Courses

Explore the journey from robotics to recommender systems in this insightful podcast episode featuring Miguel Fierro, Principal Data Science Manager at Microsoft. Delve into the limitations of applying machine learning in robotics, the integration of computer vision and AI in sports analytics, and the evolution of recommender systems. Learn about the importance of choosing simpler solutions over complex ML models, the role of embeddings and feature stores in modern AI applications, and strategies for demonstrating ROI to leadership. Gain valuable insights on high-impact AI investments and the potential of Large Language Models in recommender systems. Perfect for data scientists, AI enthusiasts, and business leaders looking to understand the practical applications and challenges of AI across various domains.

Learn to hire, foster, and manage data science teams that produce deeper insights and more effective reports and visualizations.

Learn how to manage your manager. In this course, adapted from the podcast How to Be Awesome at Your Job, Mary Abbajay explains how to build a good relationship with your boss.

Learn how to create and manage apps with SCCM and prepare for Microsoft certification exam 70-703. See how to deploy apps using PowerShell scripts, and deploy App-V virtual apps.

This Data Product Manager Nanodegree program highlights the true power of data to shape and optimize product strategies. From building data pipelines to analyzing key product metrics, you'll learn how to drive product innovation and success.

Explore a comprehensive framework for leading data science teams effectively in this 46-minute video presentation. Learn how to address key challenges faced by data science managers and senior leaders, including project execution, data and algorithm misuse prevention, and result validation. Dive into essential aspects of the framework, such as forming data science teams, establishing processes for developing analytical solutions, and implementing risk management strategies. Gain insights into team roles, project management methodologies like Scrum, CRISP-DM, and Kanban, and ethical considerations in data science. Discover techniques for ensuring data quality, managing analytics workflows, and maintaining production robustness. Enhance your ability to guide data science projects successfully and efficiently with practical examples and key questions to consider throughout the development process.

Data scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s. This MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam. General Track This track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries. Explore the General track here Methods Track This track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries. Explore the Methods track here Social Sciences Track This track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences. You are currently exploring the Social Sciences track Time Series and Social Sciences Track This track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation. Explore the Time Series and Social Sciences track here

Great leadership doesn't happen by chance—it happens by design. This course helps you create and evolve a personalized leadership checklist that keeps you focused on what matters most. You'll learn the core components of an effective checklist, from daily practices to strategic responsibilities, and how to structure them for clarity and action. Through practical tools, case examples, and hands-on activities, you'll build a Version 1 of your leadership checklist, test it against real challenges, and refine it as your role evolves.

In the Data Driven Manager specialization, you will learn how to first understand the type of data that you have (or want to generate), then describe it with numbers and graphs to communicate with your audience. You will practice using probability and distributions to understand the underlying nature of your data to make decisions and solve problems in a way that increases the likelihood of a desired outcome. You will learn the steps to create a plan to answer business and engineering questions and reduce risk when making decisions. You’ll study how to determine best- and worst-case scenarios using data. Finally, you’ll acquire data analysis skills to answer business and engineering questions that will help you make appropriate decisions. The courses in this specialization can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder.

Looking for a consistent and reliable way to install Windows applications? Learn about the Chocolatey package manager.

Learn how to effectively manage data science projects in this 18-minute video tutorial. Discover a 5-step project management framework specifically designed for data science and understand the crucial role of a project manager in its implementation. Explore the importance of project management in data science, delve into the details of the framework, and examine a real-world case study involving a YouTube video search tool. Gain valuable insights into full stack data science project management and prepare for future challenges in the field.

