What can a data science mentor teach you about the latest technologies?

Gain insights into leveraging cloud platforms for scalable data solutions. Example: "Explore AWS, Azure, or Google Cloud for efficient data storage and processing."

Various tools and services built by cloud platforms like AWS, Azure, and GCP help solve specific challenges encountered by teams and professionals when working on larger scale data science tasks. Having a good awareness on what tools and services are available and having a solid experience in using relevant services and cloud resources to solve specific data science requirements would help teams unleash the potential of these cloud solutions.

Cloud Computing technologies is one of the important parts for building and developing data-driven solutions. It's necessary for any Data Scientist, Data Engineer and ML Engineer to have cloud-computing foundations to develop data and ML solutions on the cloud. At this time all cloud computing platforms such as Azure, AWS and GCP provide different services for Data Processing, Analysis, Storage, Visuallization to help building a complete Data solution on the cloud. It Also provides professional certifications to master essential skills in their cloud computing platforms for specific major such as Data Engineering, Machine Learning and DevOps.

A mentor is not the person you should ask about the latest and greatest tools. Mentors are typically a bit further away from the bleeding edge and higher up in the organization. Mentors are fantastic to offer their opinion grounded in experience about how to do data science (unearthing common pitfalls; what to consider in corner cases; strategies to improve performance; etc...). And mentors are more, they can explain you things beyond the technical considerations: what can be commercialised? what do customers really want? what is the direction of the organization? Personally, I enjoyed the non-technical mentorship most as it widened my perspective the most. When mentoring, that is also what I provide now.

Managing cloud computing, cloud services and cloud infrastructure optimizations are responsibilities typically for a full stack data scientist or Machine Learning Engineering roles. Any Data Science problem can be tackled with various products from any cloud provider. A mentor can help decide which ones to use more accurately depending on each specific use case.

Learn the intricacies of deep learning models and applications. Example: "Implement neural networks with TensorFlow or PyTorch for advanced pattern recognition."

In the era of AI it's important as Data Scientist and Machine Learning Engineer to have fundemental understanding of how these Machine Learning Models built. Generative AI is the main technology that is driving the advancement of AI now. ChatGPT which is a type of Generative AI called Large Language Model that take input text as prompt and return generated detailed text output based on the prompt. This LLM developed using technique in Deep Learning called Artificial Neural Network that trained on huge text datasets from internet resources. This complex neural network has billions of parameters that give it this unique capability in understanding the context of the text prompt input and return the most suitable answer for it.

Deep Learning can be overwhelming at first, there are lots of concepts and frameworks to learn. The cost for training and fine tuning models is also very difficult to judge and easy to get out of control. A mentor can help by guiding where to best allocate efforts and resources to use while explaining the tradeoffs that each decision can produce.

Deep learning in data science is a cutting-edge technology that involves neural networks with multiple layers to analyze and process complex data. It excels in tasks like image and speech recognition, natural language processing, and decision-making. Its ability to automatically learn and adapt from data makes it a powerful tool for extracting valuable insights. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have revolutionized various industries by providing superior performance in pattern recognition and prediction tasks. Keeping up with the latest advancements in deep learning is crucial for staying at the forefront of technological innovation in data science.

Data narrative always wins. Often time we go for p value or any time of model performance metric to justify results. Visualization, narrative, and translation of results is ultimately how a data science project succeeds

My experience has taught me that effective data visualization is storytelling. It transforms complex data into clear, impactful stories. My tip? Experiment with visualization tools and focus on creating visuals that tell a compelling story, making data accessible and actionable for all.

A mentor can teach you that data visualization is not about creating the coolest or most complex graphs, but about effectively communicating information tailored to your audience. Understanding who you're presenting to is crucial; sometimes a simple bar graph can convey the message more powerfully than a complex chart. The goal is clarity and impact, not just aesthetics. Always focus on making the data understandable and relevant.

As a mentor, I can share my experience and what other mentors have mentally. Mentors can teach you the fundamentals of Machine Learning and Deep learning in layman's way. They can give you the real life working experience that a Data Science person do in day to day life. They can mentor how to do end to end pipeline for any data science project. They can give the extra skills that requires to stay top in the industry examples like, emailing, documentation, presentations, Technical communications, project management, Time management, Office politics, Solutions Architecture, Testing, dressing sense, behavioral etiquette etc. An experienced mentor has too much yo give you. First find a god mentor,Please trust your mentor and do what he says.

This is era of getting some extra. This is not expected in FMCG but also in tech, they say it good to have. As a Data Scientist having Data visualization skill is must e.g. Matplotlib, Seaborn, Plotly, etc. but becoming versatile is good to have, if you have hands-on with multiple visualization tools like PowerBI, Tableau, Spotfire is what stands you out of crowd, It can give you the power to vision which use can can be better handled by the tool. These things also help you to be good to great story teller around data, At the end of the day you will be solution architect or project manager after years of experience so learn as much as possible gradually.

