infrastructures. They require strong programming skills, expertise in data processing, and knowledge of database management. 

Salary Trends – Data engineers can earn salaries ranging from $90,000 to $130,000 per year, depending on their experience and the location of the job.

6. Data Architect

Data architects are responsible for designing and implementing data architectures that support business objectives. They require strong database management skills, expertise in data modeling, and knowledge of database design. 

Salary Trends – The average salary for data architects is between $100,000 and $150,000 per year, although experienced professionals can earn higher salaries.

7. Database Administrator

Database administrators are responsible for managing and maintaining databases, ensuring their security and integrity. They require strong database management skills, expertise in data modeling, and knowledge of database design. 

Salary Trends – Salaries for database administrators typically range from $80,000 to $120,000 per year, with variations based on experience and location.

8. Statistician

Statisticians are responsible for designing and conducting experiments to collect data, analyzing and interpreting data, and communicating insights to stakeholders. They require strong statistical skills, knowledge of statistical analysis, and expertise in data visualization. 

Salary Trends – Statisticians can earn salaries ranging from $70,000 to $120,000 per year, depending on their experience and the industry they work in.

Analytics managers are responsible for leading data science teams, setting objectives and priorities, and communicating insights to stakeholders. They require strong leadership skills, knowledge of data modeling, and expertise in data visualization. 

Salary Trends –  Salaries for analytics managers vary significantly based on the size and location of the company, but the average range is typically between $100,000 and $150,000 per year, with some senior-level positions earning higher salaries.

Essential skills for success in the data science workforce

Data science careers demand a unique combination of technical acumen, analytical prowess, and domain expertise. To embark on a successful career in data science, aspiring professionals must cultivate a robust skillset and acquire the necessary qualifications to navigate the intricacies of this rapidly evolving domain. Here, we outline the essential skills and qualifications that pave way for data science careers:

Proficiency in Programming Languages – Mastery of programming languages such as Python, R, and SQL forms the foundation of a data scientist’s toolkit.

Statistical analysis and mathematics – Strong analytical skills, coupled with a solid understanding of statistical concepts and mathematics, are essential for extracting insights from complex datasets.

Machine learning and data mining – A deep understanding of machine learning algorithms and data mining techniques equips professionals to develop predictive models, identify patterns, and derive actionable insights from diverse datasets.

Data Wrangling and manipulation –  Skills in data extraction, transformation, and loading (ETL), as well as data preprocessing techniques, empower data scientists to handle missing values, handle outliers, and harmonize disparate data sources.

Domain knowledge – Understanding the nuances and context of the industry allows professionals to ask relevant questions, identify meaningful variables, and generate actionable insights that drive business outcomes.

Data visualization and communication – Proficiency in data visualization tools and techniques, coupled with strong storytelling capabilities, enables professionals to convey findings in a compelling and easily understandable manner to both technical and non-technical stakeholders.

Sneak-peek into the future – Future trends and more 

In conclusion, the field of data science is constantly evolving and presents numerous opportunities for those interested in pursuing a career in this field. With the right skills and expertise, data scientists can unlock the power of data and drive meaningful insights that can lead to transformative innovations. As the demand for data science careers continues to grow, staying up-to-date with the latest trends and technologies will be essential for success in this field. With a passion for learning and a commitment to excellence, anyone can thrive in the dynamic and exciting world of data science.  

As data science evolves and grows, the demand for skilled data scientists is also rising. A data scientist’s role is to extract insights and knowledge from data and to use this information to inform decisions and drive business growth. To be successful in this field, certain skills are essential for any data scientist to possess.

By developing and honing these skills, data scientists will be better equipped to make an impact in any organization and stand out in a competitive job market. While a formal education is a good starting point, there are certain skills essential for any data scientist to possess to be successful in this field. These skills include non-technical skills and technical skills.  

Technical skills 

Data science is a rapidly growing field, and as such, the skills required for a data scientist are constantly evolving. However, certain technical skills are considered essential for a data scientist to possess. These skills are often listed prominently in job descriptions and are highly sought after by employers.

These skills include programming languages such as Python and R, statistics and probability, machine learning, data visualization, and data modeling. Many of these skills can be developed through formal education and business training programs, and organizations are placing an increasing emphasis on them as they continue to expand their analytics and data teams. 

1. Prepare data for effective analysis 

One important data scientist skill is preparing data for effective analysis. This includes sourcing, gathering, arranging, processing, and modeling data, as well as being able to analyze large volumes of structured or unstructured data.

The goal of data preparation is to present data in the best forms for decision-making and problem-solving. This skill is crucial for any data scientist as it enables them to take raw data and make it usable for analysis and insights discovery. Data preparation is an essential step in the data science workflow, and data scientists should be familiar with various data preparation tools and best practices. 

2. Data visualization 

Data visualization is a powerful tool for data scientists to effectively communicate their findings and insights to both technical and non-technical audiences.

Having a strong understanding of the benefits and challenges of using data visualization, as well as basic knowledge of market solutions, allows data scientists to create clear and informative visualizations that effectively communicate their insights.

This skill includes an understanding of best practices and techniques for creating data visualizations, and the ability to share results through self-service dashboards or applications.

Self-service analytics platforms allow data scientists to surface the results of their data science processes and explore the data in a way that is easily understandable to non-technical stakeholders, which is crucial for driving data-driven decisions and actions.  

3. Programming 

Data scientists need to have a solid foundation in programming languages such as Python, R, and SQL. These languages are used for data cleaning, manipulation, and analysis, and for building and deploying machine learning models.

Python is widely used in the data science community, with libraries such as Pandas and NumPy for data manipulation, and Scikit-learn for machine learning. R is also popular among statisticians and data analysts, with libraries for data manipulation and machine learning.

SQL is a must-have for data scientists as it is a database language and allows them to extract data from databases and manipulate it easily. 