Learn the basics and principles of data and machine learning for scientific problems What you'll learn: Understand the concepts of data science and machine learning and how they can be used in scienceKnow the main algorithms used in tasks of classification, regression, and clusteringKnow the main architectures of neural networksUnderstand how you can use algorithms/analyses in science projects/investigations/studies The course "Principles of Data Science and Machine Learning for Natural Sciences" is designed to connect traditional scientific disciplines with the rapidly growing fields of Data Science (DS) and Machine Learning (ML). As research increasingly depends on large datasets and advanced computational methods, it’s becoming essential for scientists to know how to leverage DS and ML techniques to improve their work.This course offers a solid introduction to the key concepts of Data Science and Machine Learning, specifically aimed at scientists and researchers in areas like biology, chemistry, physics, and environmental science. Participants will learn the basics of data analysis, including data collection, cleaning, and visualization, before moving on to machine learning algorithms that can help identify patterns and make predictions from data.The course doesn’t require any programming skills and focuses on fundamental theoretical concepts. It's structured into six main sections:1. Introduction We'll start by introducing the course, covering its main features, content, and how to follow along.2. Core DS/ML Concepts We’ll go over basic concepts like variables, data scaling, training, datasets, and data visualization.3. Classification In this section, we’ll discuss key classification algorithms such as decision trees, random forests, Naive Bayes, and KNN, with examples of how they can be applied in scientific research.4. Regression We’ll briefly cover linear and multiple linear regression, discussing the main ideas and providing examples relevant to science.5. Clustering This section will focus on standard and hierarchical clustering methods, along with practical examples for scientific applications.6. Neural Networks Finally, we’ll introduce neural networks, discussing their biological inspiration and common architectures like Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Hopfield Networks.

Learn how to handle date and time effectively in C# applications. Use DateOnly, TimeOnly, and DateTime classes to represent dates and times. Perform time zone conversions and interval calculations using DateTimeOffset, TimeZoneInfo, and TimeSpan. Retrieve and manipulate day of the week information using DayOfWeek. Handle culture-specific data using CultureInfo. Perform calendar-related operations using Calendar and define rules for determining the first week of the year using CalendarWeekRule. Learn, to use C# tools for managing groups of objects dynamically, ensuring type safety and efficient data manipulation. Apply C# Collections for Efficient Data Management Manage ordered collections using List<T> in C# Manage unique collections with HashSet<T> Using Dictionary<TKey, TValue> for efficient key-value pair management in C# Learn to create organized, maintainable code with enum, struct, and record types in C#. Use enums in C# to define named constants and prevent invalid values. Work with structs in C# to encapsulate related data into lightweight containers. Create records in C# to model immutable data and ensure consistency. Get started with generic and anonymous types in C#. Implement generic classes and methods to handle various data types efficiently. Utilize advanced generics features like generic interfaces, covariance, contravariance, and generic math to address complex scenarios. Apply anonymous types to create temporary, lightweight data structures for short-term use. Explore use cases for anonymous types and tuples.

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The HarvardX Data Science program prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The program covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio. In each course, we use motivating case studies, ask specific questions, and learn by answering these through data analysis. Case studies include: Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems. Throughout the program, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem.

Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

Excel in Data Science, one of the hottest fields in tech today. Learn how to gain new insights from big data by asking the right questions, manipulating data sets and visualizing your findings in compelling ways. In this MicroMasters program, you will develop a well-rounded understanding of the mathematical and computational tools that form the basis of data science and how to use those tools to make data-driven business recommendations. This MicroMasters program encompasses two sides of data science learning: the mathematical and the applied. Mathematical courses cover probability, statistics, and machine learning. The applied courses cover the use of specific toolkit and languages such as Python, Numpy, Matplotlib, pandas and Scipy, the Jupyter notebook environment and Apache Spark to delve into real world data. You will learn how to collect, clean and analyse big data using popular open source software will allow you to perform large-scale data analysis and present your findings in a convincing, visual way. When combined with expertise in a particular type of business, it will make you a highly desirable employee.

Dive into the world of data science through a comprehensive tutorial series covering essential topics like Pandas, regression analysis, and TensorFlow implementation in Python. Explore practical applications using Kaggle datasets, including wine data analysis, while gaining hands-on experience with powerful machine learning tools and techniques.

