Learn Applied Statistics
using Python Today!
 
Learn how to incorporate python for your
needs in Applied Statistics.
funding-schemes sbl-khas-black-centered
5 Days / 40 Hours / 15 Modules
+ 3 month coaching + 1 year eLearning access
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
funding-schemes-sbl-khas-white-centered
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:
 
python2
 
 
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
  • Read files from CSV
  • 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
  • Read files from CSV
  • 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
  2. Spread
  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
  5. Read From CSV
  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
  5. Addition rule for "or"
  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

Advanced Inferential Statistics

  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

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
online-learning-2
40 hours of Remote Online Learning
learning-hours
80 Additional Self Learning Hours
hands-on
12 Live Hands-on Projects
certification
Certified by International Body
mentor
Mentorship with Industry Experts
industry
Designed for Beginners & Professionals
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