New Data Analysis Learning Objectives

data-analysis/lesson01.rst

@@ -0,0 +1,27 @@
+Intro to Data Analysis
+======================
+
+Goals
+-----
+
+- Understand what the different stages of a data analysis project are.
+- Name different data analysis methods and techniques and speak to when to appropriately use each one.
+- Be able to review a dataset and speak to what metrics within the dataset are of importance to the analysis.
+
+Objectives
+----------
+
+A Data Analysis Project
+^^^^^^^^^^^^^^^^^^^^^^^
+
+- Be able to list the stages of the data analysis project lifecycle.
+- Speak to the questions asked at each stage in the data analysis project lifecycle.
+- Make connections between the available data and what might be needed to address the business issue at hand.
+- Practice the aspects of a data analyst's role that involve communicating with business leaders.
+
+Different Types of Data Analysis
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Gain awareness of what questions data analysts may need to ask before even approaching a project.
+- Define Key Performance Indicators and be able to distinguish KPIs within a dataset.
+- Define different methods and techniques used in data analytics and be able to pick the apppropriate situation for the situation at hand.

data-analysis/lesson02.rst

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+Exploratory Data Analysis with Google Sheets
+============================================
+
+Goals
+-----
+
+- Understand what exploratory data analysis is and why it is an important step in the data analysis process.
+- Be able to perform an exploratory analysis on a dataset with Google Sheets
+
+Objectives
+----------
+
+The EDA Methodology
+^^^^^^^^^^^^^^^^^^^
+
+- find patterns and connections, answer hypotheses and find solutions from data
+- Know how to uncover a data structure, aka get to know the dataset
+
+EDA Processes in Google Sheets
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Calculate summary statistics
+- Create visualizations
+- Locate outliers

data-analysis/lesson03.rst

@@ -0,0 +1,19 @@
+Cleaning Data with Google Sheets
+================================
+
+Goals
+-----
+
+- Understand common data cleaning techniques used to remove data
+- Identify different use cases for cleaning a data set
+- Identify corrupted data and handle it appropriately
+- Recognize the four types of “dirty” data: Missing, Irregular, Unnecessary, and Inconsistent
+
+Objectives
+----------
+
+- Remove duplicate data
+- Handle mis-formatted data
+- Use Google Sheets to identify whether or not any “dirty” data is present in a dataset. 
+- Use Google Sheets to handle “dirty” data and clean the dataframe.
+- Present and talk about data issues and how to address them. Be able to take into account the type of “dirty” data and project context when addressing data issues.

data-analysis/lesson05.rst

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+Control Flow
+============
+
+Goals
+-----
+
+- Use basic control flow structures
+
+Objectives
+----------
+
+Control Flow
+^^^^^^^^^^^^
+
+- Use conditional statements: ``if``, ``else``, and ``elif``
+- Use loops: ``for`` and ``while``
+- Modify the flow of control in loops using ``break`` and ``continue``
+- Explain the difference between ``break`` and ``continue``
+
+

unit2-data-analysis/lesson05.rst → data-analysis/lesson06.rst

@@ -1,16 +1,22 @@
-Lesson 5: Lists
-===============
+Python Collections
+==================
 
 Goals
 -----
 
 - Understand that lists are ordered collections similar to arrays
 - Understand the syntax for creating new lists
 - Work with lists using bracket notation, iteration, and common list methods
+- Create and use Python dictionaries
+- Create and use Python tuples
+- Recognize the benefits and drawbacks of dictionaries vs. lists vs. tuples
 
 Objectives
 ----------
 
+Lists
+^^^^^
+
 - Assign a list literal to a variable
 - Create lists that store multiple values of the same or different data types
 - Use bracket notation and index values to access or modify one element in a
@@ -27,4 +33,28 @@ Objectives
 - Create lists that store other lists (i.e. multi-dimensional lists).
 - Access elements within multi-dimensional lists.
 - Use ``join()`` to convert a list of string elements into a single string.
-- Use the ``in`` operator to check if a value is present in a list.
+- Use the ``in`` operator to check if a value is present in a list.
+
+Dictionaries
+^^^^^^^^^^^^
+
+- Understand that dictionaries are collections of properties stored as
+ **key/value pairs**
+- Recognize that dictionaries are *unordered* collections of data values.
+- Create and assign dictionary values using ``{ }``
+- Describe the allowed data types for **keys** and **values**
+- Access and/or change the value of a key by using bracket notation
+- Use bracket notation to add new key/value pairs to a dictionary
+- Use the ``.keys()``, ``.values()``, and ``.items()`` methods to retrieve a
+ list of the keys, values, or key/value pairs from a dictionary
+- Use a loop to iterate through the keys, values, or key/value pairs of a
+ dictionary
+- Utilize dictionaries as parameters, arguments, and return values of
+ functions
+
+Tuples
+^^^^^^
+
+- Understand that tuples are ordered collections of properties
+- Understand that tuples are immutable
+- Create and assign tuple values using ``()`` syntax

unit2-data-analysis/lesson03.rst → data-analysis/lesson07.rst

@@ -1,3 +1,4 @@
+
 Functions
 =========
 

data-analysis/lesson08.rst

@@ -0,0 +1,16 @@
+Exploratory Data Analysis with Pandas
+=====================================
+
+Goals
+-----
+
+- Use Python and pandas for EDA
+- Understand pandas data structures
+- Learn and use common pandas functions
+- Understand external Python Libraries
+
+Objectives
+----------
+
+- Cover statistics with pandas
+- Import pandas and numpy