4. Ability to apply math and statistics appropriately 

Exploratory data analysis is a crucial step in the data science process, as it allows data scientists to identify important patterns and relationships in the data, and to gain insights that inform decisions and drive business growth.

To perform exploratory data analysis effectively, data scientists must have a strong understanding of math and statistics. Understanding the assumptions and algorithms underlying different analytic techniques and tools is also crucial for data scientists.

Without this understanding, data scientists risk misinterpreting the results of their analysis or applying techniques incorrectly. It is important to note that this skill is not only important for students and aspiring data scientists but also for experienced data scientists. 

5. Machine learning and artificial intelligence (AI) 

Machine learning and artificial intelligence (AI) are rapidly advancing technologies that are becoming increasingly important in data science. However, it is important to note that these technologies will not replace the role of data scientists in most organizations.

Instead, they will enhance the value that data scientists deliver by providing new and powerful tools to work better and faster. One of the key challenges in using AI and machine learning is knowing if you have the right data. Data scientists must be able to evaluate the quality of the data, identify potential biases and errors, and determine. 

Non-Technical Skills 

In addition to technical skills, soft skills are also essential for data scientists to possess to succeed in the field. These skills include critical thinking, effective communication, proactive problem-solving, and intellectual curiosity.

These skills may not require as much technical training or formal certification, but they are foundational to the rigorous application of data science to business problems. They help data scientists to analyze data objectively, communicate insights effectively, solve problems proactively, and stay curious and driven to find answers.

Even the most technically skilled data scientist needs to have these soft skills to make an impact in any organization and stand out in a competitive job market. 

6. Critical thinking

The ability to objectively analyze questions, hypotheses, and results, understand which resources are necessary to solve a problem, and consider different perspectives on a problem. 

7. Effective communication

The ability to explain data-driven insights in a way that is relevant to the business and highlights the value of acting. 

8. Proactive problem solving

The ability to identify opportunities, approach problems by identifying existing assumptions and resources, and use the most effective methods to find solutions. 

9. Intellectual curiosity

The drive to find answers, dive deeper than surface results and initial assumptions, think creatively, and constantly ask “why” to gain a deeper understanding of the data. 

10. Teamwork

The ability to work effectively with others, including cross-functional teams, to achieve common goals. This includes strong collaboration, communication, and negotiation skills. 

Bottom line 

All in all, data science is a growing field and data scientists play a crucial role in extracting insights from data. Technical skills like programming, statistics, and data visualization are essential, as are soft skills like critical thinking and effective communication. Developing these skills can help data scientists make a significant impact in any organization and stand out in a competitive job market.

Landing a job that you love can be tough, especially if you’ve graduated amidst a pandemic or during the global recession that followed it. Even for more experienced people, the landscape of the job market is at an unprecedented speed and the future looks uncertain. This blog outlines some basic tips that will position you ahead of the curve and increase your chances of getting hired.   

With scores of resumes in front of them, recruiters only spend a few seconds reviewing each resume and making a decision, so if you’ve landed an interview, you’ve probably done something right. However, the actual recruitment process is much longer and usually very rigorous to ensure that the candidate is a good fit for the company. Let’s look at some of the things you can do to improve the likelihood of getting an offer.  

1. Know the recruitment process 

Every company has a standard process for vetting candidates. This could vary for each company, but it is usually a mix of a few or all of the following components. It is important to note that all these components have a specific purpose and aim to understand different sides of you. 

• Screening – This initial step is a short interview, usually with the recruiter, to evaluate your basic skills and to validate the qualifications mentioned in your resume. The most common questions are regarding your educational and work background, availability, salary expectations, and reason for applying for the job.  
• Case studies – Some companies employ case studies to evaluate core knowledge and skills related to the job. They help employers identify how candidates manage uncertain situations, their logical and analytical reasoning, problem-solving skills, and creativity among other things.  

• Intelligence testing – IQ tests commonly measure cognitive skills. A well-rounded candidate is expected to display not only technical but critical thinking capacities and IQ tests are standardized ways of measuring that. 
• Panel interviews – To get a holistic understanding of a candidate’s capabilities, the hiring manager usually interviews them along with a few other teammates to not only assess their technical expertise but also if they would fit in with the company culture.  

Knowing what the recruitment process looks like at a company could help you in preparing for it better and reduce your anxiety about what will come next. Therefore, when an HR representative reaches out to you, always ask about the next steps and the average time to run the complete recruitment cycle.  

2. Do your research  

Before walking into the interview, learn all about what the company does, its background, and how it has grown in the past. A simple LinkedIn search could also lead you to posts by employees where you can learn about their experiences. More importantly, read the job description carefully, so you know what the role requires and how your experience can contribute to your success there.  

It also helps to know a bit about the interviewers, including their career history and role in the company. This will give you a good idea of the type of questions a particular interviewer will ask, i.e., technical or related to soft skills.    

3. Know what you are looking for 

One question that interviewers use to gauge how passionate you are about the position in hand, is, “Why do you want to work with us?”. While there may be many variations of this question, your answer needs to be personalized and authentic. Generalized answers like the prestige of the company and gaining work experience won’t cut it.

With all this information, you will be able to formulate a personalized answer to why you chose to apply to the company which may include the culture, growth opportunities, and specific industry leaders you might want to work with among others.  

Not only this but, it will also mean that you will be applying for the right reasons. Being clear about why you are working at a company will keep you focused and motivated. In general, keep a list of things you are looking for in a company and target those that you feel would provide those to you. 

