Data Programming Foundations Bundle

Level 1:

1. Anatomy of a Python Program
   • Understanding Python: What are programming languages, advantages of Python, how Python works
   • Comments: Single-line comments, multi-line comments, best practices and benefits
   • Variables: What are they, assigning variables, naming rules, best practices
   • Printing: Calling print, use cases, f-strings, concatenation

   Get an introduction to Python by understanding how it works and basic commands.

2. Data Types
   • String functions: upper, lower, replace, split
   • Function Arguments
   • Boolean Operators: and, or, equality, not, xor, not equal, simulated nand, simulated nor
   • type()
   • Other Operators: Arithmetic operators, floor division, modulo

   Understand the different data types in Python and basic functions to identify and manipulate them.

3. IF and While Conditional Statements
   • If Statements
   • While Loops
   • Assignment & Comparison Operators

   Gain a strong understanding of conditional logic and use it to build loops.

4. Data Collections
   • Python Lists: What are they, creating lists, indexing
   • Reassigning Values in Lists
   • List Modification: append, insert, del
   • Using len()
   • Dictionaries: What are they, creating dictionaries, accessing values, adding values, reassigning values, removing values
   • Converting Dictionaries to Lists
   • Using in

   Learn how to create lists and dictionaries to store and handle data effectively.

5. For Loops
   • For Loops: What are they, creating for loops
   • Looping With Lists
   • Looping With Dictionaries
   • For Loops With range()

   Gain an understanding of for loops, learning how to loop through lists, dictionaries, and ranges.

Level 2:

1. Python Functions
   • Defining Functions
   • Definition Syntax
   • Using Parameters
   • Calling Functions
   • Returning Values

   Gain a strong understanding of functions in Python, learning how to define custom functions with parameters and returns.

2. Basic DataFrame Functions
   • The Pandas Library
   • Importing .CSVs - read_csv
   • DataFrame Summary Functions: head, tail, shape, columns, dtypes
   • DataFrame Manipulation Functions: drop, rename

   Understand what a DataFrame is through the Pandas library and discover basic summary and manipulation functions.

3. Data Indexing, Formatting & Cleaning
   • Retrieving DataFrame Items
   • DataFrame Analysis Functions: max, min, median, value_counts, sort_values
   • Formatting Functions: .upper, .lower, round
   • Exporting to .CSVs, .XLSXs

   Learn how to retrieve, analyse, and format DataFrames as well as export DataFrames to different formats.

Level 3:

1. Inspect Data with Pandas DataFrames
   • Descriptive Statistic Functions: describe, shape, info, kurtosis, skew
   • Transposing DataFrames

   Develop an understanding of how to analyze DataFrames to understand its contents.

2. Aggregating data
   • Using groupby
   • Using pivot_table
   • Using apply

   Learn how to aggregate data with grouping, pivot tables, and custom functions.

3. Plotting with Python
   • Using plot, relplot
   • Bar Charts
   • Pie Charts

   Understand how to transform data into powerful visualizations with Python.

Level 1:

1. Introduction to R
   • Understanding R: What is R, use cases, real life applications
   • Installation Guide
   • Navigation: Console, script editor, environment, running your code
   • R Concepts: Operators, printing, scripts, adding comments
   • Variables: Assigning variables, combining variables in calculations

   Gain a strong understanding of R as a language, its use cases, and basic commands.

2. Introduction to Dataframes
   • Dataframes: What are they, rows & records, columns & variables, data types in a dataframe, creating dataframes manually, creating dataframes with .CSVs
   • Basic Dataframe Functions: nrow, ncol, unique
   • Dataframe Notation: Selecting specific intersection, returning entire/multiple rows/columns
   • Summary Functions: summary, mean, sd

   Understand what DataFrames are and how they can be manipulated in R for data analysis.

3. Wrangling with Dataframes
   • Data Wrangling Functions: select, filter, mutate
   • Pipe Operator: Syntax, usage, benefits
   • Data Sorting Functions: arrange

   Learn how to wrangle data and use pipes to efficiently handle and manage data sets.

Level 2:

1. Working with Dataframes
   • Dataframe Functions: str, dim, summary, table, head, tail, colnames
   • Variable Types
   • Type Conversion Functions: as.numeric, as.character, as.factor, as.date

   Learn a variety of useful DataFrame analysis functions, understand data types, and learn how to convert between them.

