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Janani Ravi, Axel Sirota, and Bismark Adomako

Data science is a diverse field where scientific methods, software programming, and data analytics combine to glean insights from data, communicate those insights, and empower a business to take appropriate actions.This skill path provides foundational knowledge behind data science, specifically with its application in Microsoft Azure.

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Data science is a diverse field where scientific methods, software programming, and data analytics combine to glean insights from data, communicate those insights, and empower a business to take appropriate actions.This skill path provides foundational knowledge behind data science, specifically with its application in Microsoft Azure.

What You'll Learn

  • Coping skills for bad or incomplete data
  • Data shaping and munging
  • Application of basic statistics
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    What's inside

    Eight courses

    Representing, Processing, and Preparing Data

    (2 hours)
    This course covers data processing tools, including spreadsheets, Python, and relational databases, and deals with data quality issues and visualizing data for insight generation.

    Combining and Shaping Data

    (3 hours)
    This course covers both conceptual and practical aspects of pulling together data from different sources, with different schemas and orientations, into a cohesive whole using Excel, Python, and various tools available on the Azure cloud platform.

    Communicating Data Insights

    (2 hours)
    This course covers the key statistical and technical tools needed to convey clear, actionable insights from data to senior executives, including the use of powerful visualizations such as Sankey diagrams, funnel plots, and candlestick plots.

    Interpreting Data with Statistical Models

    (2 hours)
    Data is everywhere, and we often hear about statistics. Over this course, we will shape up our statistical knowledge, going from zero to hero analyzing complex patterns of everyday real-world problems.

    Experimental Design for Data Analysis

    (2 hours)
    This course covers building and evaluating machine learning models, using data judiciously and accounting for biases.

    Summarizing Data and Deducing Probabilities

    (2 hours)
    This course covers the most important aspects of exploratory data analysis using different univariate, bivariate, and multivariate statistics from Excel and Python.

    Interpreting Data with Advanced Statistical Models

    (3 hours)
    Machine Learning is changing the world. With this course, you will know how to create an ML application for problems that appear at your work and understand the basis behind it. You will learn the basics of Machine learning, linear regression, how to classify with Logistic Regression, SVMs, and Bayesian methods, and the intrinsic patterns of data with unsupervised techniques such as K Means and PCA.

    Building, Training, and Validating Models in Microsoft Azure

    (1 hours)
    This course provides a roadmap for Microsoft Azure Data Scientists to build, train, and validate machine learning models in Azure. It covers model selection, data preparation, feature engineering, model training, and evaluation. By building a model to predict flight delays, learners will gain practical experience in applying Azure ML to real-world problems.

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