Read about: Data Analyst interview questions

4. Prepare for different interview questions 

Prepping ahead of time plays a vital role in making you feel confident and ready for an interview. You may want to role-play with a friend to practice how you would respond to various prompts that might be asked of a data scientist. While it is impossible to know the exact questions that would be asked, you can dig deeper, prepare answers for frequent questions and not get tongue-tied in the interview. Different areas are assessed using the following diverse types of questions: 

• Knowledge-based – These questions tend to be more direct and help to see if the candidate would be able to perform well at the job they are being hired for.  
• Introspective – Companies want to hire self-aware people who not only know their strengths but also their weaknesses so they can work on them. Presenting a perfect self will not help here – it is important to reflect and be honest. 
• Hypothetical – Using hypothetical scenarios, interviewers can assess your potential to make quick decisions and give them an insight into your process of getting there.  
• Behavioral – Questions about how you handled certain situations in the past are behavioral questions. This gives the interviewers a sense of how you approach problems, conflicts, and relationships at work, which eventually helps them understand if you would fit into their team. 

5. Ask questions  

At the end of each interview, candidates are asked if they have any questions from the interviewers. This is a great opportunity to get more context on the role, the team, and the company and make an informed decision. Prepare a set of questions beforehand that could include areas like working hours, policies, team structure, or specifics about the function and role. 

As a data scientist, it is important to know about the projects you will be involved in working on. Learn about the expectations during the question-answer session in your interview.

Remember that this could be a future place of work for you, and you are evaluating it as much as the interviewer is assessing you. Moreover, it will help build your interest and motivation if it is the right place for you.  

6. Plan ahead for the day of the interview  

The way you act during the whole recruitment process, especially on the day of the interview, factors into your evaluation as a candidate. So, always stay professional, check your emails for errors before sending, and be courteous. For some things, you will need to plan:  

• Your outfit for the interview to make a good impression, 
• Being on time: if it is an on-site interview, make arrangements for transport,  
• Check your internet connection and laptop battery for remote interviews,  
• Choose a peaceful spot for virtual interviews, 

• Have a wholesome meal and stay hydrated to avoid lethargy.  

7. Highlight your uniqueness  

It is easy for someone to check all the boxes for the requirements of a role, but out of many that do, only one has to be hired. So, what is it that helps you cut? Your genuineness and uniqueness. Every person has a different journey and distinct experiences meaning everyone has something different to offer. Being able to reflect on these to understand your unique superpowers and highlight them will help the recruiters see your real potential.  

To do this, answer questions with examples of what you have done in the past and how you faced challenges. For example, a fresh graduate may not have past work experience, but you may show how good you are at teamwork by talking about a time when you delivered a team project in college.

More importantly, don’t just say that you are willing to learn and come with a growth mindset, share tangible examples that showcase your curiosity and effort. Another great way to make an impression is to share something that is not explicitly mentioned in your resume. For instance, when the interviewer asks you to introduce yourself, talk about a multi-faceted you to bring your human side to light.    

Conclusion  

Overall, recruitment is all about finding the right fit on both sides. Before convincing a company to hire you, you must have solid grounds in your mind to believe that you belong there. If so, following the above-mentioned suggestions will assist you in getting there.  

This blog will learn about “Data Science career growth in 2022”. It is no longer a secret that today’s economy is entirely dependent on analytics and data-driven solutions/decisions. 

Businesses, enterprises, and governments have spent the last few years collecting and analyzing massive volumes of data. If you are interested in the field of Data Science enroll in some Data Science courses offered by reputed Institutions which will be an added advantage during your job hunt. 

Data scientists are currently playing a crucial part in the success or failure of any organization, one can even consider choosing a proper Data science certification program which will help learn practically as well as theoretically. Therefore, it is not a stretch to state that “there is a data scientist behind every huge successful company.”

Overview of Data Science career

Data science is a fascinating, interesting, intriguing, forward-thinking, and lucrative profession. Importantly, unlike other traditional careers, you do not need an established degree or specialized educational background to begin your journey in Data Science.

All you need are the proper abilities, some connected experience, and a curious mind. Considering the need for data scientists in the current market trends indicate that data science course fees are growing.

In this blog, I’ll go over the ins and outs of the data scientist job path, as well as the abilities necessary for data Science. In addition, I’ll guide you on how to choose which data science career is best for you.

Alright!! Let’s dive into the topics.

Table of Contents:

• What is Data Science?

• What does a Data Scientist do?

• Is Data Science right for you?

• Why choose a career in Data Science?

• Job statistics in Data Science career

• Are you ready to become a Data Scientist?

What is Data Science?

Data science is the study of massive amounts of data using current tools and methodologies to discover previously unknown patterns, extract valuable information, and make business choices. 

Data for analysis can come from a wide range of sources and be provided in a variety of ways.

Now that you know what data science is, let’s look at what a Data Scientist will do in 2022.

What does a Data Scientist do?

Data science is a highly interdisciplinary field that works with a broad variety of data and, unlike other analytical fields, focuses on the overall perspective.

In business, the purpose of data science is to give an insight into customers and campaigns, as well as to aid organizations in building effective plans to engage their audiences and sell their products. 

Big data, or enormous amounts of information gathered through different methods such as data mining, necessitates the use of creative thinking on the part of data scientists. So, what exactly does a data scientist do?

Data scientists use forecasting models to evaluate data and information to produce key insights that help enterprises expand their businesses in the right direction. One of the key responsibilities is to analyze large data sets of quantitative and qualitative data. 

This personnel is in charge of developing statistical learning models for data analysis and must be knowledgeable with statistical tools. They must also be knowledgeable enough to create complex prediction models.

Is Data Science right for you?

In my opinion, it is crucial to have an answer to this issue before embarking on your path in data science. Unfortunately, many blogs on the internet indicate that the area of data science is full of demand, great incomes, and respect. 

Nevertheless, the fact is that your journey to data science is not at all easy; it takes continual learning and unlearning of complicated subjects and concepts from different professions, and you must be technically knowledgeable throughout your career.

Learn more about Data Science Roadmap 

In this section, I’ll provide you with some suggestions that will take you to the answer to this question. Fundamentally, anyone can acquire and practice any data science skill if they are truly committed to it.

Simply said, if you want to learn data science, you can do so.

Why choose a career in Data Science?

Data science has been termed the “sexiest job of the twenty-first century.” I’m sure this is a significant role in your decision to pursue a career in data science. Nowadays, any company, large or little, is looking for employees who can interpret and dissect data.

