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Statistics for Data Analytics

Batch Price From £360 (approx. $474 USD) View Dates & Prices 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.

Following topics are included in this course:

  1. Introduction to Flask
  2. Routing and URL building
  3. Flask templates and Jinja2 basics
  4. Flask Blueprints for modular applications
  5. Defining database models using SQLAlchemy
  6. CRUD operations with SQLAlchemy
  7. Flask-Login and LoginManager for user authentication
  8. Securing routes with login_required decorator
  9. Using Werkzeug.security for password hashing and verification
  10. Creating RESTful APIs using Flask
  11. Deploying Flask Applications

Requirements

  • Essential: Prior knowledge of HTML, CSS, Python (basic) and basic database concepts are helpful but not required.
  • 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:

Course Dates, Prices & Enrolment

All Training Physical Classes Virtual Classes
Time Zone:
Training MethodDates and TimesPrice 
Online Training using Zoom 14 Oct 2024 - 01 Nov 2024
3 Mondays & 3 Fridays
12:30 PM - 03:30 PM ET
£360 £420
(approx. $474 USD)
Enrol Now
Online Training using Zoom 11 Nov 2024 - 29 Nov 2024
3 Mondays & 3 Fridays
12:30 PM - 03:30 PM ET
£420
(approx. $553 USD)
Enrol Now
Online Training using Zoom 15 Feb 2025 - 08 Mar 2025
4 Saturdays
10:00 AM - 01:00 PM ET
£360 £420
(approx. $474 USD)
Enrol Now
Online Training using Zoom 17 Feb 2025 - 28 Feb 2025
Mon, Wed & Fri (2 wks)
09:30 AM - 11:30 AM ET
£360 £420
(approx. $474 USD)
Enrol Now
Online Training using Zoom 26 Apr 2025 - 17 May 2025
4 Saturdays
10:00 AM - 01:00 PM ET
£360 £420
(approx. $474 USD)
Enrol Now
Online Training using Zoom 09 Jun 2025 - 20 Jun 2025
Mon, Wed & Fri (2 wks)
09:30 AM - 11:30 AM ET
£360 £420
(approx. $474 USD)
Enrol Now
Online Training using Zoom 05 Jul 2025 - 26 Jul 2025
4 Saturdays
10:00 AM - 01:00 PM ET
£360 £420
(approx. $474 USD)
Enrol Now
Online Training using Zoom 13 Sep 2025 - 04 Oct 2025
4 Saturdays
10:00 AM - 01:00 PM ET
£360 £420
(approx. $474 USD)
Enrol Now
Online Training using Zoom 20 Oct 2025 - 31 Oct 2025
Mon, Wed & Fri (2 wks)
09:30 AM - 11:30 AM ET
£360 £420
(approx. $474 USD)
Enrol Now
Online Training using Zoom 22 Nov 2025 - 13 Dec 2025
4 Saturdays
10:00 AM - 01:00 PM ET
£360 £420
(approx. $474 USD)
Enrol Now

Course Content

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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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