What advanced data analytics techniques have you mastered?

In my journey with advanced data analytics, I've delved deep into the realm of machine learning. Beyond the basics, I've mastered sophisticated techniques such as deep learning, natural language processing (NLP), and reinforcement learning. These skills enable me to tackle complex problems, from image recognition to language understanding and even autonomous decision-making. Leveraging machine learning, I've unlocked valuable insights and built predictive models that drive innovation and data-driven decision-making in diverse domains.

I'm knowledgeable about various machine learning algorithms such as linear regression, decision trees, random forests, support vector machines, k-nearest neighbors, clustering algorithms like k-means, hierarchical clustering, dimensionality reduction techniques like principal component analysis (PCA), and more.

As a professional data scientist I've used multiple techniques to analyze data and derive insights to propel business forward. Some of them that stand out to me are Expectation Maximization to impute missing data, LightGBM and XGBoost always seem to provide me the best prediction results and are super fast. Convolution neural networks work best for image classification and segmentation tasks, though I've also used them for text sentiment classification.

When it comes to advanced data analytics techniques, I have been actively working in the field of Analytics & Data Science since 2015. Over the years, I have gained expertise in a wide range of tools such as Power BI, Tableau, various databases like MongoDB, Cassandra, Druid, Snowflake, as well as technologies like Spark/PySpark, Kafka, Datalakes/Datawarehouses, Cloud (GCP, Azure, AWS). Moreover, I am well-versed in various machine learning, deep learning, nlp and computer vision algorithms as well.

Machine Learning is subset of Artificial Intelligence and in which algorithms are trained on data sets to perform specific routine / tasks. Additionally, for Machine Learning you need good set of data on which you can train your model / algorithm. For algorithm to work properly, data should be versatile and unbiased.

In order to get good insights and use them to drive better decisions, we have to go beyond data collection and analysis. We need to look more into data interpretation. Let's not confuse this with sentiment analysis. Data interpretation gets to the root of the "why" behind the sentiment, the reviews, the comments etc. It implies understanding the culture, behaviors, drives and socio-economic norms of the humans behind the text.

I understand concepts related to NLP, including tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, text classification, machine translation, and topic modeling.

I had the opportunity to build a Transformer model that utilized Multi-Head Attention from scratch to translate of text from German to English and vice versa.

In my experience, I have worked with state-of-the-art techniques like BERT, that leverage the power of the Multi-Head Attention mechanism of transformer, for text categorization. I am well-versed in applying techniques like RNN, LSTM, etc. that are specially designed to work with sequential data using Python. I have also spent numerous hours doing text processing by applying methods like tokenization, text embedding, part-of-speech tagging, etc.

Text analytics, a critical facet of advanced data analysis, involves extracting insights from textual data like documents, emails, reviews, and social media posts. It aids in understanding meaning, sentiment, tone, and topics within text, enabling actionable insights. For instance, it's used in customer feedback analysis, trend identification, brand reputation monitoring, and text summarization. To master text analytics, one must grasp text data preprocessing, tokenization, and vectorization, as well as employ techniques like natural language processing, sentiment analysis, and topic modeling. Proficiency with tools such as NLTK, spaCy, or Gensim is crucial for effective text analytics in data analysis.

Knowing how to evaluate and interpret the results is what sets you apart from others in being successful with data mining. You need to understand the domain you are working in and what business problems you are trying to solve. It is one thing to find an anomaly in your data but when you truly understand the data you are able to bring action to your insights.

Data mining, a cornerstone of advanced data analytics, entails uncovering concealed patterns, associations, or anomalies within extensive datasets. This process aids in data exploration, relationship identification, and the revelation of non-obvious insights. Applications range from discovering customer segments to identifying frequent itemsets and detecting outliers. To master data mining, one must become adept in techniques like clustering, association rules, and anomaly detection, while possessing the ability to assess and interpret outcomes effectively. Proficiency in data mining tools like WEKA, RapidMiner, or KNIME is instrumental in executing efficient data mining tasks.

Leveraging tools like Apriori algorithm and software such as RapidMiner, we conducted association rule mining on transaction data. By setting support and confidence thresholds, we identified itemsets with significant co-occurrence patterns. For example, we uncovered that dresses and matching accessories like scarves and handbags had a high lift value, indicating a strong association. This data driven approach allowed us to strategically create bundles and recommendation engines within our e-commerce platform, effectively increasing cross-selling and upselling opportunities.

Data mining helps uncover insights, relationships, and trends that can be crucial for making informed decisions. It's not just about gathering data; it's about finding the gold within it. Techniques like clustering, association rule mining, and anomaly detection are the tools of the trade in this digital treasure hunt. So, if you've got data, I'll help you mine it for all it's worth ????

I perform EDA by using Python because it is a versatile programming tool and I am very satisfied by the possibilities that it offers. You can use statistical techniques, data visualization and other techniques to make a good and efficient EDA.

When crafting visuals to communicate your intended message to your audience, its necessary to reduce the cognitive load needed to process the message out of the visual. Here's an idea ??Apply Gestalt Principles for Data Visualization. This includes: observing closeness of the visuals, resemblance, continuity, enclosure, connection and closure. Gestalt Principles will help you think like a designer.

At Amazon, we try to "work backwards" in everything that we do. The same applies to data storytelling - start with the end. What decisions does the user need to make? What data points are actually meaningful to support this decision making? If you can spend some time 'in the shoes' of the end user, you'll be much more effective with your storytelling. The "hard skills" are the easy part!