Choosing a profession in data science involves respecting the numerous disciplines on which data science as a subject has been founded, such as statistics, math, and technology, among others. The variety of abilities required to become a data scientist might be considered an advantage.

Now, let me direct your attention to a few key reasons why you should pursue a career in data science;

• High prestige
• Be part of future
  Excellent pay
• Constant challenging work or NO boring work
  Exceptional growth & demand in the market
• Endless career opportunities

Data Science has shown the ability to transform companies and our society. It has become a lucrative job due to a limited supply of trained workers in Data Science and high demand.

Job statistics in Data Science career

If you’re here, I’m presuming you’ve picked or are thinking about choosing a career path. Let me direct your attention to a few more key criteria which might assist you in making your final decision.

• 650% Job growth since 2015 (Via: Linkedin)
• By 2026, 11.5 million additional jobs are expected to be created (source: U.S. Bureau of Labor Statistics)
• A data scientist earns an average annual income of $120,931. (source: Glassdoor)
• In 2020, there are expected to be 2.7 million available positions in data analysis, data science, and related fields (source: IBM).
• By 2020, there will be a 39% increase in employer demand for both data scientists and data engineers (source IBM).
• 59% of employment will be in finance, information technology (IT), insurance, and professional services. This is divided as follows: 
• 19% in banking and insurance, 18% in professional services, and 17% in information technology.
• Bachelor’s degree holders will be able to apply for 61% of data scientist and advanced analytic roles, while 39% will require a master’s or Ph.D.
• Positions in data science and data analysis are available for 5 days longer than the average for all jobs, indicating that there is less competition in these professional sectors and recruiters must work harder to locate competent individuals.
• A possible annual salary of $8,736 more than any other bachelor’s degree position (source: IBM).

 

Pro-Tip: Build up your Data Science career as a licensed Data Scientist

 

The data presented above indicates the development and need for data science specialists across various business areas, geographical regions, and even experience levels. As more businesses implement data-driven solutions, the need for data scientists will continue to rise.

So, relax, you’re on the correct track!

Are you ready to become a Data Scientist?

Data science is the most in-demand career this decade and will continue to be so in the future. With increased awareness of the industry, competition for positions among professionals is at an all-time high. If you follow this approach and do honest self-evaluation, I am confident you will make the best decision for you.

Enroll in Data Science Bootcamp today to begin your Data Science career

Remember that selecting the proper career path is only the beginning of your journey.

The position title is often used pretty vaguely, and that’s because there are so many ways to be one. For every specialty and job responsibility, there are corresponding personality types and skills to match. We created this quiz to tell you what kind of data scientist you are (or maybe even what kind you should be), based on some characters from pop culture!

Quiz for what kind of data scientist you are

If you enjoyed this, some of the questions in this quiz were based off actual interview questions from companies like Amazon and Google. Check out our master list of 101 Data Science Interview Questions for more. Or if you even want to expand your data skills, join our Data Science Bootcamp program today.

Do you agree with your result? Comment below!

As of 2020, the average salary that a data scientist makes in the US is over $113,000. With that stated, it can be affirmed that data scientists are in high demand. You can think of data science as a way to earn money but then you will never have the actual motivation to learn it. Instead, you should identify a problem; be it marketing-related or a research problem, and then start learning data science & its tools accordingly, because you cannot excel at every tool or a data science skill set. 

Stay updated with Artificial Intelligence and learn more about Large Language Models:

First & foremost, you need to motivate yourself to love the data, with no drive you will probably leave your learning journey at some point. Furthermore, you need to work on real projects. Just acquiring the fundamental knowledge or skills won’t make you an expert data scientist, likewise, to increase your expertise, you need to increase the level of difficulty every time you undertake a data science project. While being at work or by joining a top-rated Data Science Bootcamp, learn from your instructors & peers, and check how they are executing the data science projects. Last but not least, present your insights & analysis to others.

But you might be wondering what skills do you exactly require for being a successful data scientist & how to Learn Data Science? What steps do you need to follow to leap into the field of data science?  

Before we get started with the actual data science career path, which of the following expertise/skills do you have?

An insight of Data Science roadmap

Since you now know what skills you already possess, the roadmap below can help you understand where you stand & what effort is needed for you to reach the endpoint.

Read more about Data Science Career 

Step 1: Getting started 

Before you move on to learning & adapting to new skills, it is important for you to understand what data science is & whether you are a great fit for it or not.

To further assess, check what type of data scientist you are with the below short quiz: 

Step 2: Learn the basics of mathematics & statistics  

The next checkpoint in the data science career path is to learn the fundamentals of mathematics & statistics. The topics listed below should be your area of focus: 

1. Descriptive Statistics 
2. Probability  
3. Inferential Statistics  
4. Linear Algebra 
5. Structured Thinking  

You can further enrich your concepts with these 5 free statistics books, along with these amazing resources to learn math for data science. If you are wondering why math is needed, then you need to take a quick look at this blog post by Dave Langer from Data Science Dojo that explains why math is important in data science.  

Step 3: Acquainting the key tools for data science 

1. Python: It is one of the most popular & widely used programming languages. Learning this language can help you with creating web applications, handling big data, rapid prototyping, and much more.  

Learn all the fundamentals of Python for Data Science with our upcoming training! 

2. R: Another popular language for programming in R. It provides a free software environment for statistical computing. These few blog posts can definitely add value to your knowledge of R programming:  

1. Logistic Regression in R
2. R language programming for Excel Users
3. Natural language Processing with R programming books

You might be stuck with the same traditional argument between R Versus Python; if you are wondering which one of them you should opt for, then I suggest you begin with R and transition to Python gradually. Then use them as per the needs of your organization.  

3. Data Exploration & Visualization: If you are into the analytical side of the data, i.e. data analysis, then you must learn data exploration & visualization. Data exploration is the initial step of data analysis, while, data visualization is the graphical representation of the data itself. Both Python & R can be used for exploring & visualizing the data.

