Data Science Foundation With Python

Modules / Chapter

Preliminaries

Quick introduction to python libraries, revision of structure and construct.

        Prerequisite:

• Python Introduction
• Python: Beyond basic

    Introduction

• Python for Data Analysis
• Essential Python Libraries

    python

• Basic
• Loops, Range, Enumerate
• Functions and Lambda
• Sets and Dictionaries
• Primer
• Iterator and Generators
• Map, Filter, Reduce
• List Comprehension

Numerical Computation

    introduction to numpy

    Prerequisite:

• Python: Intro
• Python: Beyond Basic

    Numerical Python

• Why NumPy 
• Operation Rules 
• Function
• Broadcasting
• nd-array
• Indexing: Normal and Fancy
  
• Slicing Opertation
• Reshaping, Pivoting, Transform

      Introduction to Matplotlib

• Graphing Data
• Scatter line, Stemplot
• Bar: Histograms, Pareto Charts
• Other: Pie Charts, Box Plots/Box
• Introduction to Matplotlib 
• Axis
• Subplots
• Color map
• Styling

Statistical Foundation

• Handling Data 
• Web, html
• Plain, Tabular
• Basic Statistics
• What is Data
• Qualitative and Quantitative
• Discrete and Continuous
• Measurement: Nominal. Ordinal, Interval, Ratio
• Data Summary
• Measures of Center: Mean, Median, Mode, Midrange
• Frequency Tables/Distributions
• Class Limits, Width, Midpoints, Boundaries
• Frequencies: Cumulative, Relative
• Standard Deviation and Mean of Frequency Table

Exploratory Data Analysis

• Quantitative Association
• Covariance
• Correlation
• Scatter Matrix

Data Processing Framework

Pandas is the integrated structural framework for statistics, data manipulation analysis.

    Data Frame

• Data Selection
• Sorting, Searching, Group-By, filter
• Data Summarization
• Aggregation methods, Axis

    Time Series

• Date and Time Module
• Delta, Time Spans, Conversion
• Series
• Shifting, Resampling, Frequency
• Rolling Statistics
• Lag Function
• Window Function

    Inferential Statistics

• Sampling
• Populations, Samples, Parameters
• Random Samples and Simple Random Samples
• Stratified, Cluster, Systematic, and Convenience Sampling
• Sample Size needed to Estimate a Population Proportion
• Measures of Relative Standing
• Variance
• Coefficient of Variation
• Z-Scores
• Maximum 

• Probability

• Probability Distributions
• The Normal Distribution
• Relationship Between Variables
• Computing Normal Probabilities
• Standard Normal Distribution
• Central Limit Theorem
• Binomial Distribution
• Poisson Distribution
• Precision and Significance
• Interpreting Confidence Intervals
• Margin of Error
• Z versus T
• NHST
• Hypothesis Tests for one mean, two means
• One proportion, two proportions
• Dependent and independent data
• Hypothesis tests given the data
• Hypothesis tests for paired Data
• Interpreting Hypothesis tests
• Computing p-values

Data Analysis

• Association of variable
• Correlation
• Serial correlation test: durbin watson test
• Chi-square Test
• Analysis of Variance
• Correlation Analysis
  
• Linear Correlation
• Coefficient R
• Coefficient of Determination
• Least Squares Regression Line
• Regression
• Simple Linear Regression
• Least Squares Line
• Correlation Coefficient
• Coefficient of Determination
• General Linear Model
• f-test
• Multicollinarity
• Multi Variable Linear Regression
• Time Series Analysis
• Self Correlation
• Auto Correlation Function
• Partial Autocorrelation
• Trend Analysis
• Detrend, Sesonality
• Stationarity Tests: Dickey Fuller Test
• Forecasting Using ARIMA Model