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    Data Science Prep Course

    BY
    Springboard

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    Mode

    Online

    Duration

    6 Weeks

    Fees

    $ 490

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom
    Mode of Delivery Text Based
    Learning efforts 10-15 Hours Per Week

    Course and certificate fees

    Fees information
    $ 490
    certificate availability

    No

    The syllabus

    Topic 1: Introduction to Data Science & Python

    • An introduction to the world of data science
    • Reasons why Python is such a popular programming language
    • Common Python terms
    • An introduction to the Next.Tech platform
    • Python syntax
    • Python control flows
    • Python lists
    • Python functions
    • An introduction to computational thinking
    • Data distributions
    • Displaying and describing quantitative data
    • Scatter Plots

    Topic 2: Intermediate Python

    • Python dictionaries
    • Python strings
    • Part 2 of Python control flows
    • The difference between functions and methods
    • How to effectively interact with a program in Python 

    Topic 3: Foundations of Probability

    • Calculating basic probabilities
    • Bayes’ Theorem
    • Conditional probability 

    Topic 4: Python: Data Structures & Algorithms

    • Data structures, including stacks and queues
    • Search algorithms
    • Sort algorithms

    Topic 5: Data Science Technical Skills Survey

    • Features 10 statistics multiple choice questions and 2 open-ended coding questions
    • You’ll be able to take the technical skills survey as many times as you like


    Topic 6: Your Data Science Toolbox

    • Using Anaconda and Conda
    • Working with Git and GitHub
    • An introduction to Jupyter Notebooks
    • NumPy and Matplotlib
    • Data manipulation with Pandas

    Topic 7: The Apps Project

    • Approaching a dataset with a business problem in mind
    • Using common data science tools (like Jupyter Notebooks and Matplotlib)
    • Downloading and transforming data for analysis
    • Exploring data through the use of plots and statistics
    • Data storytelling

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