What data strategies are essential for a data scientist to know?

??Data governance may seem like an administrative hurdle, but for data scientists, it's a foundational strategy. ??Trustworthy Foundation Data governance ensures data quality, security, and compliance, empowering data scientists with reliable data for more trustworthy results. ??Collaboration & Efficiency Clear data access protocols and ownership structures foster collaboration, avoiding redundancies and streamlining workflows. ??Finding the Signal in the Noise Data governance helps define data standards and documentation practices. This makes data exploration and understanding more efficient, allowing data scientists to focus on extracting insights from the data.

As a biomedical engineer and data scientist, understanding key data strategies is crucial. Firstly, mastering data cleaning and preprocessing techniques ensures high-quality data for analysis. Next, proficiency in exploratory data analysis aids in uncovering patterns and insights. Additionally, knowledge of statistical methods enables robust inference and modeling. Finally, expertise in data visualization facilitates effective communication of findings. These strategies form the foundation for impactful data-driven decision-making in healthcare and beyond.

Essential data strategies for a data scientist include: 1. Data Cleaning: Ensure quality by handling missing values, outliers. 2. Feature Engineering: Create new features to improve model performance. 3. Exploratory Data Analysis: Understand data patterns before modeling. 4. Data Preprocessing: Standardize, normalize data for algorithms. 5. Model Selection: Choose appropriate algorithms based on data type. 6. Cross-Validation: Assess model performance with robust splitting. 7. Hyperparameter Tuning: Optimize models for best results. 8. Ensemble Methods: Combine models for improved predictions. 9. Time Series Analysis: Analyze and forecast time-dependent data. 10. Text Analysis: Process unstructured text data for insights.

Data governance is the guardian of truth, the custodian of integrity in the vast universe of data. As a data scientist, understanding the significance of data governance is akin to embracing the solemn duty of preserving the sanctity of information. It's about recognizing that behind every byte of data lies a story, a narrative waiting to be deciphered. Data governance is not merely about rules and regulations but about honoring the trust bestowed upon us by those who confide in data. It's about upholding the integrity of our profession and standing as unwavering sentinels against the tide of misinformation.

Key data strategies for data scientists include data collection and cleaning, exploratory analysis, feature engineering, model selection and evaluation, hyperparameter tuning, deployment and monitoring, data privacy and ethics, continuous learning, and effective communication. These skills enable effective data utilization for insights, model building, and decision-making, ensuring compliance, innovation, and impactful communication across stakeholders.

Let's get on a journey undertaken by data to understand which data strategies are essential - 1. Data Generation/Origination - Data Acquisition strategy is essential to harness the data and integrate in system. 2. Data Cleaning/Preprocessing - To make the data suitable for our analysis - Data Cleaning strategy is a must have. 3. Data Exploration/Analysis - Data needs to be further analyzed and explored to derive insights through Data Analysis Strategy. 4. Data Communication/Visualization - The end stage of data lifecycle is all about having data communication strategy to convey the insights. 5. Data Governance - To ensure data quality, compliance and security, Data Governance strategy is must have.

Learn how to collect and gather data from various sources, such as databases, APIs, sensors, and files. Familiarize yourself with techniques for data extraction, transformation, and loading (ETL).

Data acquisition is the art of exploration, the quest for hidden treasures in the labyrinth of information. As a data scientist, every dataset acquired is a doorway to a new realm, a glimpse into the unknown. It's about embarking on a journey of discovery, fueled by curiosity and guided by intuition. Data acquisition isn't just about gathering numbers; it's about capturing moments, capturing essence, capturing the very heartbeat of the world pulsating through data streams. It's about forging connections, weaving together the threads of disparate datasets to create a tapestry of understanding.

When we tackled data acquisition at our company, it went something like this: Spotting the Right Data: Kicked off by hunting for the best data sources that could fuel our analyses. Smart Collection Tactics: Rolled out clever ways to gather data, using some neat tech to keep things efficient. Fitting the Pieces Together: Once we had our data, we pieced it together, making sure it was ready to work its magic. Quality Check: We didn't just take data at face value; we dug in to ensure it was spot-on and filled in any blanks. Fixing the Flaws: Ran into some data snags? No sweat. We ironed them out pronto to keep our data top-notch.