Step 4: Learning the key tools for ML 

There are some basic and advanced machine learning tools that you need to learn & adapt yourself to. Some of the most important ones are listed below. These skills can be of immense value in your overall data science roadmap:  

1. Exploratory Data Analysis & Data Cleaning: Before moving on to the ML tools, you need to be well versed in what EDA & data cleaning is. EDA or exploratory data analysis, is a way of studying the datasets to summarize them into a visual format. Data cleaning is the process of detecting & correcting errors and ensuring that the data is free of errors.

     The cheat sheet below can help you get started with EDA now.

2. Feature Selection & Engineering: This should typically be your next step in learning ML. This uses domain knowledge to obtain the features from the data, which in turn helps with improving the performance of ML algorithms. So, if you are willing to gain expertise in the ML domain, you need to learn about feature selection & engineering.

3.  Model Selection: Out of all the statistical models, you will need to select one model that is well-suited for your problem. These are some of the statistical models that you can go with:

 Interested in learning about Large Language Models and building custom ChatGPT like applications for your business? Click below

The below graphic by Avik Jain can be a great help for you to get started with SVMs: 

4.  Model Evaluation: Moving towards the last step of machine learning, model evaluation, generalizes the accuracy of the model based on future data. It typically uses two methods, holdout & cross-validation.

Step 5: Profile building 

Building a profile on GitHub is an important task that every data scientist must complete. It is one of the most effective ways for a data scientist to gather all the code of the projects they have undertaken. It showcases your code and projects undertaken and shows how long you have been practicing data science.  

Moving on, you need to be part of some discussion forums. These will help you find an answer to the questions you are stuck at. Here are some of the discussion forums you can be part of: 

1. Quora  
2. Stackoverflow 

To gain more knowledge in the data science domain, start following different YouTube channels.   
Our YouTube channel can surely be a good start for you.  

Step 6: Prepare for a data science interview  

You need to know all those key data science concepts that can help you ace your interviews. With these 101 Data Science Interview Questions. Answers, and Key Concepts you can prepare yourself for the interviews.

Step 7: Take a look at a typical data scientist’s job 

Reaching the end of your data science roadmap, you might want to get an idea of a typical data scientist’s job. It is always helpful to look at some job descriptions, showcase your skills, and stand out as the best candidate. If you think you are a good fit for it, you must get started right away!

Before I end this post, let me repeat it again, instead of trying to learn all the skills required to be a data scientist endlessly, pick up a problem that inspires you or bees relevant to your domain. Try to solve that problem using the data science skills, only pick up the skills necessary to solve that problem. As you solve more problems, you will learn more skills along the way.

If you hated probability in high school or university, it is because every example of probability has to do with coin tosses and dice. But if you happen to come across interesting problems, such as the Birthday Paradox, you might have ended up loving probability.

Additional support  

Want to learn more about data science roadmap? The following blog posts have been a great support to me, and likewise, I believe it can be a great help to you as well:

So, what have you decided? Are planning to get started with Data Science? Take a look at our Data Science Bootcamp, a great way to start your data science journey.

Process Mining is a critical skill needed by every data scientist and analyst for mining rich and varied data contained in event logs.

Event logs are everywhere and represent a prime source of big data. Event log sources run the gamut from e-commerce web servers to devices participating in globally distributed Internet of Things (IoT) architectures.

Even Enterprise Resource Planning (ERP) systems produce event logs! Given the rich and varied data contained in event logs, process mining these assets is a critical skill needed by every data scientist, business/data analyst, and program/product manager.

At the meetup for this topic, presenter David Langer showed how easy it is to get started process mining your event logs using the OSS tools of R and ProM.

David began the talk by defining which features of a dataset are important for event log mining:

Activity: A well-defined step in some workflow/process.

Timestamp: The date and time at which something worthy of note happened.

Resource: Staff and/or other assets used/consumed in the execution of an activity.

Event: At a minimum, the combination of an activity and a timestamp. Optionally, events may have associated resources, life cycle, and other data.

Case: A related set of events denoted, and connected, by a unique identifier where the events can be ordered.

Event Log: A list of cases and associated events.

Trace: A distinct pattern of case activities within an event log where each activity is present at most once per trace. Event log typically contain many traces.

Below is an example of IIS Web Server data that may be used for process mining:

In this example, the traces for this event log are:

1. portal, dashboard, purchase order report
2. portal, help, contact us
3. portal, my team, expense reports

David proceeded his talk with a live demo using the Incident Activity Records dataset from the 2014 Business Processing Intelligence Challenge (BPIC).

About the meetup

In this presentation hosted by Data Science Dojo:• The scenarios and benefits of event log mining• The minimum data required for event log mining• Ingesting and analyzing event log data using R• Process Mining with ProM• Event log mining techniques to create features suitable for Machine Learning models• Where you can learn more about this very handy set of tools and techniques for process mining.

Process mining source code

David’s source code can be viewed and cloned here, at his GitHub repository for this meetup. To clean and process the dataset, he ran through his R script step-by-step. David installed the R package, edeaR, which was specifically used to analyze and the dataset.

After cleaning the dataset, he loaded the new .csv file into the process mining workbench tool, ProM, for visualization. The visualization created helped gain insights about the flow of incident activities from open to close.

Speaker: David Langer

The number of applications for data scientist programs has increased. With various online resources, is it necessary to take a university degree in Data Science?

Data Science is one of the fastest-growing fields, and the data shows this trend will continue into the near future. Data Science has become the backbone of many fields – it is the data science that helps us make sense of the information we collect during marketing campaigns, and it is the data science that helps us construct economic models that predict macroeconomic trends. It’s a field bustling with technological innovation, and people studying it will be at the forefront of multiple industries in the years and decades to come.