2. Data Visualization in R
   • ggplot2: ggplot, geom_point, geom_line, labs, xlabs
   • Scatter plots
   • Line plots
   • Plot Customization

   Learn how to transform data into digestible visuals with R, learning how to use ggplot2 for a layered approach to visualization.

3. Bar Charts and Histograms in R
   • Bar Charts
   • Histograms
   • Grouping Series

   Understand how to create bar charts and histograms in R, learning their differences and use cases.

Level 3:

1. Exploratory Data Analysis
   • Additional Summary Functions: median, max, min
   • Missing Value Functions: is.na, anyNA, sapply
   • Invalid/Missing/Null Values

   Learn the importance of EDA through summary functions and learning how to deal with missing values.

2. Feature Engineering
   • Understanding Feature Engineering: What is it, why use it, applications in the real world
   • colSums
   • dplyr
   • If_else
   • .rm
   • Join Functions: inner_join, left_join, right_join, full_join

   Gain a strong understanding of feature engineering through conditional logic and joins.

3. Introduction to Linear Regression & Model Evaluation
   • Linear Regressions: What are they, independent & dependent variables, use cases, using lm
   • Model Summaries: coefficients table, residuals, R-squared value, adjusted R-squared value, F-statistic & p-value
   • Multivariate Regressions: What are they, use cases, using lm
   • Evaluation Metrics: MAE, MSE, RMSE, calculating metrics
   • Predictions: predict, interpretation

   Learn how to create and interpret regressions to build data-based models that can be used for predictions.

Level 1:

1. SQL Overview & DBeaver Installation
   • Understanding SQL: What is it, use cases, comparison to Excel, real world applications
   • SQL Query Structure
   • Database Structure: Tables & attributes, relationships & types of keys
   • DBeaver: What is it, installation guide, navigational guide
   • ERDs
   • Connecting Scripts to Databases

   Gain an introductory understanding of SQL, installing DBeaver and learning how databases are structured in SQL.

2. Basic SQL Queries - Filtering
   • SELECT Clause: Mathematical Operations, data retrieval
   • Comments: Single-line, multi-line
   • Aliases
   • WHERE Clause
   • Condition Clauses: IS, IS NOT, NOT IN, LIMIT, DISTINCT, ISNULL, NOTNULL
   • BETWEEN Clause

   Learn basic SQL queries and how to use them to extract specific data from databases.

3. String Clauses
   • LOWER
   • UPPER
   • SUBSTRINGS

   Understand how to manipulate strings using queries.

4. Aggregate Functions
   • Aggregation Functions: COUNT, SUM, AVG, MAX, MIN
   • ROUND
   • GROUP BY Clause
   • ORDER BY Clause
   • HAVING Clause

   Learn how to aggregate data in SQL through calculations, grouping, ordering, and filtering.

Level 2:

1. Views
   • Understanding Views: What are they, benefits
   • Creating Views
   • Using Pipes

   Learn the use cases for views in SQL, learning how to create them and use pipes.

2. Join Clauses
   • Understanding Joins: What are they, real-life examples
   • Types of Joins: INNER JOIN, LEFT JOIN, CROSS JOIN

   Understand the different types of joins in SQL and their use cases.

3. Union Clauses
   • UNION: Definition, use cases, rules
   • UNION ALL: Definition, use cases, rules

   Gain a strong understanding of the UNION vs UNION ALL clauses, understanding their differences and unique use cases.

4. Subqueries
   • Understanding Subqueries: What are they, use cases, real-life examples
   • Subqueries Practice: FROM, SELECT, WHERE

   Learn how to use subqueries for more complex data querying, practicing with FROM, SELECT, and WHERE.

Level 3:

1. Introduction to Cast & Dates
   • CAST: What is it, use cases, benefits, practice
   • Dates in SQL: Supported formats, DATE, DATETIME, JULIANDAY
   • STRFTIME function

   Learn how to use CAST in SQL, understanding how to deal with dates.

2. Data Definition Language (DDL)
   • Understanding DDL: What is it, use cases
   • Creating Tables
   • Populating Databases
   • Applying Table Constraints
   • Updating Tables: ALTER, DROP, UPDATE

   Gain an understanding of DDL, learning how to create and manipulate tables to populate databases.

3. CASE Clauses
   • Understanding CASE: What is it, use cases, real life examples
   • CASE WHEN Expressions

   Discover the CASE clause and learn how to build conditional logic in SQL to streamline categorization.