data-analysis/lesson09.rst

@@ -0,0 +1,10 @@
+Cleaning Data with Pandas
+=========================
+
+Goals
+-----
+
+- Use Pandas to locate and resolve issues related to all four types of dirty data: missing data, irregular data, unnecessary data, and inconsistent data.
+
+Objectives
+----------

data-analysis/lesson10.rst

@@ -0,0 +1,25 @@
+Data Manipulation
+=================
+
+Goals
+-----
+
+Aggregate data accross multiple columns (mean, median, mode)
+Append data: stack or concatenate multiple datasets with the .concat function
+Recode and map values within a column to new values by providing conditional formatting
+Group data together with the .groupby function
+Merge columns together based on the provided column or indices
+Restructure data long vs. wide with pandas melt and pivot functionality
+
+Objectives
+----------
+
+Summing
+Aggregating
+Merging
+Appending
+Recoding
+Creating New Variables
+Removing Duplicates
+Restructure Data Long Vs. Wide
+Missing Data

data-analysis/lesson11.rst

@@ -0,0 +1,12 @@
+Python Data Visualization
+=========================
+
+Goals
+-----
+
+Use Matplotlib and Seaborn to create visualizations
+Differentiate between chart styles and pros and cons to each style. Scatter plots, histograms, bar charts, pie charts, etc.
+Talk about what to do and what not to do with data visualizations
+
+Objectives
+----------

data-analysis/lesson12.rst

@@ -0,0 +1,17 @@
+SQL Part 1: Introduction to SQL and Azure Data Studio
+=====================================================
+
+Goals
+-----
+
+Understand what a relational database is and how SQL is used to manage it.
+Write SQL queries to perform read operations.
+Understand different types of table relationships and what foreign/primary keys are.
+
+
+Objectives
+----------
+
+- Explain what relational databases are used for and what SQL is
+- Explain what a table is
+- Perform CRUD (Create, Read, Update, Delete) operations using SQL queries

unit2-data-analysis/lesson13.rst → data-analysis/lesson13.rst

@@ -1,12 +1,13 @@
-Lesson 13: SQL: Dates, Views and Derived columns
-================================================
+SQL Part 2: String and Date Functions
+=====================================
 
 Goals
 -----
 
-- Work with different ways of storing dates in SQL
-- Create views 
-- Create derived columns in SQL
+Use common string functions in queries, such as LEFT, RIGHT, and REPLACE.
+Use common date functions in queries, such as DAY, DATEADD, and SYSDATETIME.
+Use aggregate functions in queries, such as SUM, MAX, and MIN.
+
 
 Objectives
 ----------

data-analysis/lesson14.rst

@@ -0,0 +1,13 @@
+SQL Part 3: Joins
+=================
+
+Goals
+-----
+
+Understand what a join is.
+Write queries that use inner, full, left, and right joins.
+Explain the differences between the types of joins listed above.
+Use the HAVING clause with joins to filter result sets.
+
+Objectives
+----------

data-analysis/lesson15.rst

@@ -0,0 +1,12 @@
+Subqueries, UNION, INTERSECT, EXCEPT
+====================================
+
+Goals
+-----
+
+Write nested subqueries.
+Articulate the differences between UNION, INTERSECT, and EXCEPT operators.
+Use all three of the above operators in queries.
+Use the IN keyword with a WHERE clause.
+Write correlated subqueries.
+

data-analysis/lesson16.rst

@@ -0,0 +1,12 @@
+Schemas and CRUD operations
+===========================
+
+Goals
+-----
+
+What is a schema?
+Create new tables.
+Add records to tables.
+Edit an existing record.
+Delete records from a table.
+Delete a table.

unit2-data-analysis/lesson15.rst → data-analysis/lesson17.rst

@@ -1,5 +1,5 @@
-Lesson 15: Working with databases in Python
-===========================================
+Working with Databases in Python
+================================
 
 Goals
 -----

data-analysis/lesson18.rst

@@ -0,0 +1,7 @@
+Introduction to Visualization with Tableau
+==========================================
+
+Goals
+-----
+
+Objectives: Workspace navigation & layout, connect to data, understand how Tableau handles (interprets) data, and build your first chart. 

unit2-data-analysis/lesson19.rst → data-analysis/lesson19.rst

@@ -1,12 +1,10 @@
-Visualization w/ Tableau 1
-==========================
+Data Preparation in Tableau
+===========================
 
 Goals
 -----
 
-- Setup Tableau
-- Understand measures and dimensions
-- Create simple worksheets
+Objectives: Filtering, sorting, groups, hierarchies, and sets
 
 Objectives
 ----------

unit2-data-analysis/lesson20.rst → data-analysis/lesson20.rst

@@ -1,11 +1,10 @@
-Visualization w/ Tableau 2
-==========================
+Tableau Part 3
+==============
 
 Goals
 -----
 
-- Understand how to use functions in Tableau
-- Setup Tableau public profile
+Objectives: Creating basic calculations, parameters
 
 Objectives
 ----------

data-analysis/lesson21.rst

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+Stories and Dates in Tableau
+============================
+
+Objectives: dates, dashboard tips, and stories

data-analysis/lesson22.rst

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+Introduction to Statistics and Data Modeling
+============================================
+
+Understand how to use statistics to analyze data
+Learn the basics of data modeling
+Practice describing data with modeling

data-analysis/lesson23.rst

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+Linear Regression
+=================
+
+- Learn what regression analysis is
+- Understand what multicollinearity is
+- Practice predicting trends from models

data-analysis/lesson24.rst

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+Final Project Fair
+==================