If you are someone who wants to join the ranks of data scientists, you have multiple ways of achieving your goals, including going to a university, taking online data science courses, and lastly self-learning. Which of these approaches is the best one? Is it still necessary to go to university to have the best prospects of landing a job? This article will answer these questions and help you decide how to approach this exciting new field.

Why might you still need a university degree?

The days that universities were for diving into academic studies are long gone. The recent advances in technology and the plethora of online resources have made it extremely easy for motivated individuals to learn on their own.

Instead, the university is a place for you to socialize and network with influential people from your field of study. While we like to think we live in a meritocracy where people succeed by skill alone, that has never been true. It is not only about what you know; it is about who you know.

Your university will give you numerous chances to present yourself and your skills to eminent professors and influential people who’d be able to help you start a successful career. It is much easier to jump-start your career when you have direct access to employers instead of being one of the hundreds of online resumes, they receive each day.

The difficulty of getting the fundamentals right without an academic setting

Not all academic fields are created equal when it comes to online teaching platforms. There are certain fields of study like computer science and language studies that rely mostly on a passive intake of information, and that makes them excellent subjects to learn online.

Other subjects like philosophy and mathematics require methodological approaches and engaging extensively with professors and classmates, and these present significant hurdles for a self-learner. They’ll have to try harder to learn the concepts and follow the material if they want to learn these subjects, and many online learners aren’t motivated to do so.

While data science is looked at as a subfield of computer science, it requires a good grounding in the fundamentals of Calculus and extensive knowledge of statistics and probability. Due to the field’s heavy reliance on math, an online learner might have trouble handling the subjects.

A good university will provide you with receptive professors and like-minded fellow students that’ll help you engage with the harder subjects and stay motivated.

Innovative approaches making universities obsolete

While self-study textbooks and online video courses have been on the market for decades now, a wave of innovations in teaching methods is starting to threaten our traditional institutions, and the top two approaches, which might prove to be more effective than universities, are interactive learning platforms and gamified learning:

Interactive learning platforms

These were developed in the hopes of making the online learner more proactive. Studies have shown that passively listening to online courses without participation isn’t an effective method of learning.

If you use these platforms, you won’t just learn what a piece of computer code does, but you’ll be asked to use it to solve a problem. You won’t just be told about price equilibrium in Economics, but the platform will tell you to explain a system using the theory. This way you will be able to immediately apply the knowledge you’ve acquired, which makes learning the fields like economics and mathematics much easier.

Gamified learning

One thing the last decade has shown us is how effective games are in capturing people’s attention and gluing them to their seats. That’s why some educators and psychologists have done extensive research to help bring over some aspects of gaming to education.

Correct use of gaming principles in a learning system will make it easier for you to focus on learning more, retain more of the information, and feel less fatigue after long studying sessions. While this method is still in its infancy, it is already showing great promise.

The show, don’t tell: How can you start a career as a data scientist

While choosing to opt out of enrolling in a university might prevent you from networking, and it is really hard for online resumes to help you stand out, there are new ways and platforms where you can show your skills!

Competition Sites

Competition sites like Kaggle provide an excellent training ground for budding data scientists to show their skills. They provide competition from diverse fields from economics to computer vision. The people who come up with the best algorithms not only get monetary rewards, but they have a great chance of getting job offers. Most employers will be impressed if you achieve good results in these competitions as it shows a practical understanding of the field beyond academics.

Github and Jupyter Notebooks

Github and Jupyter Notebook allows you to present data analyses in a readable and concise format. Instead of boring old CVs, employers are more receptive to a rich portfolio. Thanks to the tools being completely free and intuitive to use, you’re only limited by your skills when it comes to the projects you tackle. You can build an amazing portfolio from the comfort of your home.

Conclusion

The answer isn’t cut-and-dry, and while there have been some movements claiming universities have become completely redundant in 2018, there are still some real benefits to them. You should ask yourself if you’d thrive in an academic setting, if yes, then you’d probably see sizable benefits from attending university. On the other hand, the new approaches to learning and portfolio building have made it easier than ever to succeed on your own, and you can do it if you are motivated enough.

You might also like: Is it worth going to university anymore?

Data Science is a hot topic in the job market these days. What are some of the best places for Data Scientists and Engineers to work in?

To be honest, there has never been a better time than today to learn data science. The job landscape is quite promising, opportunities span multiple industries, and the nature of the job often allows for remote work flexibility and even self-employment. The following post emphasizes the top cities across the globe with the highest pay packages for data scientists.

Industries across the globe keep diversifying on a constant basis. With technology reaching new heights and a majority of the population having unlimited access to an internet connection, there is no denying the fact that big data and data analytics have started gaining momentum over the years.

Demand for data analytics professionals currently outweighs supply, meaning that companies are willing to pay a premium to fill their open job positions. Further below, I would like to mention certain skills required for a job in data analytics.

Python

Being one of the most used programming languages, Python has a solid understanding of how it can be used for data analytics. Even if it’s not a required skill, knowledge and understanding of Python will give you an upper hand when showing future employers the value that you can bring to their companies. Just make sure you learn how to manipulate and analyze data, understand the concept of web scraping and data collection, and start building web applications.

SQL (Structured Query Language)

Like Python, SQL is a relatively easy language to start learning. Even if you are just getting started, a little SQL experience goes a long way. This will give you the confidence to navigate large databases, and obtain and work with the data you need for your projects. You can always seek out opportunities to continue learning once you get your first job.

Data visualization

Regardless of the career path, you are looking into, it is crucial to visualize and communicate insights related to your company’s services, and is a valuable skill set that will capture the attention of employers. Data scientists are a bit like data translators for other people who exactly know what conclusions to draw from their datasets.

Best opportunities for a data scientist

Have a look at cities across the globe that offer the best opportunities for the position of a data scientist. The order of the cities does not represent any type of rank.

