Foundations of data analytics with Python

Python variables, data types, and control structures (AC 1.1):

• Python programming, focusing on using control structures (if statements, loops) and data structures (lists, tuples, dictionaries) to manage and filter data collections
• Learners will explore conditional logic and loops to repeat tasks.
• Working with lists, tuples, and dictionaries to organise and process data. Integrating statistical results into structured Python data structures for further analysis. 

Applying basic operations (AC 1.2):

• Arithmetic Operations from Python's math module
• String Operations: concatenation, formatting with f-strings, string methods to contextualise the calculations
• Comparison and Logical: equality and inequality operators, greater than and less than and boolean
• Built-in Functions

Data structures and libraries (NumPy and Pandas) for manipulation and analysis (AC 1.3):

• NumPy Fundamentals:

1. Array: quick array manipulation, statistical calculations, data aggregation, and correlation analysis.

• Pandas Essentials:

1. Data Selection: To easily manage, transform, and analyse structured data for data-driven insights.
2. Basic Operations: data loading, exploration, manipulation, and analysis using data structures like DataFrames and Series.

• Integration Benefits: NumPy for efficient array computations, Pandas for data manipulation and analysis

Analysing data and applying statistical concepts to practical scenarios (AC 2.1):

• Understanding Hypothesis Testing and Statistical Significance: This introduces the concept of hypothesis testing, focusing on the null and alternative hypotheses, p-values, and significance testing. Learners will explore ANOVA, T, and Chi-square tests, applying these methods to real-world datasets to evaluate statistical significance. Using spreadsheets, they will conduct statistical tests and calculate confidence intervals to assess the reliability of the data and identify trends.
• Key areas of focus: Understanding p-values, significance levels, and hypothesis formulation. Conducting ANOVA, T-tests, and Chi-square tests to determine statistical significance.
• Applying confidence intervals to assess the reliability of conclusions. Performing hypothesis testing in spreadsheets and other tools.
• Central Tendency Measures: calculating mean, median, mode, and when to use each measure
• Dispersion Measures: variance, standard deviation, range calculations, interquartile range (IQR), and when to use them
• Correlation Analysis: Pearson correlation, interpreting correlations, causation vs correlation warning
• Practical Applications: customer behaviour analysis, sales trend identification, quality control metrics, risk assessment, performance benchmarking

Descriptive and inferential techniques for real-world problems (AC 2.2):

• Descriptive Statistics Implementation:

1. Differentiating descriptive and inferential statistics. Introduction to data visualisation (histograms, box plots).
2. Calculate descriptive statistics and create visualisations in a shared virtual environment.
3. Create and interpret visualisations in spreadsheets to identify patterns, trends, and outliers.
4. Link visual outcomes to statistical concepts, such as central tendency and variability.

• Inferential Statistics Basics:

1. Sampling Concepts: sample vs population, sample size considerations, sampling bias awareness
2. Hypothesis Testing Introduction: null and alternative hypotheses, t-tests, p-value interpretation, confidence intervals, Type I and II errors

• Real-World Problem-Solving: A/B testing scenarios, customer segmentation, predictive analytics basics, quality assurance, business forecasting

How effective data handling contributes to accurate analysis and model development (AC 3.1):

• Data Quality Impact: garbage in, garbage out principle, accuracy and reliability, bias prevention, decision-making implications
• Data Integrity Principles: consistency maintenance, completeness requirements, validity checks, uniqueness constraints, timeliness considerations
• Business Value: cost of poor data quality, ROI from data cleaning, competitive advantages, regulatory compliance, stakeholder trust
• Best Practices: documentation standards, version control basics, testing procedures, validation protocols, quality metrics

Proficiency in data cleaning, feature engineering, and data transformation (AC 3.2):

• Handling missing data, outliers, and inconsistencies using Python or BI tools.
• Data wrangling techniques using Pandas and NumPy for high-performance processing.
• Handling categorical and numerical data transformations
• Exploratory Data Analysis (EDA) as part of the data cleaning pipeline.

Classifying different data preprocessing methods (AC 3.3):

• Cleaning Methods Classification:

1. Removal Methods: dropping nulls, removing duplicates, outlier elimination, feature selection, row/column filtering
2. Imputation Methods: statistical filling, forward/backward fill, interpolation, model-based imputation, domain-specific rules
3. Correction Methods: format standardisation, typo fixing, unit conversion, date parsing, encoding fixes

• Feature Engineering:

1. Creates new features and combines datasets using pandas functions or BI tools to produce a unified and enriched dataset.