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Statistical Modeling for Data Science Mini Bootcamp

Statistical Modeling for Data Science Mini Bootcamp

  • About

    Today, there’s a huge demand for data science expertise as more and more businesses apply it within their operations. Python offers the right mix of power, versatility, and support from its community to lead the way. While Python is most popular for data wrangling, visualization, general machine learning, deep learning and associated linear algebra (tensor and matrix operations), and web integration, its statistical modeling abilities are far less advertised. A large percentage of data scientists still use other special statistical languages such as R, MATLAB, or SAS over Python for their modeling and analysis. However, only by Python-based statistical modeling, one can build a powerful end-to-end data science pipeline (a complete flow extending from data acquisition to final business decision generation) using a single programming language. This bootcamp will teach fundamentals of statistical modeling concepts with easy-to-follow examples in Python to get you started in your data science journey.

  • Curriculum
    • Descriptive statistics

    • 1. A bit of history, why statistics is ‘hot’ today, a few examples


      2. Central tendency, dispersion measures, other simple descriptive measures


      3. Bi-variate statistics, correlation, plotting


      4. Probability, random variables, probability distributions


      5. Exploratory data analysis (EDA) example using descriptive stats

    • Inferential statistics

    • 1. Estimation, inference, confidence interval


      2. Hypothesis testing, p-values, statistical significance


      3. Type-I/Type-II errors, nature of statistical learning, difference from ML


      4. Comparing means, t-test, ANOVA

    • Application in machine learning/data science

    • 1. Linear regression, posing linear regression as statistical inference problem


      2. Logistic regression, inference


      3. Naive Bayes classification, maximum likelihood estimation


      4. K-means clustering, expectation-maximization (E-M), Gaussian mixture model (GMM)

  • Schedule
    • Start learning the Statistical Modeling for Data Science with outside of business hours schedule!
    • The 12 hours of schedule is as follows:
    • February 13 – 20 – 27 and March 5
    • Thursdays, from 6:30 pm to 9:30 pm
  • Location

    The venue for the bootcamp is Magnimind Academy Sunnyvale Campus: 830 Stewart Dr #182, Sunnyvale, CA 94085. The capacity is limited to 20 people.

  • Online

    Statistical Modeling for Data Science Mini Bootcamp is now also available online. Anyone who wants to attend this mini bootcamp can join online live webinars where the same course content will be taught. Online sessions will be distributed through zoom conferences. Students will have access to the screen of the instructor, external camera showing class atmosphere, whiteboard, and be able to ask questions through chat. You may attend this mini bootcamp no matter where you are.

  • Tuition
    Tuition fee

    Statistical Modeling for Data Science Mini Bootcamp has a $300 tuition fee.

    For the “Early Bird” applicants (January 15 – February 6), the tuition fee is $250.

    Payment process

    After you finish filling your application form, the website will direct you to the payment page. There, you can select available payment options.

    Cancellation

    If you’re not satisfied with the course you may cancel your application.

  • Application

    The application process starts at magnimindacademy.com. You can view the course pages and learn more about your intended course. You can apply by clicking the “Buy now” button and then fill out the application form.

  • Instructor

    Dr. Tirthajyoti Sarkar works as a Sr. Principal Engineer at ON Semiconductor developing cutting-edge semiconductor and power electronics technology and applying AI and machine learning techniques to related problems. He serves as an associate editor of Towards Data Science, the most wide-reaching online publication platform in data science and machine learning. Tirthajyoti regularly conducts tutorials and workshops on hands-on machine learning and data analytics along with IEEE and ACM societies in the Bay area. His book on “Data wrangling with Python” was well received in the community and he is working on a second book “Hands-on Mathematics for Data Scientists”. He has also published multiple open-source software packages in the domain of data science and statistical modeling. Tirthajyoti holds a Ph.D. from University of Illinois. He is a senior member of IEEE, owner of multiple U.S. patents, and has published numerous papers in top international journal and publications.

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