What are the most effective ways to develop a successful career in data science?

As much as formal training in Data Science is helpful, I believe having a solid foundation in critical reasoning and independent work is far more important. Algorithms change frequently and continuing education is always needed. What's not taught as frequently is how to properly troubleshoot data science. Mostly that comes with experience with working on hard, open ended questions. As for skill sets, I think having a good math (linear algebra at least) and statistics background is a must.

In my experience, I would say understanding the business problem is the most important thing to be successful in data science. Applying the technical stuffs are secondary, one has to understand the underlying concept of the business requirements. The second thing is the communication skills. Having a strong communication skills allow us to share the insights to both technical and non-technical audiences.

Focus on building and maintaining relationships with non-data scientists, and with data scientists who have been recently promoted into a role one or two levels up from yours.

In my experience, majoring in a technical field will be the most effective form of education for a successful career in data science. This can include majors like Computer Science, Data Science, or even Information Systems. Once you master coding in languages like Python and SQL, you will be able tooexecute tasks more efficiently. There are other great tools offered for free for you to test and practice your learning like Kaggle or leetcode.

In addition to a strong foundation in mathematics and statistics, a needed skill for any data scientist is the ability to formulate high-quality prompts tailored to specialized domains like healthcare, manufacturing, media, education, and more.

In my experience, start from basic mathematics and statistics. Move on to complex mathematical and statistical equations. Then try to embed them in the algorithm. This will help develop strong understanding of the subject issue.

In my experience, start from basic mathematics and statistics. Move on to complex mathematical and statistical equations. Then try to embed them in the algorithm. This will help develop strong understanding of the subject issue.

To become proficient in data science and Python, one should have a strong foundation in math and statistics. Comfort with linear algebra, calculus, probability, and statistics is vital. In data science, math is used to perform calculations, create models, and make predictions. Some of the key mathematical concepts used in data science include linear algebra, calculus, probability, and statistics.Python libraries such as NumPy, Pandas, and Matplotlib are tools to perform complex mathematical and statistical operations. NumPy provides support for multi-dimensional arrays and matrix operations. Pandas provides data structures and functions for data manipulation and analysis. Matplotlib provides tools for data visualization and plotting.

Critical technical skills, including computer programming, machine learning, data exploration, analysis, and visualization, are paramount in data science. Equally crucial are often overlooked soft skills like effective communication, interaction with business leaders, and a desire to develop deep understanding of business. Ignoring these soft skills can hinder a successful data science career.

Apesar das plataformas como Kaggle e Datacamp não trazerem uma experiência real de problemas corporativos, sempre usei como ferramenta para me desafiar e aprender mais sobre análise e técnicas de ciência de dados. Quanto mais contato com problemas, mais você ficará familiarizado com o processo de um projeto de IA. Eu gosto de ver notebooks do pessoal que resolveu o desafio para comparar com meu raciocínio e processo. Sempre aprendo algo novo com estas comparações…

Focus on three pillars i.e. Statistics, Business fundamentals, and any programming language you love (R/Python in fashion now). Let me reiterate that without understanding the Statistics basics, no one can be a data scientist.

Domain knowledge is crucial in data science, as it involves understanding the specific context, goals, and challenges of the industry where data science is applied. This includes familiarity with terminology, regulations, ethical issues, and relevant data sources. For instance, in healthcare, knowledge of medical terms, anatomy, pharmacology, and healthcare regulations like HIPAA and GDPR is key. This helps in understanding data, spotting trends, and communicating with healthcare professionals. Similarly, in finance, understanding financial markets, accounting principles, risk management, and regulations such as SEC and FINRA is vital for analyzing financial data, creating predictive models, and advising investors and executives.

Data science value is when you can tell and sell your story. For telling and selling your story, one has to know what is our product. In that sense domain knowledge plays a crucial role. Data Science will always be a tool for helping some business problem and knowing that business is key to solving that problem. When one has to know about the domain and have to solve that problem few basic questions needs to be asked from ourselves: 1. What is the sector and what is the position of specific company for which data science is being used 2. Jargons of sector so that story telling can be interesting 3. How data science can mislead to that sector problem 4. Telling senior management and industry people data story in there language

After learning the basics, working on real world projects is the best way to excel in data science. Even in a single field like transit planning there are a huge amount of unique datasets packaged into various forms across different agencies. Cleaning, transforming, and aggregating data in different forms is critical.

As a starter in data science career getting real world data might be harder. Better focus on kaggle, Analytics Vidhya, Hacker Earth for datasets or competition involvement in these platforms will give you confidence on your concepts which you have learned in courses or videos.

