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Data Science - Interpretation & Visualization

Course Description

Data Science - Interpretation & Visualization for kids is the second more advanced sequence to the first python course. Coding Young has created this curriculum for those who have completed the basic variant. It draws from the existing knowledge that your child has about Python and challenges them to use this knowledge for data analysis through a self-paced learning mode.

Since Python is an extremely popular coding language, it is widely used in the data science field. It is also used in jobs that are not heavily technology based in order to automate data gathering and data analysis. For example, Python can be used by an entrepreneur to analyze the sales data from their marketing campaigns to optimize their advertising budget. Facebook, Amazon, and Google are one of the top employers for data scientists for exactly this reason.

This course will teach your child how to use Python to analyze and interpret data through clustering and estimation. Learners would also require a solid understanding of algebra in order to understand the concepts more clearly. They will be learning how to write scripts that perform data plotting and probability analysis in this course while working with the Python library. This will help collect, process and identify patterns of data in terms of graphs, plots, probability distributions, inferential statistics, data wrangling and variability. Mastering these will allow your child to explore programming solutions to real life problems. Their newfound knowledge will prepare them for internships in numerous fields such as data analysis, marketing, programming, entrepreneurship, etc.

Check out if this course is right for you by getting a free lesson!

Course Curriculum

right-icon Graphs   
right-icon Panda Dataframes 
right-icon Plots 
right-icon NumPy arrays 
right-icon Feature Variables 
right-icon Probabilistic Estimation
right-icon Coefficients
right-icon Normal Distribution
right-icon Random Variables
right-icon Conditional Probability

right-icon Graphs    right-icon Probabilistic Estimation
right-icon Panda Dataframes  right-icon Coefficients
right-icon Plots  right-icon Normal Distribution
right-icon NumPy arrays  right-icon Random Variables
right-icon Feature Variables  right-icon Conditional Probability


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Our academic advisor will help students enroll into various levels and topics based on their age, grade, prior exposure to programming, availability and goals.

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Completion Speed

Speed at which students will complete each topic depends on their level of commitment, frequency of weekly classes, and prior exposure to programming.

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