One of the skills that sets me apart from the crowd is learning data storytelling. Since I embarked on my entrepreneurial journey two years ago, I have opened my eyes to measuring what matters and tracking progress using relevant data. The question is whether it's vanity metrics or actionable metrics. Here's my step-by-step guide to implementing a data storytelling framework: 1. Who will be listening when I present this data? 2. Which metrics are most important from their perspective? 3. How does it compare to the previous period? Without comparison data, it doesn't have meaning. 4. What can we learn from this data? What actions should we take? If we can't find any actionable insights, then the data is useless.

Data storytelling involves communicating insights in a compelling way. It's both art and science. You need to structure your story, choose visualizations wisely, craft an engaging narrative, and tailor the message to your audience. The goal is influencing decisions, persuading action, or educating others. For example, present findings, recommendations, or solutions to stakeholders. To master data storytelling, leverage tools like Tableau, PowerBI, Data Studio to create and share stories. But also hone soft skills like audience analysis and narrative development. Data alone isn't convincing - context and connection drive impact. Crafting compelling data stories takes practice.

One thing that I found helpful is that these visualization tools helps you visually explain Big data and is the most effective way of communicating with the non technical stakeholders.

In the world of data, it's easy to think that if something is statistically correct, it must be right or ethical. However, that's not always the case. Statistics can be manipulated or taken out of context to support a certain viewpoint. Also, even if data shows a pattern or trend, acting on it can sometimes reinforce stereotypes or create unfair situations. Being data-driven is good, but it's crucial to also consider ethics, fairness, and the bigger picture. Just because you can do something backed by data doesn't mean you should. Always blend statistical insights with ethical considerations to make balanced decisions.

Bias testing: It is not enough to test bias in performance metrices and compare the protected group scores with control group score. You also need to analyze if the resulted bias have statistical significance. This is very important as you just may end up training again and again when the score differences result from data distribution. So check target distribution too.

Moreover, embracing data ethics goes beyond mere compliance; it's about fostering trust. In an age where data is constantly collected and analyzed, users want to ensure that their information is not only protected but also used responsibly. Building an ethical data culture means being transparent about data collection, storage, and usage practices, while actively seeking feedback from stakeholders. It is about creating an environment where data-driven decisions respect individual rights. Therefore, businesses and organizations that prioritize data ethics not only adhere to regulations but also cultivate stronger relationships with their users and stakeholders, ensuring long-term success and credibility in the digital age.

Data ethics involves the responsible, ethical use of data - it's critical.Knowing legal/ethical frameworks like GDPR, HIPAA, CCPA and apply them to projects. This helps protect privacy, security, rights. It ensures quality, accuracy, fairness in analysis. Mitigate risks and impacts of misuse. Compliance isn't enough - truly consider subjects. For example, address algorithmic bias that could disproportionately impact marginalized groups. Data is powerful - it can help but also harm. We must be thoughtful in how we collect, analyze, store, share data. Consider consent, transparency, accountability. Data ethics requires examining our assumptions and blindspots. It's ongoing work, but foundational. Ethics is not just compliance - it's our duty.

Most important thing that I need for data analytics that is looking problems from different side and believe this quote "everything thingsis possible" and "everything happenswithsome reason"

Unleash Python's data prowess! ???? Pandas wrangles data, NumPy computes, Matplotlib & Seaborn visualize trends ??, Scipy tackles science, Scikit-learn masters ML ??, Jupyter weaves code and insights ??. Plotly wows with interactivity ??, SQLAlchemy handles databases ???, Beautiful Soup scrapes the web ???. NLTK conquers text ??, TensorFlow & PyTorch dive deep ??. Python's toolkit rocks diverse data realms! ???? #PythonDataMagic #AnalyticsAdventure

Additional concepts I have mastered: Time Series Analysis: I can help with forecasting techniques, such as autoregressive integrated moving averages (ARIMA), exponential smoothing methods, and more advanced methods like seasonal decomposition of time series (STL) and state space models. Deep Learning: I'm familiar with neural networks, convolutional neural networks (CNNs) for image analysis, recurrent neural networks (RNNs) for sequence data, and more advanced architectures like Long Short-Term Memory (LSTM) and Transformers. Anomaly Detection: I can provide guidance on anomaly detection techniques such as statistical methods, clustering-based approaches, and machine learning-based methods for identifying outliers in datasets.

This may be a contrarian take, but "mastering" may be the wrong word. Even with my experience publishing analyses, delivering product analytics that shaped business decisions, and putting ML models in production, I still don't feel like I've mastered these skills. Proficient and able to drive value? Yes! Mastered? No. I hold this belief because the more I learn about statistics, the more I am humbled. Every time I learn about regression, I learn something new or see something from a completely different perspective. Also, every ML use case is so context-dependent that you will be learning once again. Thus, I argue to master learning rather than master specific analytics skills. That way, you can learn to solve any problem thrown your way.

For a comprehensive skill set in advanced analytics, emphasize Data Governance to manage data's lifecycle, ensuring quality, privacy, and compliance. Domain Expertise and Business Acumen are critical for interpreting data within industry contexts, aligning analytics with business objectives. Soft Skills enhance communication with stakeholders, making complex insights accessible. Ethical AI practices ensure responsible data science. The cornerstone of success in analytics lies in continuously aligning solutions with the evolving needs of business partners or end users, ensuring analytics work remains relevant and delivers tangible value.

I think there are a couple of skills which are important and they are not technical in nature 1. Ability to quickly immerse and understand the data in question to understand the business domain and processes 2. Being able to distill and really understand the problem that needs to be resolved 3. Running a data project using a design thinking methodology