Data Acquisition is directly propotional to creativity of a data scientist while building for features in his/her model. I remember predicting sales of a retail store, and a simple addition of holiday data from different regions, Stock price of SPY500 Index from a third party data source, not only improved accuracy, but gave more controls to owners to take more intelligent decision as they had more information. In a closed atmosphere you rely on the data architect to provide you more data if possible, which is most of the time frustrating, because of limitations. But as a rule of thumb, more column adds to more inferential strength of the model, whereas more rows add to accuracy.

Dive deep into the data to understand its structure, patterns, and relationships. Use exploratory data analysis (EDA) techniques to uncover insights and identify potential issues or anomalies.

Data exploration is the voyage of self-discovery, the odyssey of unraveling mysteries concealed within the layers of data. As a data scientist, each exploration is a pilgrimage into the depths of uncertainty, a pilgrimage fueled by courage and illuminated by insight. It's about embracing the chaos, embracing the ambiguity, and finding beauty in the patterns that emerge from the chaos. Data exploration is not just about crunching numbers; it's about listening to the whispers of data, deciphering their secrets, and painting a portrait of reality with strokes of statistical significance

We sometimes tend to get mechanical when it comes to data exploration and analysis which is a good start but hides the real truth from us. The same piece of info can have completely conflicting analyses so thinking like a statistician seems to be quite important. One great book to learn this is The Art of Statistics by David Spiegelhalter

Diving into data exploration, here’s how we went about it: Crunching the Numbers: Started with the basics – mean, median, mode – to get a feel for our data’s pulse. Visual Treats: Plotted everything from histograms to scatter plots. A picture’s worth a thousand data points! Spotting Connections: Used correlation matrices to uncover any secret handshakes between our variables. Odd One Out: Kept an eye out for outliers. They can tell you a lot or lead you astray. Filling the Gaps: Missing data? We had strategies in place to tackle that. Asking the Right Questions: All this exploration wasn’t just for fun. It guided our analysis, shaping our questions and hypotheses.

Data exploration is akin to embarking on a thrilling adventure. It involves uncovering hidden patterns, trends, and insights within the data landscape. According to a report by Harvard Business Review, organizations that prioritize data exploration witness a 10% increase in revenue growth compared to their counterparts. This highlights the transformative impact of effective data exploration techniques in driving business outcomes. Source: Harvard Business Review - "The Power of Data Exploration."

Data Preprocessing is a beautiful art and can increase your accuracy, but in most cases I see everyone applying standard scalers, box-cox transformations and tukkey ladders. All of it sounds so fancy, but if you ask a seasoned quant analyst, there are proofs that these are responsible for overfitting of your data. Some commonly observed wrong example even on platform like Kaggle 1. Predicting stock prices (post applying fancy preprocessing), predict the growth rate instead 2. Calculating quantile values of entire data, instead do a quartely running window, entire data computation leads to model leakage 3. Using ID variables in the model, instead stick to categorical and numeric variables

Data preprocessing is the art of purification, the alchemy of transforming raw data into refined knowledge. As a data scientist, every preprocessing step is a ritual of purification, a ritual that demands meticulous care and unwavering attention. It's about sifting through the noise, sifting through the clutter, and distilling the essence of truth from the cacophony of data. Data preprocessing is not just about cleaning data; it's about cleansing the mind, purging biases, and preparing oneself to embrace the truth that lies beneath the surface.

Knowing the Data functionally helps in deciding what to clean and strike balance between removing errors and keeping valuable information. Understanding scaling and encoding and how these affect the models. Feature selection and Feature creation can also help in removing irrelevant features and combine existing to enhance insights.

Data pre-processing is the most creative part of the data workflow, where different approaches can be applied to solve a problem. Strategy and focus on the problem are essential, but it is the space where experimentation occurs.

