Data Science Math Skills

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“Data Science Math Skills” is a specialized course focused on equipping learners with the mathematical foundation necessary for data science. It covers essential mathematical concepts and techniques used in data analysis, machine learning, and statistical modeling, making it an essential prerequisite for aspiring data scientists.

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About The Course

Data Analysis has been around for a long time. But up until a few years ago, developers practiced it using expensive, closed-source tools like Tableau. But recently, Python, SQL, and other open libraries have changed Data Analysis forever. In the Data Analysis with Python Certification, you’ll learn the fundamentals of data analysis with Python. By the end of this certification, you’ll know how to read data from sources like CSVs and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.

What Will You Learn

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Course Contents

Introduction to Data Analysis

Data analysis is the act of turning raw, messy data into useful insights by cleaning the data up, transforming it, manipulating it, and inspecting it.

Data Manipulation with pandas

Introduction to pandas library and its data structures (DataFrame, Series) Data cleaning and preprocessing techniques Data transformation, filtering, and aggregation Handling missing data and outliers

Statistical Analysis with NumPy and SciPy

Introduction to NumPy and its array-based computing capabilities Statistical functions for data analysis (mean, median, variance, etc.) Hypothesis testing and statistical significance

FAQ

While prior knowledge of data analytics is not always required, having basic mathematical skills and familiarity with computer applications can be beneficial. Some courses are designed for beginners and assume no prior experience.

Yes, most data analytics courses include real-world projects and case studies where you can apply your learned skills to practical scenarios using real datasets.

Yes, responsible data handling and ethical considerations are often covered, highlighting the importance of data privacy and security.

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Data Science Math Skills
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