November 18

• Define the concept of data
• Describe basic data variable types (Boolean, numeric, string)

Novemeber 19

• Describe basic structures used in data analytics (Tables, rows, columns, lists)
• Describe data categories (Qualitative, quantitative, structured, unstructured, metadata, big data)

November 20

• Import, store, and export data: Fundamental understanding of ETL (extract, transform and load) processes, data manipulation tools (SQL, R, Python, Microsoft Excel including aspects of Power Query), and common data storage file formats (delimited data files, XML, JSON)
• Clean data: Purpose and common practices (handling NULL, special characters, trimming spaces, inconsistent formatting, removing duplicates, imputing data, etc.); validating data

November 21

• Organize data: Purpose and common practices (sorting, filtering, slicing, transposing, appending, truncating, etc.)
• Aggregate data: Purpose and common practices (grouping, joining/merging, summarizing, pivoting, etc.)

November 22

• Describe and differentiate between types of data analysis: Descriptive analysis, diagnostic analysis, hypothesis testing, predictive analysis, prescriptive analysis
• Describe and differentiate between data aggregation and interpretation metrics: Searching, filtering, unique values, aggregate functions such as Sum, Max, Min, Count, Avg/Mean, Mode, Median, Std Dev

November 23

• Describe and differentiate between exploratory data analysis methods: Identify data relationships, describe data drilling concepts (granularity, etc.), describe data mining concepts (anomalies, correlation analysis, patterns, outliers, etc.)
• Evaluate and explain the results of data analyses: Calculate trends, determine expected values, interpret results of predictive models, p-values, t-tests, and regression analyses

November 24

• Define and describe the role of artificial intelligence in data analysis: Define artificial intelligence, machine learning, and algorithm; describe how AI is used in data analysis; describe how machine learning algorithms are used in data analysis (Note: Specific algorithms are out of scope)

November 25

• Report data: Effectively display information in tables and charts; explain when and why to disaggregate data

November 26

• Create visualizations from data: Identify data visualization practices that minimize the potential for misinterpretation; identify visualization types that represent the underlying data structure and analysis questions (including comparison, time/trend, part-to-whole, relationship, distribution, correlation graphs, box and whisker diagram, scatter chart, scatter plot, bar chart, Sankey diagram, histogram, pie chart, column chart, etc.)

November 27

• Derive conclusions from a data visualization: Translate a visual representation of data into words; identify differences between claims based on an analysis and its graphical representation

November 28

• Describe data privacy laws and best practices: GDPR, FERPA, HIPAA, IRB, PCI, etc.

November 29

• Describe best practices for responsible data handling: Methods of handling PII, securing data, and protecting anonymity within small data sets; importance of anonymizing data; trade-offs when balancing interpretability and accuracy; shortcomings of making population-level generalizations with limited sample data

November 30

• Given a scenario, describe types of bias that affect the collection and interpretation of data: Confirmation bias, human cognitive bias, motivational bias, sampling bias; selecting visualizations/data representations to avoid bias

December 1

• Familiarizing the students with the exam format, including the types of questions, time limits, and scoring system.
• Solving past exam papers with the assistance of the trainer.
• Make the students involved in a series of steps that they need to follow in order to sign up for the IT Specialist - Data Analytics Examination.