Statistics for Data Analytics

Batch Price From £210 (approx. $277 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:

Introduction to Flask
Routing and URL building
Flask templates and Jinja2 basics
Flask Blueprints for modular applications
Defining database models using SQLAlchemy
CRUD operations with SQLAlchemy
Flask-Login and LoginManager for user authentication
Securing routes with login_required decorator
Using Werkzeug.security for password hashing and verification
Creating RESTful APIs using Flask
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:

Python Programming for Beginners

Course Dates, Prices & Enrolment

All Training Physical Classes Virtual Classes

Time Zone:

Training Method	Dates and Times	Total Hours & Days	Price
Online Training using Zoom	24 Sep 2024 - 27 Sep 2024 Tue, Wed, Thu & Fri 12:30 PM - 03:30 PM ET	12 hours over 4 days	£210 £420 (approx. $277 USD)	Enrol Now
Online Training using Zoom	14 Oct 2024 - 01 Nov 2024 3 Mondays & 3 Fridays 12:30 PM - 03:30 PM ET	12 hours over 4 days	£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	12 hours over 4 days	£420 (approx. $553 USD)	Enrol Now

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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