1.1 Statistics for Data Science with Python -and- 1:1 use case support
1.1 Advanced Analytics Statistics coursework for applied data science problems and prepared application in python. (10-hours)
Application of statistics tools in python using Google Collab similar to Jupyter notebook. Application using all other tools available with specialization using python. (10-hours)
1:1 use case support to apply statistics to your custom business challenges using illustrative data to support your application and success. Support available in your time zone and able to connect via google live meeting. (4-hours within 4 weeks of purchase)
Submit enrollment for customized quote of business needs. Proposal and introductory meeting will be scheduled within 24-48 hours.
- Objectives of course will enable participants to select, recommend and perform statistical tests using Python in cloud.
- Four (4) 2.5 hours sessions; 2.0 hour session integrated with coding exercises; 0.5 hour summary session to apply learning.
- Material provided in MS Powerpoint, Google Slides and Python Google Colab environment with prepared code; see enrollment email for instructions.
- Complimentary 4-hour 1:1 applied learning / use case support included; additional support can be purchased in 4-week blocks from www.analyticsforliving.org.
Session 1: Statistics and Python Introduction
- Statistics for Data Science with Python environment
- Python environment applied learning session
- Introduction to statistics
- Surveys on experiment
- Levels of measurement
Session 2: Describing data
- Describing data with tables and graphs
- Describing data with averages
- Describing variability
- Normal distribution and standard (z) scores
- Describing relationships: Correlation
- Populations, Samples, probability; Sampling distribution of the mean
Session 3: Hypothesis and t tests
- Introduction to hypothesis testing: The z test
- Hypothesis testing
- Estimation with confidence intervals
- t tests for one sample
- t tests for two independent samples
- t tests for two related samples (repeated measures)
Session 4: Variance measures
- Analysis of variance (one factor)
- Analysis of variance (repeated measures)
- Analysis of variance (two factors)
- Chi-Square (x) test for qualitative (nominal) data
- Tests for ranked (ordinal) data
Course offers advanced analytics statistics instruction for applied data science problems, as well as hands-on practice to prepare python application.
This course includes instruction on the application of statistics tools in Python utilizing Google Collab, akin to a Jupyter notebook. There will also be instruction on applying all other tools available with a focus on Python.
Receive personalized guidance in navigating data analysis for your unique business needs with real-world examples to bring context to the learning. Technical support is available in your time zone for connecting through Google Live Meeting.
Get the necessary skills to drive data-driven decisions with this 10-hour course in Statistics for Data Science with Python, and gain individual support and real-life use case application. Submit your enrollment for a customized quote crafted to your business needs. Proposals and introductory meetings will be scheduled within 24-48 hours.
This course enables participants to apply their statistics knowledge with Python. With 1:1 use case support, they will be empowered to select, recommend, and perform statistical tests within a cloud setting. Get 10 hours of real-world application and expert advice to get you up and running with data science success.
This comprehensive learning package introduces data science statistics with Python. Four 2.5 hour sessions with detailed explanations and plenty of coding exercises will give you the confidence to apply your new knowledge, and a 0.5 hour summary session ensures your understanding and allows you to apply your learning in real-world settings.
This 10-hour package provides comprehensive material for Statistics for Data Science with Python, including MS Powerpoint, Google Slides, and a Python Google Colab environment with prepared code. Receive 1:1 use case support to apply your learning and gain a deeper understanding of the concepts.
This course offers a comprehensive introduction to Statistics for Data Science using Python. It includes 10 hours of content and complimentary 4-hour 1:1 applied learning support to ensure comprehension and successful use case implementation. Additional use case support can be purchased from www.analyticsforliving.org.
Session 1: Statistics and Python Introduction. Learn to apply statistics to data science with the 10 hours of 1:1 use case support. Get a comprehensive introduction to the Python environment, statistics, experiment surveys, and levels of measurement in the first session. Expert guidance ensures successful mastery of the concepts.
Session 2: Describing data. This course offers a comprehensive introduction to descriptive statistics with Python. Participants will learn how to use tables and graphs to describe data, as well as to calculate averages, variability, and correlation. The course also delves into probability and sampling distributions of the mean, providing a thorough background in this essential mathematics topic. 1:1 use case support is included to ensure participants can apply their learning.
Session 3: Hypothesis and t tests. This 1:1 sessions in Statistics for Data Science with Python offers 10 hours of comprehensive use case support to apply your learning. The third session covers hypothesis and t tests, including introduction to hypothesis testing, estimation with confidence intervals, one-sample t tests, and two-sample (independent and related) t tests. Equip yourself with the knowledge you need to analyze data scientifically and draw accurate conclusions.
Session 4: Variance measures. Expertly increase your understanding of Statistics for Data Science with Python and gain 1:1 use case support to apply your learning with this 10 hours course. Session 4 provides comprehensive instruction on Variance measures, Analysis of Variance (one factor, repeated measures, two factors), Chi-Square (x) test for qualitative data, and tests for ranked data.
Gain the skills you need to implement your data science projects with Statistics for Data Science with Python. With 10 hours of personalized support from experts, you'll get step-by-step guidance on using Python to extract and analyze data, plus become comfortable using Google Colab and Jupyter Notebooks for all your data science needs.