Need for data science ᎓ benefits and uses ᎓ facets of data ᎓ data science process ᎓ setting the research goal ᎓ retrieving data ᎓ cleansing, integrating, and transforming data ᎓ exploratory data analysis.
Frequency distributions ᎓ Outliers ᎓interpreting distributions ᎓ graphs ᎓ averages – describing variability – Normal distributions ᎓ z scores ᎓correlation ᎓ scatter plots ᎓ regression ᎓ regression line᎓ multiple regression equations
Populations ᎓ samples ᎓ random sampling ᎓ Sampling distribution- standard error of the mean – Hypothesis testing ᎓ z-test ᎓ z-test procedure ᎓decision rule ᎓ calculations ᎓ decisions ᎓ interpretations – one-tailed and two-tailed tests.
t-test for one sample ᎓ sampling distribution of t ᎓ t-test procedure ᎓ t-test for two independent samples ᎓ p-value ᎓ statistical significance ᎓ t-test for two related samples.
Linear least squares ᎓ implementation ᎓ goodness of fit ᎓ testing a linear model ᎓ weighted resampling
Reference Book:
1. Allen B. Downey, Think Stats: Exploratory Data Analysis in Python, Green Tea Press, 2014. 2. Sanjeev J. Wagh, Manisha S. Bhende, Anuradha D. Thakare, Fundamentals of Data Science, CRC Press, 2022. 3. Chirag Shah, A Hands-On Introduction to Data Science, Cambridge University Press, 2020. 4. Vineet Raina, Srinath Krishnamurthy, Building an Effective Data Science Practice: A Framework to Bootstrap and Manage a Successful Data Science Practice, Apress, 2021.
Text Book:
1. David Cielen, Arno D. B. Meysman, and Mohamed Ali, Introducing Data Science, Manning Publications, 2016. (first two chapters for Unit I). 2. Robert S. Witte and John S. Witte, Statistics, Eleventh Edition, Wiley Publications, 2017. 3. Jake VanderPlas, Python Data Science Handbook, O᎙Reilly, 2016