Universities have been slow at creating specialized data science programs. It is difficult to acquire the skills necessary to be hired as a data scientist. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. It is digital, programming-oriented, and analytical. Microsoft Excel 2003, 2010, 2013, 2016, or 365ĭata scientist is one of the best suited professions to thrive this century.
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We will show you how to do that step by step +Case Study – Analyzing the Predicted Outputs in Tableau +Case Study – Loading the ‘absenteeism_module’ +Case Study – Applying Machine Learning to Create the ‘absenteeism_module’ +Case Study – Preprocessing the ‘Absenteeism_data’ +Appendix: Deep Learning – TensorFlow 1: Business Case +Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset +Appendix: Deep Learning – TensorFlow 1: Introduction +Deep Learning – Classifying on the MNIST Dataset +Deep Learning – Digging into Gradient Descent and Learning Rate Schedules +Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks +Deep Learning – TensorFlow 2.0: Introduction +Deep Learning – How to Build a Neural Network from Scratch with NumPy +Deep Learning – Introduction to Neural Networks +Advanced Statistical Methods – Other Types of Clustering +Advanced Statistical Methods – K-Means Clustering +Advanced Statistical Methods – Cluster Analysis +Advanced Statistical Methods – Logistic Regression +Advanced Statistical Methods – Practical Example: Linear Regression +Advanced Statistical Methods – Linear Regression with sklearn +Advanced Statistical Methods – Multiple Linear Regression with StatsModels +Advanced Statistical Methods – Linear regression with StatsModels +Part 5: Advanced Statistical Methods in Python +Statistics – Practical Example: Hypothesis Testing +Statistics – Practical Example: Inferential Statistics +Statistics – Inferential Statistics: Confidence Intervals +Statistics – Inferential Statistics Fundamentals +Statistics – Practical Example: Descriptive Statistics +Probability – Probability in Other Fields +The Field of Data Science – Debunking Common Misconceptions +The Field of Data Science – Careers in Data Science +The Field of Data Science – Popular Data Science Tools +The Field of Data Science – Popular Data Science Techniques +The Field of Data Science – The Benefits of Each Discipline +The Field of Data Science – Connecting the Data Science Disciplines +The Field of Data Science – The Various Data Science Disciplines Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data Perform linear and logistic regressions in Pythonīe able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learnĪpply your skills to real-life business cases Start coding in Python and learn how to use it for statistical analysis Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!) Impress interviewers by showing an understanding of the data science field The course provides the entire toolbox you need to become a data scientistįill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
The Data Science Course 2019 Complete Data Science Bootcamp