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Introduction to Python for Data Science

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P ython is a powerful high level object oriented programming language with a simple syntax. It has many applications but the major ones are web development, software development and data science. Data science is a field where meaningful insights are extracted from data to allow for decision making and planning in businesses. It combines math, statistics, programming, machine learning, artificial intelligence and advanced analytics.  Data Science project life cycle The data science project life cycle consists of various processes which include: data collection, data cleaning, exploratory data analysis, model building and model deployment. Python has multiple libraries which facilitates these processes hence making it suitable for data science. Examples of these libraries are pandas for data analysis, wrangling and cleaning, matplotlib and seaborn for data visualization, tensor flow and scikit-learn for machine learning, keras and pyTorch for deep learning, SciPy and NumPy for mathematic