Learn and master the Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science What you'll learn: Learn Complete Data Science skillset required to be a Data Scientist with all the advance conceptsMaster Python Programming from Basics to advance as required for Data Science and Machine LearningLearn complete Mathematics of Linear Algebra, Calculus, Vectors, Matrices for Data Science and Machine Learning.Become an expert in Statistics including Descriptive and Inferential Statistics.Learn how to analyse the data using data visualization with all the necessary charts and plotsPerform data Processing using Pandas and ScikitLearnMaster Regression with all its parameters and assumptionsSolve a Kaggle project and see how to achieve top 1 percentileLearn various classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector MachinesGet complete understanding of deep learning using Keras and TensorflowBecome the Pro by learning Feature Selection and Dimensionality Reduction Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more? Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.We are going to execute following real-life projects,Kaggle Bike Demand Prediction from Kaggle competitionAutomation of the Loan Approval processThe famous IRIS ClassificationAdult Income Predictions from US Census DatasetBank Telemarketing PredictionsBreast Cancer PredictionsPredict Diabetes using Prima Indians Diabetes DatasetToday Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others. As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning? Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,Understanding of the overall landscape of Data Science and Machine LearningDifferent types of Data Analytics, Data Architecture, Deployment characteristics of Data Science and Machine Learning projectsPython Programming skills which is the most popular language for Data Science and Machine Learning Mathematics for Machine Learning including Linear Algebra, Calculus and how it is applied in Machine Learning Algorithms as well as Data ScienceStatistics and Statistical Analysis for Data ScienceData Visualization for Data Science Data processing and manipulation before applying Machine LearningMachine LearningRidge (L2), Lasso (L1) and Elasticnet Regression/ Regularization for Machine Learning Feature Selection and Dimensionality Reduction for Machine Learning modelsMachine Learning Model Selection using Cross Validation and Hyperparameter TuningCluster Analysis for unsupervised Machine LearningDeep Learning using most popular tools and technologies of today.This Data Science and Machine Learning course has been designed considering all of the above aspects, the true Data Science and Machine Learning A-Z Course. In many Data Science and Machine Learning courses, algorithms are taught without teaching Python or such programming language. However, it is very important to understand the construct of the language in order to implement any discipline including Data Science and Machine Learning. Also, without understanding the Mathematics and Statistics it's impossible to understand how some of the Data Science and Machine Learning algorithms and techniques work. Data Science and Machine Learning is a complex set of topics which are interlinked. However, we firmly believe in what Einstein once said,"If you can not explain it simply, you have not understood it enough."As an instructor, I always try my level best to live up to this principle. This is one comprehensive course on Data Science and Machine Learning that teaches you everything required to learn Data Science and Machine Learning using the simplest examples with great depth.As you will see from the preview lectures, some of the most complex topics are explained in a simple language. Some of the key skills you will learn,Python ProgrammingPython has been ranked as the #1 language for Data Science and Machine Learning. It is easy to use and is rich with various libraries and functions required for performing various tasks for Data Science and Machine Learning. Moreover, it is the most preferred and default language of use for many Deep Learning frameworks including Tensorflow and Keras. Advance Mathematics for Machine LearningMathematics is the very basis for Data Science in general and Machine Learning in particular. Without understanding the meanings of Vectors, Matrices, their operations as well as understanding Calculus, it is not possible to understand the foundation of the Data Science and Machine Learning. Gradient Descent which forms the very basis of Neural Network and Machine Learning is built upon the basics of Calculus and Derivatives. Advance Statistics for Data ScienceIt is not enough to know only mean, median, mode etc. The advance techniques of Data Science and Machine Learning such as Feature Selection, Dimensionality Reduction using PCA are all based on advance inferential statistics of Distributions and Statistical Significance. It also helps us understanding the data behavior and then apply an appropriate machine learning technique to get the best result from various techniques of Data Science and Machine Learning. Data VisualizationAs they say, picture is worth a thousand words. Data Visualization is one of the key techniques of Data Science and Machine Learning and is used for Exploratory Data Analysis. In that, we visually analyse the data to identify the patterns and trends. We are going to learn how