Foundations of data analytics with Python

Clarify the use of Python variables, data types, and control structures

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. 

Apply basic operations

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

Explain how data structures and libraries like NumPy and Pandas are used for data manipulation and analysis.

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

Analyse data and apply appropriate statistical concepts to practical scenarios

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

Demonstrate an understanding of descriptive and inferential techniques and their use in real-world data problems.

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

Explain how effective data handling contributes to accurate analysis and model development.

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

Demonstrate proficiency in data cleaning, feature engineering, and data transformation.

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.

Classify different data preprocessing methods.

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.