Internal talent recommendation is a critical strategy for organizational continuity, yet conventional approaches suffer from structural limitations, often overlooking qualified candidates by relying on the narrow perspective of a few managers. To address this challenge, we propose a novel framework that models two distinct dimensions of an employee's position fit from email data: WHAT they do (semantic similarity of tasks) and HOW they work (structural characteristics of their interactions and collaborations). These dimensions are represented as independent graphs and adaptively fused using a Dual Graph Convolutional Network (GCN) with a gating mechanism. Experiments show that our proposed gating-based fusion model significantly outperforms other fusion strategies and a heuristic baseline, achieving a top performance of 40.9% on Hit@100. Importantly, it is worth noting that the model demonstrates high interpretability by learning distinct, context-aware fusion strategies for different job families. For example, it learned to prioritize relational (HOW) data for 'sales and marketing' job families while applying a balanced approach for 'research' job families. This research offers a quantitative and comprehensive framework for internal talent discovery, minimizing the risk of candidate omission inherent in traditional methods. Its primary contribution lies in its ability to empirically determine the optimal fusion ratio between task alignment (WHAT) and collaborative patterns (HOW), which is required for employees to succeed in the new positions, thereby offering important practical implications.

This class can be summarized in one sentence, "to learn how to put your machine learning ideas into your customer's plate". Here we will extend multiple Python machine learning ideas into fully interactive web applications, into a format that anybody anywhere can access as long as they have access to a web browser. Our last project will be built around a professional paywall infrastructure so you can control and monetize how and whom can access it. Whether you want to test out business ideas or share advanced and predictive analytics ideas with the world, the tools taught in this class will allow you to do that quickly, easily and without spending a lot of money.

If it is your goal is to become a Data Scientist, you have to first understand what it takes to become one, the skills and competencies that you should learn. Data Science is an amazingly interesting field, full of interesting concepts and power to create magic from Data. Comprehensive knowledge on Deep Learning, ML-Ops and AI/ML Product Development are critical knowledge areas for any Data Scientist/Data Engineer/Machine-Learning Professional. This course places a lot of focus into these areas so that there is no learning gap when you start on a Data Science/Machine Learning role. The curriculum prepares you to be a leader in this field through mastery of core data science concepts like Statistical Analysis of Data, Exploratory Data Analysis Techniques using Python, powerful Visualizations, Machine Learning, Deep Learning and Model Deployment in Production.

This Advanced Certification in Applied Data Science, Machine Learning & IoT is intricately designed for graduates and working professionals by E&ICT Academy, IIT Guwahati. It includes world-class instruction for applied data science and machine learning regimes. Live sessions and the highly curated modules will give students real-life exposure and learning applications. The different modules cover major concepts like Statistical Thinking, Data Analysis & Visualisation, ... Artificial Intelligence & Machine Learning, Python, Tensor Flow, Neural Networks & Deep Learning, Speech Recognition, Internet of Things in a healthy atmosphere. It will help them to try out the applied concept for solving real-time issues.

INDIA India is the second-most noteworthy nation to enlist workers in the field of data science or information investigation, and so forth with 50,000 positions accessible – second just to the United States. The interest for data specialists is similarly serious, regardless of whether you take a gander at the huge organizations, the online business industry, or even new companies. In this manner, on the off chance that you have the necessary range of abilities and are prepared to keep yourself refreshed, your profession as a Data Scientist is required to continue to develop onwards and upwards. This line stands genuine particularly when we think about that as an information researcher's compensation in India is straightforwardly or by implication subject to how upskilled and refreshed, they are. The normal data scientist compensation is ₹698,412.

The B2B Insights & Analytics team exists to drive optimal decision-making that improves our business customers' FF&E shopping experience through Sales interactions. We seek to optimize Sales incrementality by identifying the most CLV-accretive Sales interactions with customers, as a critical driver of our flywheel. The data we are experts in represents the voice of external customers (e.g. This team leads measurement - particularly around causal inference - and also plays a data challenger role to ensure the STO understands the "why" behind great customer experiences. And we partner closely with Data Science - to jointly develop and apply machine learning approaches to drive growth, and Data Solutions/Engineering - to ensure we have a strong, and high-velocity data foundation.

Data science, artificial intelligence, and machine learning – from theories and nascent beginnings, these fields have grown to become extremely important not just to IT, e-commerce, and entertainment, but also financial services, pharmaceuticals, disease prevention and public health services as well diagnostic tools. The opportunities are immense, and you only need to equip yourself for them. Here are four courses that can give you the necessary skills to lead businesses in the 21st century. All of them include Python programming as a course component. Most of them require an undergraduate knowledge of statistics, calculus, linear algebra, and probability, so we recommend checking your course of interest for the specifics.

DSFM (Data Science for Managers) offers either a 2-day (fast) track for Data Science or a 5-Day Technical Boot Camp (including the same Executive information plus technical deep dive).

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.