Global Certificate in Industry-Standard Data Science Tools
-- ViewingNowThe Global Certificate in Industry-Standard Data Science Tools is a comprehensive course designed to equip learners with essential data science skills in high demand by employers worldwide. This program covers a range of industry-standard tools such as Python, R, SQL, and Tableau, providing a strong foundation in data manipulation, analysis, and visualization.
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⢠Data Wrangling and Preprocessing: Introduction to data cleaning, data munging, and data preprocessing using Python and R. This unit will cover essential libraries such as Pandas, Numpy, and Tidyverse.
⢠Data Visualization: Explore data visualization tools and libraries in Python and R, including Matplotlib, Seaborn, Plotly, and Ggplot2. This unit will also cover best practices in data visualization and storytelling.
⢠Statistical Analysis: Understanding descriptive and inferential statistics, probability distributions, and statistical testing. This unit will cover hypothesis testing, p-values, and confidence intervals.
⢠Machine Learning Algorithms: Overview of machine learning algorithms, including regression, classification, clustering, and dimensionality reduction. This unit will also cover model evaluation metrics such as accuracy, precision, recall, and F1 score.
⢠Deep Learning and Neural Networks: Introduction to deep learning and neural networks, including backpropagation, activation functions, and optimization algorithms. This unit will cover popular deep learning frameworks such as TensorFlow and PyTorch.
⢠Big Data and Hadoop Ecosystem: Overview of big data technologies, including Hadoop, Spark, Hive, and Pig. This unit will cover distributed computing, parallel processing, and data storage in big data systems.
⢠Cloud Computing and Data Science: Introduction to cloud computing platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). This unit will cover cloud-based data science tools and services, such as AWS SageMaker and GCP AI Platform.
⢠Ethics and Bias in Data Science: Understanding the ethical implications of data science, including data privacy, bias, and fairness. This unit will cover best practices in ethical data science and the role of data scientists in promoting ethical AI.
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