📖 Lessons

1. Introduction to Mathematics in Data Analytics
   • The importance of mathematics in data analytics
   • Overview of the course structure and learning outcomes
2. Basic Arithmetic and Algebra
   • Fundamentals of arithmetic operations
   • Introduction to algebraic expressions and equations
   • Practical exercises: Applying basic operations and algebra in data analytics scenarios
3. Maths applied to Data Analytics
   • Calculating proportions and understanding their significance in data
   • Year-over-Year (YoY), Year-to-Date (YTD), cumulative, and rolling and trailing calculations
   • Percent difference: Theory and application
   • Case studies: Analyzing real-world data sets to identify trends and changes

✅ Results

By the end of this module, you’ll equip yourself with the mathematical foundations crucial for data analytics, enabling you to approach data analysis with confidence and precision.

📖 Lessons

1.Central Tendency and Variability

• • Mean, Median, Mode: Comprehensive calculation methods and in-depth comparison of their usage in various datasets. Understanding when to apply each measure depending on data distribution and outliers.
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  • Range, Variance, Standard Deviation: Detailed exploration of data variability, how to calculate these metrics, and their significance in interpreting the spread and consistency of data. Discussion on the importance of standard deviation in assessing data reliability and predicting trends.       

2. Exploring Data Distributions

• • Types of distributions (normal, skewed, etc.)
  • The shape of data and its implications

✅ Results

By the end of this module, you’ll master descriptive statistics to effectively summarize and describe the characteristics of data sets, laying the groundwork for deeper data exploration

📖 Lessons

1. Inferentials statistics and tests
   • Master hypothesis testing and the different types of test statistics like the T-Test, Anove and Chi-Square
2. Regression and Correlation
   • Understanding linear regression and correlation coefficients
   • Practical application: Using regression to predict outcomes

✅ Results

By the end of this module, you’ll learn how to use inferential statistics to identify and analyze relationships between variables using regression and correlation, critical for predictive modeling and data-driven decision-making.

📖 Lessons

1. Sampling Techniques
   • Probability and non-proability sampling methods
   • Recognizing and mitigating bias in data collection
2. Bias
   • Recognizing the different bias types critical to build an informed conclusion of your analysis

✅ Results

By the end of this module, you’ll understand the principles of different sampling methods, enhancing your ability to infer and make predictions about larger populations from sample data.

📖 Lessons

Hands-on project: Conducting EDA on a provided dataset, using Python and Large Language models to apply the concepts covered in the previous modules

✅ Results

By the end of this module, you’ll develop the skills to conduct thorough exploratory data analysis (EDA), uncovering insights and guiding informed analytical hypotheses.

📖 Lessons

1. Final Tips for Learning Mathematics and Statistics
   • Strategies for effective learning and application in data analytics
   • Resources for further study and practice

✅ Results

By the end of this module, you’ll gain practical advice and resources for continuous learning in mathematics and statistics, ensuring you remain at the cutting edge of data analytics proficiency.