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.

#### Duration

5 days / 40 hours

#### Level

Intermediate to Advanced

#### Delivery

100% Online - Instructor Led

- Course Code

ML103 - Audience

Anyone with interest in Machine Learning And R programming - Language

English - Certification

Participants will receive a certificate of competence.

Tools Covered For This Course:

Course Modules Covered in the Applied Statistics using Python training program

Day 1 - Descriptive Statistics, Charts and Graphs, Data distribution

#### Descriptive Statistics

- Introduction to statistics
- Data types
- Ways of classifying data, levels of measurement
- Types of series – Individual, Discrete and Continuous
- Critical thinking skills
- Python numpy
- Ndarray
- Data Types
- Attributes
- Creation Routines
- Array from existing data
- Ranges
- Indexing and Slicing
- Arithmetic, Logical and Binary Operators
- String Functions

#### Charts and graphs

- Introduction
- Benefits of Plotting Graph
- Frequency distributions
- Bar Charts
- Stem and Leaf plots
- Introduction to Matplotlib
- Introduction to Seaborn
- Distribution Plots
- Categorical Plots
- Matrix Plots

#### Data distribution

- Center
- Spread
- Shape – Symmetry, Number of peaks, Skewness, Uniform>
- Unusual Features – Gaps, Outliers
- Measures of central tendency - Mean, Median, Mode, Midrange
- Measures of spread - Range, Variation, Standard deviation, Interquartile range
- Measures of shape - Empirical rule, Chebyshev's rule, Skewness, Kurtosis
- Measures of relative position – Quartiles, Percentiles, Midquartile

Day 2 - Python Data distribution, Probability, Decisions

#### Python Data distribution

- Lab: HousePrices.csv
- Introduction to Series
- Introduction to Pandas
- DataFrames
- Read From CSV
- Methods: head, shape, info, mean, median mode
- Histogram
- Methods: min, max, range, sqrt
- Methods: sorted, std, hist, correlation, heatmap
- Methods: skew, kurt, cov, quantile
- Boxplot

#### Probability

- Fundamentals of probability
- Probabilities from frequency tables
- Set theorem
- Unions and intersections
- Addition rule for "or"
- Multiplication rule for "and"
- Introduction to Python Set
- Set operations
- Built-in Set methods

#### Decisions

- Tree diagrams
- Lab: Tree-plots using Python
- Conditional probabilities
- Lab: Calculate the probability of student getting A, based on attendance
- Counting techniques
- Lab: Counting task using Python

Day 3 - Distribution, Z-Score, Inferential Statistics

#### Distribution

- Introduction
- Benefits of Distribution
- Random variables
- Discrete random variable
- Continuous random variable
- Variance
- Standard deviation
- Uniform distributions
- Normal distributions
- Gamma distributions
- Exponential distributions
- Poisson distribution
- Binomial distributions
- Bernoulli distributions
- Multinomial distributions
- Lab: Plotting distribution graph using Matplot and Seaborn

#### Z-Score

- Definition
- Finding areas from z-scores
- Finding z-scores from areas.
- Applications of the normal distribution.
- Converting from and to raw scores.

#### Inferential Statistics

- Introduction
- Definitions of Sample, Population
- Randomization testing
- Lab: Calculate the probability of student getting A, based on attendance
- Types of sampling
- Types of sampling errors

Day 4 - Central Limit Theorem (CLT), Inference and Significance, Inference for Comparing Means

#### Central Limit Theorem (CLT)

- Introduction
- Sampling variability and CLT
- CLT for the mean examples
- Confidence interval for a mean
- Accuracy vs Precision

#### Inference and Significance

- Hypothesis testing fundamentals
- Hypothesis testing for a mean
- Inference and other Estimators
- Decision Errors
- Approach comparing probability value to significance level
- Approach comparing claimed value to confidence interval

#### Inferential Statistics

- T distribution
- Inference for a mean
- Inference for comparing two independent means
- Inference for comparing two paired means
- Comparing more than two means
- Confidence intervals for the population mean
- Confidence intervals for the population proportion

Day 5 - Probability and Bayes' Theorem, Advanced Inferential Statistics, Classification

#### Probability and Bayes' Theorem

- Classical and frequentist probability
- Bayesian probability and coherence
- Bayes' theorem
- Inference example: frequentist4m
- Inference example: Bayesian6m
- Confidence intervals
- Likelihood function and maximum likelihood
- Computing the MLE
- Plotting the likelihood in Python
- Continuous version of Bayes' theorem
- Posterior intervals

#### Advanced Inferential Statistics

- Linear correlation
- Hypothesis test for correlation
- Regression analysis, finding regression equation
- Linear Regression
- Explained, unexplained, and total deviations
- Coefficient of determination
- Table of coefficients and Analysis of Variance. F distribution.
- Multiple regression, adjusted R squared (time permitting)

#### Classification

- Introduction
- Logistic Regression
- Logistic Model
- Estimating the Regression Coefficients
- Making Predictions
- Multiple Logistic Regression
- Linear Discriminant Analysis
- Using Bayes Theorem
- Logistic Regression
- Linear Discriminant Analysis
- K-Nearest Neighbors
- Decision Trees and Random Forests
- Support Vector Machines
- K Means Clustering
- Principal Component Analysis

### Key Features

- 40 hours of instructor led training
- Fully Online
- Class recording available
- Interactive Learning
- 3 month Coaching Session
- 100% HRDF SBL-KHAS Claimable!

### Pre-Requisites

- Basic Programming Knowledge
- Analytical Mindset
- Willingness to self learn online
- No prior experience is required
- We will start from the very basics
- Committed to complete all tasks

### Who Should Join

- Professional switching careers
- Business Analysts
- IT Engineers
- Students
- New Programmers
- Anyone interested in Web Application Development

Program Key Highlights

40 hours of Remote Online Learning

80 Additional Self Learning Hours

12 Live Hands-on Projects

Certified by International Body

Mentorship with Industry Experts

Designed for Beginners & Professionals

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