Statistics for Data Analytics

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Custom Price From £240 (approx. $316 USD) Price Calculator & Booking Short course on Statistics for Data Analytics

Total Duration: 12 Hours
Course level: Beginner
Delivery Method: Instructor-led Virtual Classes
Certification: Certificate of Completion will be provided after completing the course

Course Overview

This course comprehensively introduces the key statistical concepts and techniques essential for data analytics. It is designed for individuals looking to build a statistics foundation, focusing on practical applications in data-driven decision-making. Throughout the course, students will learn to apply statistical methods to real-world data, interpret the results, and use these insights to inform business decisions. We highly recommend this course to the students before starting our "Data Science with Python" and the "AI and Machine Learning with Python" courses.

Requirements

Basic arithmetic skills
Familiarity with basic algebra concepts
Basic knowledge of programming (preferably Python)

You may also complete the following course(s) before attending the Statistics for Data Analytics course but they are not mandatory:

Python Programming for Beginners

Course Dates, Prices & Enrolment

All Training Physical Classes Virtual Classes

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Trainig venue: Unit 15, Boardman House, 64 Broadway, Stratford, London E15 1NT, United Kingdom

Course Content

Introduction to Statistics and Data Science
- Definition and importance of statistics in data science
- Types of data: qualitative vs. quantitative
- Levels of measurement: nominal, ordinal, interval, ratio
- Types of statistics: descriptive vs. inferential
- Overview of the data science process: data collection, cleaning, analysis, and interpretation
- Activities:
  - Discussion on real-world applications of statistics in data science
  - Interactive quiz on types of data and levels of measurement
Descriptive Statistics
- Measures of central tendency: mean, median, mode
- Measures of variability: range, variance, standard deviation
- Data visualisation: histograms, bar charts, box plots
- Using Python libraries (e.g., Pandas, Matplotlib) for descriptive statistics and visualisation
- Activities:
  - Hands-on calculation of mean, median, and mode using Python
  - Creating and interpreting various data visualisations in Python
Probability Basics
- Definition and basic concepts of probability
- Probability rules: addition and multiplication rules
- Independent and dependent events
- Conditional probability
- Introduction to Python libraries for probability calculations (e.g., NumPy, SciPy)
- Activities:
  - Simple probability experiments (coin toss, dice roll)
  - Problem-solving exercises on probability rules using Python
Probability Distributions
- Discrete vs. continuous probability distributions
- Binomial distribution
- Normal distribution and its properties
- Standard normal distribution and z-scores
- Using Python to visualise and calculate probabilities for different distributions
- Activities:
  - Visualisation of binomial and normal distributions in Python
  - Practice problems on calculating probabilities using z-scores in Python
Inferential Statistics
- Sampling methods and sample size
- Central Limit Theorem
- Confidence intervals
- Hypothesis testing: null and alternative hypotheses, p-values
- Implementing inferential statistics in Python
- Activities:
  - Sample size determination exercises
  - Calculating and interpreting confidence intervals in Python
  - Conducting hypothesis tests using Python
Correlation, Regression, and Practical Applications
- Scatterplots and correlation
- Pearson correlation coefficient
- Simple linear regression: interpretation of slope and intercept
- Introduction to multiple regression
- Practical applications in data science: predictive modelling, feature selection
- Using Python for correlation and regression analysis
- Activities:
  - Calculation and interpretation of correlation coefficients in Python
  - Creating and interpreting regression lines in Python

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Save Money with Packages

SAVE up to 20% by booking this course with other related courses as shown below:

Python Immersive

£2,352 (save £588)

This package combines

Python Programming for Beginners - 12 hours
Python Intermediate to Advanced - 12 hours
Data Analysis with Python - 12 hours
Statistics for Data Analytics - 12 hours
Data Science with Python - 12 hours
Web Development with Python Flask - 12 hours
AI and Deep Learning with Python - 12 hours

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