 Learn Applied Statistics

using Python Today!

Learn how to incorporate python for your
needs in Applied Statistics.

5 Days / 40 Hours / 15 Modules
+ 3 month coaching + 1 year eLearning access
Learn Applied
Statistics using
Python Today

Learn how to
incorporate python
applied statistics
5 Days / 40 Hours / 15 Modules
+ 3 month coaching + 1 year eLearning access

Learn the how python is used in the practice of Applied Statistics and deep dive into the fundamentals and key features of it

Applied Statistics is a statistics course that uses current technology (Python) to focus on mathematical understanding instead of manual routine calculations.

Tools Covered For This Course:

Key Learning Outcomes

This course will also give students a chance to understand the fundamental issues and challenges web application development with the Django framework. This course will equip you with the necessary skills needed to excel in this field. By the end of the training program, you will be able to:

• Classify data through levels of measurement
• Understand Python data distribution
• Conduct hypothesis test for correlation
• Understand Bayes' theorem

Key Learning Outcomes

This course will also give students a chance to understand the fundamental issues and challenges web application development with the Django framework. This course will equip you with the necessary skills needed to excel in this field. By the end of the training program, you will be able to:

• Classify data through levels of measurement
• Understand Python data distribution
• Conduct hypothesis test for correlation
• Understand Bayes' theorem

Course Modules Covered in the Applied Statistics using Python training program
Day 1 - Descriptive Statistics, Charts and Graphs, Data distribution

#### Descriptive Statistics

1. Introduction to statistics
2. Data types
3. Ways of classifying data, levels of measurement
4. Types of series – Individual, Discrete and Continuous
5. Critical thinking skills
6. Python numpy
7. Ndarray
8. Data Types
9. Attributes
10. Creation Routines
11. Array from existing data
12. Ranges
13. Indexing and Slicing
14. Arithmetic, Logical and Binary Operators
15. String Functions

#### Charts and graphs

1. Introduction
2. Benefits of Plotting Graph
3. Frequency distributions
4. Bar Charts
5. Stem and Leaf plots
6. Introduction to Matplotlib
7. Introduction to Seaborn
8. Distribution Plots
9. Categorical Plots
10. Matrix Plots

#### Data distribution

1. Center
3. Shape – Symmetry, Number of peaks, Skewness, Uniform>
4. Unusual Features – Gaps, Outliers
5. Measures of central tendency - Mean, Median, Mode, Midrange
6. Measures of spread - Range, Variation, Standard deviation, Interquartile range
7. Measures of shape - Empirical rule, Chebyshev's rule, Skewness, Kurtosis
8. Measures of relative position – Quartiles, Percentiles, Midquartile
Day 2 - Python Data distribution, Probability, Decisions

#### Python Data distribution

1. Lab: HousePrices.csv
2. Introduction to Series
3. Introduction to Pandas
4. DataFrames
6. Methods: head, shape, info, mean, median mode
7. Histogram
8. Methods: min, max, range, sqrt
9. Methods: sorted, std, hist, correlation, heatmap
10. Methods: skew, kurt, cov, quantile
11. Boxplot

#### Probability

1. Fundamentals of probability
2. Probabilities from frequency tables
3. Set theorem
4. Unions and intersections
6. Multiplication rule for "and"
7. Introduction to Python Set
8. Set operations
9. Built-in Set methods

#### Decisions

1. Tree diagrams
2. Lab: Tree-plots using Python
3. Conditional probabilities
4. Lab: Calculate the probability of student getting A, based on attendance
5. Counting techniques
6. Lab: Counting task using Python
Day 3 - Distribution, Z-Score, Inferential Statistics

#### Distribution

1. Introduction
2. Benefits of Distribution
3. Random variables
4. Discrete random variable
5. Continuous random variable
6. Variance
7. Standard deviation
8. Uniform distributions
9. Normal distributions
10. Gamma distributions
11. Exponential distributions
12. Poisson distribution
13. Binomial distributions
14. Bernoulli distributions
15. Multinomial distributions
16. Lab: Plotting distribution graph using Matplot and Seaborn

#### Z-Score

1. Definition
2. Finding areas from z-scores
3. Finding z-scores from areas.
4. Applications of the normal distribution.
5. Converting from and to raw scores.

#### Inferential Statistics

1. Introduction
2. Definitions of Sample, Population
3. Randomization testing
4. Lab: Calculate the probability of student getting A, based on attendance
5. Types of sampling
6. Types of sampling errors
Day 4 - Central Limit Theorem (CLT), Inference and Significance, Inference for Comparing Means

#### Central Limit Theorem (CLT)

1. Introduction
2. Sampling variability and CLT
3. CLT for the mean examples
4. Confidence interval for a mean
5. Accuracy vs Precision

#### Inference and Significance

1. Hypothesis testing fundamentals
2. Hypothesis testing for a mean
3. Inference and other Estimators
4. Decision Errors
5. Approach comparing probability value to significance level
6. Approach comparing claimed value to confidence interval

#### Inferential Statistics

1. T distribution
2. Inference for a mean
3. Inference for comparing two independent means
4. Inference for comparing two paired means
5. Comparing more than two means
6. Confidence intervals for the population mean
7. Confidence intervals for the population proportion
Day 5 - Probability and Bayes' Theorem, Advanced Inferential Statistics, Classification

#### Probability and Bayes' Theorem

1. Classical and frequentist probability
2. Bayesian probability and coherence
3. Bayes' theorem
4. Inference example: frequentist4m
5. Inference example: Bayesian6m
6. Confidence intervals
7. Likelihood function and maximum likelihood
8. Computing the MLE
9. Plotting the likelihood in Python
10. Continuous version of Bayes' theorem
11. Posterior intervals

1. Linear correlation
2. Hypothesis test for correlation
3. Regression analysis, finding regression equation
4. Linear Regression
5. Explained, unexplained, and total deviations
6. Coefficient of determination
7. Table of coefficients and Analysis of Variance. F distribution.
8. Multiple regression, adjusted R squared (time permitting)

#### Classification

1. Introduction
2. Logistic Regression
3. Logistic Model
4. Estimating the Regression Coefficients
5. Making Predictions
6. Multiple Logistic Regression
7. Linear Discriminant Analysis
8. Using Bayes Theorem
9. Logistic Regression
10. Linear Discriminant Analysis
11. K-Nearest Neighbors
12. Decision Trees and Random Forests
13. Support Vector Machines
14. K Means Clustering
15. Principal Component Analysis
Program Key Highlights 40 hours of Remote Online Learning  12 Live Hands-on Projects Certified by International Body Mentorship with Industry Experts Designed for Beginners & Professionals