Big data refers to large and complex data sets that cannot be easily managed or analyzed using traditional methods. It is characterized by its volume, velocity, and variety of data sources. Big data offers valuable insights and opportunities for organizations to improve efficiency, make data-driven decisions, and develop innovative solutions. However, it also poses challenges related to data quality, privacy, and security. Specialized tools and technologies have emerged to process and analyze big data, but skilled data scientists and analysts are needed to extract meaningful insights. Effective use of big data provides a competitive advantage in today's data-driven world.

A data science mentor is an important figure in updating you on the latest technological developments, especially as regards Big Data. A mentor of such a kind offers insights into new development tools and frameworks aimed at handling large, but extremely complex data sets, keeping you abreast with the current trends. They can help you navigate the complexities of distributed computing platforms such as Apache Hadoop and Apache Spark, providing insights on their use for efficient processing and analysis of large datasets. Furthermore, a mentor can shed light on developments in cloud solutions by pointing out products such as AWS , GCP and Microsoft Azure allowing you to take advantage of resources based on scale for Big Data processing.

Big Data in data science refers to the vast and complex sets of information that exceed the capabilities of traditional data processing methods. In the context of the latest technologies, Big Data leverages advanced tools like machine learning, artificial intelligence, and distributed computing to extract valuable insights from massive datasets. It involves processing, analyzing, and interpreting data to uncover patterns, trends, and correlations. Big Data technologies enable organizations to make data-driven decisions, enhance efficiency, and gain a competitive edge in various industries. Embracing scalable and innovative solutions is crucial for effectively managing and deriving value from Big Data .

Big data has gone from a ripple to tidal wave to an ocean of structured and unstructured data. Big data can be handled in a variety of ways with applications and services. Data lakes have been a popular catch all for data but when unattened can turn into a data swamp. There is an emergence of data lake houses which seek to structure all data types and recently the data fabric concept has focused on system unification across applications and data.

Adhering to ethical standards as a data scientist builds your reputation, encourages trust, and benefits society. In a world that is becoming more and more data-driven, it also aids in navigating legal and regulatory issues. Interetingly ethical data science is important because it guarantees that data is used responsibly, safeguards privacy, and keeps models from producing biassed or discriminating results. Therefore a mentor should focus more on teaching the data ethical guidelines/standards.

Ethical data science in the context of the latest technologies emphasizes responsible and transparent use of data. It involves ensuring fairness and avoiding biases in algorithms and models. Privacy protection, informed consent, and secure data handling are paramount. Ethical data scientists prioritize accountability and actively work to mitigate potential negative impacts on individuals and society. Ultimately, the goal is to harness technological advancements in a way that aligns with ethical principles and respects the rights of data subjects.

With the strong rise of LLMs, the landscape of AI has witnessed a transformative shift. Individuals and businesses are heavily relying on the outputs of these models without clear processes that evaluate, audit or control the generated content. And with their immense potential to drive decision-making, LLMs carry the risk of unintended consequences and ethical dilemmas. This is why, ethical data science practices are crucial to be passed down from experienced professionals to the next generation. This ensures that emerging data scientists not only possess technical prowess but also understand the ethical implications of their work.

My view, mentor can teach on current Topics like Gen AI, how to build LLM models, also importantly they can suggest or assist on your domain related usecases development by using current tools and techniques.Additionally mentor can motivate and suggest him to contribute on presneting /Submmiting Tech. papers will helps to build skills sysmetic methodology.

Currently Generative AI is rising and it has disrupted the data science market. Skills like Prompt engineering is something the data science enthusiasts and mentors can look to add into their skill set. For example Earlier we used to write lines of code for Twitter sentiment analysis, now if we just pass the tweet/ any sentence and ask what is the sentiment, we will get the answer in seconds.

A Data Science mentor can be valuable guide for you in this rapidly developing domain. He can provide you with information about necessary skills and technologies you need to master in the job market. This recommendation include the programming languages you need to know it such as Python or R. Also the most common libraries in python language such as Pandas for data wrangling and analysis. Numby and scikit-learn for machine learning. Matplotlib and Plotly for data visualization. Also another important language you need to have experience in it is SQL for retrieving and manipulating data records in relational databases such as MySQL or Postgres which is core skill for any data scientist and data engineer.

The barrier of entry to creating an application using Deep Learning is getting lower every month. This is an exciting time to invest efforts in Deep Learning and the understanding of what all hype around Large Language Models, Generative AI and Transformers are all about!

Many times I have had to be a mentor as a data scientist for workers and students in different areas, where one of the aspects where I put greater emphasis on the relationship of technologies with theory and practice and how this union allows us to generate value for the organizations.