When we are talking about folks who are new to the field and have checked all the bullets above there are only two ways to get exposed to real world problems: - Hackathons: the quickest way of getting exposure to some real world problems. Limitation being the data is well curated which you’ll never find in real industry. Also, curated solutions are usually readily available as well so one is not solving the problem from scratch - Solving a unique problem around you from real world: find topics that excite you, gather relevant data and try to solve the problem however vague it may be. e.g. possible trading strategy for Apple stock. As a hiring manager I can certainly say that doing latter would be a key differentiator in any interview

With individuals new to data science, one of the most significant learning curves is dealing with the messiness of real-world data. I frequently see new data scientists doing everything by the book without consideration for how that shapes the usability of the data. One recent example was comparing outcomes of eating different foods. The analyst normalized all of the foods to 100 g portions, even though some foods in the list would be consumed in servings 3x that size while others 1/10th that size. The result is that the analysis wasn’t useful to anyone. This type of normalization is what is consistently pushed in school, but in this real-world situation, it would have been more appropriate to normalize to each food’s standard serving size.

Data Science is a rapidly growing field that has become extremely popular in recent years. Additionally, it is crucial to have hands-on experience with real-time projects to apply theoretical knowledge to practical scenarios. This is where ONLEI Technologies comes in. They provide an excellent platform for individuals to not only gain a comprehensive understanding of data science but also work on real-time projects with industry experts. This hands-on experience not only enhances one's technical skills but also provides valuable industry insights that can be applied in future projects. Moreover, ONLEI Technologies offers a zero to advanced level of knowledge, making it an ideal place for beginners to kick-start their data science journey.

Strive to become the data person who really understand the domain. As an example, I strive to be the data scientist who really understands how life sciences works in the lab, since the primary people my teammates and I interact with are analytical chemists, biologists, and everything in between.

Data science thrives on both collaboration and competition, making networking and reputation-building key for career advancement. Engaging in the community can be achieved through various methods: Attending Events: Participate in conferences, workshops, or meetups. Joining Communities: Become an active member of online forums or local groups focused on data science. Reaching Out to Mentors: Seek guidance from experienced professionals . Participating in Discussions: Engage in conversations on platforms like Reddit or specialized data science forums. Regularly posting articles, code repositories, or updates about your data science journey on Platforms like Medium, GitHub, LinkedIn, and Twitter can significantly enhance your visibility.

Data Science is ever evolving field due to its dependence on ability of technology to calculate. A person to person connect matters due to this ever changing mode of this field. Few collaborative idea are as below: 1. Take ideas about making your work inclusive of all the dimensions 2. There may be some shortcuts to solving a problem and you may be not aware of that 3. Posting your work and insights make you forward thinking as you are ready to enable new ideas in your work 4. Posting your real life problem in work make academicians to know what it is like to work on data Aling with these benefits there are some shortcomings also like intellectual property theft. Still we have to work and have to tell, discuss and evolve in our work.

Stay updated on data science trends through conferences, workshops, webinars, and online communities. Network effectively by attending industry events, joining forums, and connecting with professionals to unlock collaboration, mentorship, and job referrals. Personally, I have found LinkedIn, along with various relevant groups and communities, very helpful in building a strong network and staying up-to-date with the latest trends in the industry.

In science as we are measuring so there is no end. Due to this nature data science will evolve and evolve only. To keep yourself updated one has to read, read and read. There is no shortcut of reading but what one have to read for that you can follow below suggestions: 1. Govt policies releated to AI 2. Keep in regular touch of one of the magazine like AIM, BizTech Magzine etc 3. Read research papers where AI is being used 4. Read regular sections of science and tech from daily news papers Learning from above sources and applying that in your work is key for your growth in analytics field. In addition of this be in regular touch with researchers/academicians of AI field and learn from them as nothing can replace learning from human.

The most important thing for a data scientist to do is to talk to people. Generally within organizations, there are lots of problems that can be simplified, automated or enhanced through automation and analytics. However, in many cases, people may not know how data science can streamline their current work, and so talking with people more generally allows you to find greenfield areas in which small efforts can yield big organizational pay-offs.

Sometimes I think we talk about technical skills at the expense of human skills. Being able to communicate business value with data is a sure way of levelling up your career in Data Science. This is in addition to building team player skills and leadership skills along the way. Being able to communicate what you know is more valuable than knowing.

Lembre-se sempre que entender o negócio é parte de seu crescimento como cientista de dados e é de suma importância para melhorar suas entregas e qualidade de seus projetos!