Data Science & Python - Maths, models, Stats PLUS Case Study

Based on the detailed outline you've provided, this course is designed to take learners through a comprehensive journey in Data Science, with a particular emphasis on Business Intelligence. The curriculum covers a wide range of topics, from the basics of statistical concepts to advanced machine learning techniques. Here's a structured breakdown of what the course appears to offer:

Introduction and Overview

1. Introduction to Scientist – Statistics and Data Domain: An introduction to the platform and domain.
2. Business Intelligence Tools: An overview of tools used in Business Intelligence.
3. Types of Data Acquisition: Understanding where data comes from and how it is collected.
4. Data Preparation, Exploration: Techniques for preparing and exploring data effectively.
5. Process of Data Science: A step-by-step guide through the process of data science.
6. Career Aspects for a Data Scientist: Insights into the role and career opportunities.
7. Demand and Challenges for Data Science: Understanding the current demand for data science professionals and the challenges they face.
8. Mathematical and Statistical Concepts: A foundation in the mathematical and statistical concepts used in data science.
9. Variables – Numerical and Categorical: Differences between numerical and categorical variables.
10. Qualitative Variables, Central Tendency, Dispersion: An exploration of qualitative variables and measures of central tendency and dispersion.
11. Descriptive vs Inferential Statistics: The differences between descriptive and inferential statistics.

Data Science Techniques and Tools

1. Installing Anaconda and Using Jupyter: Practical steps to set up the environment for data science using Anaconda and Jupyter.
2. Data Statistics and Analysis in Jupyter: How to input and analyze data within the Jupyter application.
3. Probability Theory and Conditional Probability: Introduction to probability concepts and how to apply them conditionally.
4. Inferential Statistics – Distribution and Probability: Understanding normal distribution, PDF, CDF, and Gaussian distribution.
5. Correlation Coefficient, Scatter Plot, Regression Analysis: Techniques to measure and interpret relationships between variables.
6. Machine Learning Models: An introduction to decision trees, clustering (K-means), and other machine learning models.
7. Evaluation Metrics: Learning about accuracy, precision, recall, F1 score, MSE, RMSE, R-squared, and more.

Real-World Applications in Sales

1. Data Science Use Cases in Sales: Applying data science to predict future sales.
2. Case Study – Future Sales Prediction: A practical example of using data science for sales forecasting.

Course Delivery and Support

1. Premium Support and Feedback: The course offers personalized support and feedback to help learners become more confident in their data science skills.
2. Happiness Guarantee: A 30-day money-back guarantee if you are not satisfied with the course.

Instructors

1. Laika Satish: The lead instructor, a professional data scientist.
2. Peter Alkema: A content creator collaborating with Laika to deliver the course content.

This course seems to be designed for learners who want to gain a deep understanding of data science, from both theoretical and practical perspectives, with a focus on applying these skills in real-world scenarios, particularly within the context of sales. It's clear that the course aims to provide comprehensive instruction with hands-on experience using Python and its libraries such as scikit-learn, pandas, and seaborn for data analysis and visualization.