1. San Jose, California – Have you ever dreamed about working in Silicon Valley? Who hasn’t? It’s the dream destination of any tech enthusiast and an emerging hot spot for data scientists all across the globe. Being an international headquarters and main offices of the majority of American tech corporations, it offers a plethora of job opportunities and high pay. It may interest you to know that the average salary of a chief data scientist is estimated to be $132,355 per year.
2. Bengaluru, India – Second city on the list is Bengaluru, India. The analytics market is touted to be the best in the country, with the state government, analytics startups, and tech giants contributing substantially to the overall development of the sector. The average salary is estimated to be ₹ 12 lakh per annum ($17,240.40).
3. Berlin, Germany – If we look at other European countries, Germany is home to some of the finest automakers and manufacturers. Although, the country isn’t much explored for newer and better opportunities in the field of data science, it seems to be expanding its portfolio day in and day out. If you are a data scientist, you may earn around €11,000, but if you are a chief data scientist, you will not be earning less than €114,155.
4. Geneva, Switzerland – If you are seeking one of the highest paying cities in this beautiful paradise; it is Geneva. Call yourself fortunate, if you happen to land a position as a data scientist. The mean salary of a researcher starts at 180,000 Swiss Fr, and a chief data scientist can earn as much as 200,000 Swiss Fr with an average bonus ranging between 9,650-18,000 Swiss Fr.
5. London, United Kingdom – One of the top destinations in Europe that offers high-paying and reputable jobs in London. UK government seems to rely on technologies day in and day out, due to which the number of opportunities in the field has gone up substantially, with the average salary of a Data Scientist being £61,543.

I also included the average data scientist salaries from the 20 largest cities around the world in 2019:

1. Tokyo, Japan: $56,783
2. New York City, USA: $115,815
3. Mexico City, Mexico: $32,487
4. Sao Paolo, Brazil: $45,891
5. Los Angeles, USA: $120,179
6. Shanghai, China: $66,014
7. Mumbai, India: $29,695
8. Seoul, South Korea: $45,993
9. Osaka, Japan $54,417
10. London, UK: $56,820
11. Lagos, Nigeria: $48,771
12. Calcutta, India: $7,423
13. Buenos Aires, Argentina: $40,512
14. Paris, France: $37,861
15. Rio de Janeiro, Brazil: $54,191
16. Karachi, Pakistan: $6,453
17. Delhi, India: $20,621
18. Manila, Philippines: $47,414
19. Istanbul, Turkey: $30,210
20. Beijing, China: $72,801

Kaggle Days Dubai is a data science competition to improve your data science skillset. Here’s what you can expect to learn from the grandmasters.

Anyone interested in analytics or machine learning would certainly be aware of Kaggle. Kaggle is the world’s largest community of data scientists and offers companies to host prize money competitions for data scientists around the world to compete in. This has made it the largest online competition platform too. However, Kaggle has started to evolve itself to organize offline meetups globally.

One such initiative is the organization of Kaggle Days. Up till now, four Kaggle Days events have been organized in various cities around the world, the recent one being in Dubai. The format of Kaggle Days involves a 2-day session consisting of presentations, practical workshops, and brainstorming sessions during the first day followed by an offline data science competition the next day.

For a machine learning enthusiast with intermediate experience in this field, participating in a Kaggle-hosted competition and teaming up with a Kaggle Grandmaster to compete against other grandmasters was an enjoyable experience on its own for me. I couldn’t reach the top ranks in the data science competition, but competing with and networking with the dozens of grandmasters and other enthusiasts present during the 2-day event boosted my learning and abilities.

I desired to make the best use of this opportunity, learn to the utmost extent I could, and ask the right questions from the grandmasters present at the event to get the best out of their wisdom and learn the optimal ways to approach any data science problem. It was heart-whelming to discover how supportive they were as they shared tricks and advice to get to the top position in data science competitions and improve the performance of any machine learning project. In this blog, I’d like to share the insights that I gathered during my conversations and the noteworthy points I recorded during their presentations.

Strengthen your basic knowledge of Kaggle

My primary mentor during the offline competition was Yauhen Babakhin. Yauhen is a data scientist at H2O.ai and has worked on a range of domains including e-commerce, gaming, and banking, specializing in NLP-related problems.

He has an inspiring personality and is one of the youngest Kaggle Grandmasters. Fortunately, I got the opportunity to network with him the most. His profile defied my misconception that only someone with a doctoral degree can achieve the prestige of being a grandmaster.

During our conversations, the most significant advice that came from Yauhen was to strengthen our basic knowledge and have an intuition about various machine learning concepts and algorithms. One does not need to go extensively deep into these concepts or be extra knowledgeable to begin with. As he said, “Start learning a few important learning models, but get to know how they work!”

It will be ideal to start with the basics and extend your knowledge along the way by building experience through competitions, especially the ones hosted on Kaggle. For most of the queries, Yauhen suggests, one must know what to search on Google. This alone will prove to be an extremely handy tool on its own to get us through most of the problems despite having limited experience relative to our competitors.

Furthermore, Yauhen emphasized how Kaggle single-handedly played a leading role in heightening his skills. Throughout this period, he stressed on how challenges triggered him to perform better and learn more.

It was such challenges that provoked him to learn beyond his current knowledge and explore areas beyond his specialization, such as computer vision, said the winner of the $100,000 TGS Salt Identification Challenge. It was these challenges that prompted him to dive into various areas of machine learning, and it was this trick that he suggested we use to accelerate career growth.

Through this conversation, I was able to learn the importance of going broad. Though Yauhen insisted on selecting problems that target a broad range of problems and cover various aspects of data science, he also suggested limiting it to the extent that it should align with our career pursuits and make us realize if we even need to target something beyond what we are ever going to use.

Lastly, the Grandmaster in his late 20’s also wanted us to practice with deep learning models as it’ll allow us to target a broad set of problems to discover the best approaches used by previous winners and to combine them in our projects or competition submissions. These approaches could be found in blogs, kernels, and forum discussions.