When prepping our data for the big game, here's our playbook: Cleanup Crew: Started with scrubbing the data clean - out with the duplicates, typos, and inconsistencies. Scale It Down: Got everything on a common scale. No feature outmuscling another. Encode the Message: Translated categorical data into a language our models could understand. Filling in the Blanks: Missing data didn’t stand a chance. Whether imputing or inferring, we made sure there were no gaps. Feature Spotlight: Crafted new features and tweaked the old ones to shine the spotlight on useful insights. Dataset Makeover: Sometimes, we had to rethink our datasets entirely, crafting them to fit our analysis snugly.

Data analysis is the symphony of understanding, the harmonious orchestration of insights derived from data. As a data scientist, every analysis is a performance, a performance that requires mastery of both technique and intuition. It's about diving deep into the ocean of data, navigating through its currents, and surfacing with pearls of wisdom glistening in the light of understanding. Data analysis is not just about crunching numbers; it's about telling stories, stories that resonate with meaning, stories that illuminate the path to enlightenment.

Diving into the world of data, here's how we make sense of the numbers: Toolbox Time: Armed with stats, machine learning, and a bit of NLP magic, we're ready to tackle the data beast. Question Quest: Every piece of data tells a story. Our job? To decode it. From testing hypotheses to answering the big and small questions. Pattern Patrol: On the lookout for patterns and trends, we're like detectives in a sea of numbers, hunting for clues. Model Match-Up: It's a showdown between models and algorithms. We pit them against each other, analyzing their moves to crown the champ that best fits our puzzle. Insight Invasion: At the end of the day, it's all about those golden nuggets of insight that help us light the way forward.

Effectively communicate your findings and insights to stakeholders through visualization, storytelling, and presentations. Translate complex technical concepts into clear and actionable recommendations.

Data communication is a crucial factor in the whole data science life cycle. Let's say you have built an awesome model with all the optimization techniques. But are you able to convey the results to your stakeholders? And what if I tell you that stakeholders can be from sales team/marketing team/product team/communications team - All looking at the same data from their own perspectives. Hence, storytelling becomes extremely important in these scenario. Your efforts should give tangible results to the stakeholders so that they can improve or build business around it (or take decisions around it). If not communicated properly, your efforts can go unnoticed and teams might not realize the return on Investment of your work.

Data communication is the art of storytelling, the eloquence of conveying insights in a language understood by all. As a data scientist, every communication is a narrative, a narrative that transcends the boundaries of data and touches the hearts of those who behold it. It's about crafting a tale that captivates the imagination, a tale that inspires action and drives change. Data communication is not just about presenting findings; it's about igniting the flames of curiosity, sparking conversations, and forging connections that bridge the gap between data and understanding.

If you think building dashboards on Power BI, Tableau or developing a fancy excel dashboard is part of data communication, we might be completely mistaken, they are definately a channel to visualise data, a data communication involves completely different steps. Steps Identify Audience -Semi-technical, Technically retarded or technical wizard Audience Interest - Revenue, Product Quality (Statistical Outputs) or Jazzy Outputs Level of Detail Required - Aggregate Level Summary (Text), Data Visuals, Drill Down Examples (Single Data points) Again with advancement in Gen AI, Text generated output can be automated using simple python prompts

In the grand tapestry of data science, there are myriad threads waiting to be woven into the fabric of knowledge. From ethical considerations to technological innovations, from domain expertise to interdisciplinary collaboration, the journey of a data scientist is a kaleidoscope of experiences, each offering new insights and new horizons to explore. As we navigate this ever-changing landscape, let us embrace the challenges, let us cherish the moments of discovery, and let us always remember that behind every data point lies a story waiting to be told.

One defination for data scientist that still sits in my mind is If you are doctor (no disrespect to our heroes and someone kidnaps you into a cave for 10 years, post that if you come out you still might be able to treat a patient until you know the disease atleast save his/her life, but if you are data scientist after 10 years in cave you will feel like Fred Flintstone in 21st Century, One of the elemental fact of Data Science is upgrading your self with top notch, cutting edge, state of art etc. etc. algorithms that float in the industry. BERT was invented in 2018, 4 years later we have Chat GPT. The same problem that was solved using CBOW, was solved via BERT and now Chat GPT.