to create various plots and charts as well as how to analyse them for all the practical purposes. Feature Selection plays a key role in Machine Learning and Data Visualisation is key for it. Data ProcessingData Science require extensive data processing. Data Science and Machine Learning practitioners spend more than 2/3rd of the time processing and analysing the data. Data can be noisy and is never in the best shape and form. Data Processing is one of the key disciplines of Data Science and Machine Learning to get the best results. We will be using Pandas which is the most popular library for data processing in Python and various other libraries to read, analyse, process and clean the data. Machine LearningThe heart and soul of Data Science is the predictive ability provided by the algorithms from Machine Learning and Deep Learning. Machine Learning takes the overall discipline of Data Science ahead of others. We will combine everything we would learn from the previous sections and build various machine learning models. The key aspects of the Machine Learning is not just about the algorithms but also understanding various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values impact the outcome so that you can build the best machine learning models. Feature Selection and Dimensionality ReductionIn case you wonder, what makes a good data scientists, then this section is the answer. A good Data Science and Machine Learning practitioner does not just use libraries and code few lines. She will analyse every feature of the data objectively and choose the most relevant ones based on statistical analysis. We will learn how to reduce the number of features as well as how we can retain the value in the data when we practice and build various machine learning models after applying the principles of Feature Selection and Dimensionality Reduction using PCA. Deep LearningYou can not become a good Data Science and Machine Learning practitioner, if you do not know how to build powerful neural network. Deep Learning can be said to be another kind of Machine Learning with great power and flexibility. After Learning Machine Learning, we are going to learn some key fundamentals of Deep Learning and build a solid foundation first. We will then use Keras and Tensorflow which are the most popular Deep Learning frameworks in the world. Kaggle ProjectAs an aspiring Data Scientists, we always wish to work on Kaggle project for Machine Learning and achieve good results. I have spent huge effort and time in making sure you understand the overall process of performing a real Data Science and Machine Learning project. This is going to be a good Machine Learning challenge for you. Your takeaway from this course,Complete hands-on experience with huge number of Data Science and Machine Learning projects and exercisesLearn the advance techniques used in the Data Science and Machine LearningCertificate of Completion for the most in demand skill of Data Science and Machine Learning All the queries answered in shortest possible time. All future updates based on updates to libraries, packagesContinuous enhancements and addition of future Machine Learning course materialAll the knowledge of Data Science and Machine Learning at fraction of costThis Data Science and Machine Learning course comes with the Udemy's 30-Day-Money-Back Guarantee with no questions asked. So what you are waiting for? Hit the "Buy Now" button and get started on your Data Science and Machine Learning journey without spending much time.I am so eager to see you inside the course. Disclaimer: All the images used in this course are either created or purchased/downloaded under the license from the provider, mostly from Shutterstock or Pixabay.

This course focuses on understanding why managers are crucial to team success and the positive impact they can have. It covers recognizing both effective and ineffective management behaviors and highlights the unique value managers bring to their teams.

The volume of data generated daily is staggering—more than 2.5 quintillion bytes every day. As the data surge continues to grow exponentially, organizations and individuals alike need to understand how to process and analyze this information to create strategic advantage. The CS50 Professional Certificate Program: Computer Science for Data Science explores the limitless potential of computer science converging with the analytical power of R programming. Beginning with CS50: Introduction to Computer Science, learners will complete an intensive and comprehensive dive into the core concepts of computer science developed by renowned Harvard University Professor David J. Malan. The course will cover concepts like abstraction, algorithms, and data structures and management—serving as a foundation for how data is used to improve decision-making and critical thinking skills. Through CS50’s Introduction to Programming with R, you will elevate your skills as you discover the statistical power of R using real-world datasets to manipulate data, create colorful visualizations, and package and export R code for reproducibility. Whether you're a data enthusiast, a seasoned computing professional, or interested in entering the fastest-growing industry, this professional certificate program unravels the complexities of today’s data landscape, equipping you with the skills needed to create efficient, accurate, and actionable data insights.