Remain persistent

My next detailed interaction was with Abhishek Thakur. The conversation provoked me to ask as many questions as I could, as every suggestion given by Abhishek seemed wise and encouraging. One of the rare examples of someone crowned with 2 Kaggle Grandmaster titles, competitions, and discussion grandmasters, Abhishek is the chief data scientist at boost.ai, having once attained the 3rd rank in global competitions at Kaggle.

What made his profile more convincing was Abhishek’s accelerated growth from a novice to a grandmaster within a year and a half. He started his career in machine learning from scratch and took this initiative from Kaggle itself. Initially starting with the lowest rank in competitions, Abhishek was adamant that Kaggle could be the only platform one could totally rely on to catapult his growth within such a short time.

However, as Abhishek repeatedly said, it all required continuous persistence. From the beginning until now, even after being placed in the bottom ranks initially, Abhishek carried on and demonstrated how persistence was the key to his success. Upon inquiring about the significant tools that led him to get gold in his recent participation, Thakur emphasized immensely on feature engineering.

He insisted that this step was the most important of all in distinguishing the winner. Similarly, he suggested that a thorough exploratory data analysis can assist one in finding those magical features that can enable one to get the winning results.

Like other Grandmasters who have attained massive success in this domain, Abhishek also emphasized improving one’s personal profile through Kaggle. Not only does it offer you a distinct and fast-paced learning experience, as it did for all the grandmasters at the event, but it’s also recognized across various industries and major employees who value these rankings. Abhishek told how it enabled him to get numerous lucrative job offers over time.

Start instantly with data science competitions

On the first day, I was able to attend Pavel Pleskov’s workshop on ‘Building The Ultimate Binary Classification Pipeline’. Based in Russia, Pavel currently works for an NLP startup, PointAPI, and was once ranked number 2 among Kagglers globally. The workshop was fantastic, but the conversations during and after the workshop intrigued me the most as they mostly comprised tips for beginners.

Pavel, who quit his profitable business to compete on Kaggle, Pavel insisted on the ‘do what you love’ strategy as it leads to more life satisfaction and profit. Pavel told us how he started with some of the most popular online courses on machine learning but found them lacking practical skills and homework, which he covered using Kaggle.

For beginners, he strongly recommended not to put off Kaggle contests or wait until the completion of courses, but to start instantly. According to him, practical experience on Kaggle is more important than any other course assignment.

Some other noteworthy and touching tips from Pavel were that to win such competitions, unlike many students who approach Kaggle as an academic problem and start creating fancy architectures and ultimately do not score well, Pavel approaches a problem with a business mindset. He increased the probability of success by leveraging resources, such as including people in his team who had resources, like a GPU, or merging his team with another to improve the overall score.

Upon an inquiry related to keeping the right balance between taking time to build theoretical knowledge and using that time to generate new ideas, Pavel advised looking at forum threads on Kaggle. They can help you know how much theoretical knowledge you are missing while competing with others.

Pavel is an avid user of LightGBM and CatBoost models, which he claims have given him superior rankings during the competitions. One of his suggestions is to use the fast.ai library, which, despite receiving many critical reviews, has been a flexible and useful library that he mostly keeps in consideration.

Hunt for ideas and rework them

Due to the limitation of time during the 2-day event, I was able to hear less from another young grandmaster from Russia, coincidentally sharing the same first name with his fellow Russian grandmaster, Pavel Ostyakov. Remarkably, Pavel was still an undergrad student then and has been working for Yandex and Samsung AI for the past couple of years.

He brought a distinct set of advice that can prove to be extremely resourceful when one is targeting gold in data science competitions. He emphasized writing clean code that could be used in the future and allows easy collaboration with other teammates, a practice usually overlooked which later becomes troubling for participants. He also insisted on trying to read as many forums on Kaggle as one could.

Not just ones related to the same competition but those belonging to other data science competitions as well since most of them are similar. Apart from searching for workable solutions, Pavel suggested also looking for ideas that failed. As he recommended, one must try using (and reworking) those failed ideas as there are chances they may work.

Pavel also brought up the point that to surpass other competitors, reading research papers and implementing their solutions could increase your chances of success. However, during all this time he stressed a lot on to have a mindset that anyone can achieve gold in a competition, even if he/she possesses limited experience relative to others.

Experiment with diverse strategies

Other noteworthy tips and ideas that I collected while mingling with grandmasters and attending their presentations included those from Gilberto Titericz (Giba), the grandmaster from Brazil with 45 Gold medals! While personally inquiring about Giba, he repeatedly used the keyword ‘experiment’ and insisted that it is always important to experiment with new strategies, methods, and parameters. This is one simple, although tedious, way to learn quickly and get great results.

Giba also proposed, that to attain top performance, one must build models using different viewpoints of the data. This diversity can come from feature engineering, using varying training algorithms, or using different transformations. Therefore, one must explore all possibilities.

Furthermore, Giba suggested that fitting a model using default hyperparameters is good enough to start a data science competition and build a benchmark score to improve further. Regarding teaming up, he repeated that diversity is the key here as well, and choosing someone who thinks similar to you is not a good move.

A great piece of advice that came from Giba was to blend models. Combining models can help improve the performance of the final solution, especially if each model’s prediction has a low correlation. A blend can be something as simple as a weighted average. For instance, non-linear models like Gradient Boosting Machines blend very well with neural network-based models.

Conclusion

Considering the key takeaways from the suggestions given by these grandmasters and observing the way they competed during the offline data science competition, I noted that beginners in data science must use their efforts to try varying methodologies as much as they can.  Moreover, a summary of the recommendations given above stresses the significance of taking part in online data science competitions no matter how much knowledge or experience one possesses.

I also noted that most of the experienced data scientists were fond of using ensemble techniques and one of the most prominent methods used by them was the creation of new features out of the existing ones. This is what was cited by the winners of the offline data science competition as their strategy for success. Conclusively, these sorts of meetups could enable one to interact with the top minds in the field and gain the maximum within a short time as I fortunately did.