Monday, July 3, 2017

Analytics course New York

PROJECTS
FINAL PROJECT For the Analytics final project, you will collect, clean and
analyze a data set to solve a real world problem. From this
data, you will segment the data set and perform analysis.
Following your analysis, you will create both a dashboard and
presentation.
In order for your project to be considered a success, you will
complete the following steps
‣ Identify a problem
‣ Obtain the data
‣ Understand the data
‣ Prepare, clean and format the data
‣ Analyze the data
‣ Create a dashboard to display insights both numerically and
graphically.
‣ Present high level insights and the resulting actions to key
stakeholders.
As you complete elements of your final project, you will be
required to present materials and receive feedback from your
instructional team and classmates as well industry experts.
Our instructors are on hand to validate the feasibility and
manage the scope of your project.
6
Data Analytics
Units

UNITS
UNIT 1: DATA IN EXCEL ‣ The Value of Data Lesson 1
‣ Prepare Data in Excel Lesson 2
‣ Clean Data in Excel Lesson 3
‣ Dynamic Data Referencing Lesson 4
‣ Dynamic Data Aggregation Lesson 5
‣ Conditional Formatting and Aggregation Lesson 6
‣ The Value of Databases Lesson 7
‣ Query Large Databases Lesson 8
‣ Data Aggregation in SQL Lesson 9
‣ More Data Aggregation in SQL Lesson 10
‣ Efficient and Dynamic Queries Lesson 11
‣ Present Analysis Results Lesson 12
‣ Statistics to Validate Analysis Lesson 13
‣ Predictive Analysis Lesson 14
‣ Dashboard Design Lesson 15
‣ Track Metrics with Dashboards Lesson 16
‣ Effective Presentations with Data Lesson 17
‣ Flexible Session Lesson 18
‣ Flexible Session Lesson 19
‣ Final Project Presentation Lesson 20
UNIT 2: DATA IN SQL

UNIT 2: COMMUNICATION AND
DASHBOARD DESIGN
1 THE VALUE OF DATA
‣ Explain the value of data.
‣ Describe the analytics workflow
‣ Use mean, median, mode to describe data and find outliers
2 PREPARE DATA IN EXCEL
‣ Describe best practices in data cleaning and collection to
ensure the best results from data analysis
‣ Use complex nested logical functions [IF, OR, and AND] to
further manipulate data sets
‣ Manipulate data formats to gain insights on how to analyze
data
3 CLEAN DATA IN EXCEL
‣ Clean a large messy datasets by removing duplicate rows
and performing text manipulations
‣ Transform and rearrange columns and rows to structure
data for analysis
‣ Manipulate data formats to gain insights on how to analyze
data
4 DYNAMIC DATA REFERENCING
‣ Use data functions [VLOOKUP and HLOOKUP] to
manipulate data sets
‣ Use data functions [INDEX and MATCH] to look up values
in other tables
‣ Reconcile data values by joining and matching
5 DYNAMIC DATA AGGREGATION
‣ Summarize data using the pivot tables
‣ Use excel aggregation commands [‘Min’, ‘Max’, ‘Sum’,
‘Average’, ‘Count’, ‘Frequency’ to accomplish “count
distinct” ] and their conditional variants [‘COUNTIF’,
‘COUNTUNIQUE’, ‘COUNTA’, ‘COUNTIFS’,
‘COUNTBLANKS’] to summarize data sets
6 CONDITIONAL FORMATTING AND AGGREGATION
‣ Derive insights from data by highlight cells based on
conditionals
‣ Describe color theory and how it applies to data visualization
7
Data Analytics
Units Continued

DATA IN EXCEL 1
8
Data Analytics
Units Continued
GA.CO/AN
7 THE VALUE OF DATABASES AND SQL
‣ Use database schema to design appropriate queries
‣ Explain differences between relational databases (tabular
data storage) and document-based databases(key-value
pairs)
‣ Collect data using standard sql commands [Select, From,
Create, Update, Delete, Truncate, Drop]
8 QUERY LARGE DATABASES
‣ - Use advanced SQL commands [where, groupby, having,
orderby, limit] to filter data
‣ - Use joins to create relationships between tables to obtain
data
‣ - Use SQL boolean operators [AND and OR] and SQL
conditional operators [=,!=,>,<,IN and BETWEEN] to
obtain filtered data
9 DATA AGGREGATION IN SQL
‣ U- Create relationships between tables and data points
including has_many and many_to_many with join tables
using Joins [‘full’, and ‘union’]
‣ - Use sql conditional operators [=,!=,>,<,IN and
BETWEEN] and Null functions[‘is Null’, ‘ is not Null’ and
‘NVL’ ] to create boolean statements
‣ - Use sql mathematical functions [ABS, SIGN, MOD,
FLOOR, CEILING, ROUND, SQRT] to clean data
10 MORE DATA AGGREGATION IN SQL
‣ - Use aggregation commands [‘Min’, ‘Max’, ‘Sum’, ‘Average’,
‘Count’, ‘Count Distinct’] to summarize data sets
‣ - Use aggregation methods to determine trends from data
11 EFFICIENT AND DYNAMIC QUERIES
‣ Use CASE statements to structure data and create new
attributes
‣ - Use "WITH AS (" to combine subqueries into one query
‣ - Present analysis results and describe stakeholder
implications and insights
DATA IN SQL 2
9
Data Analytics
Units Continued

12 PRESENT ANALYSIS RESULTS
‣ Provide appropriate context of dataset
‣ Appropriately describe analysis techniques
‣ Present and describe stakeholder implications and insights
13 STATISTICS TO VALIDATE ANALYSIS
‣ Describe the value of descriptive and summary statistics in
understanding a dataset
‣ Create basic statistical measures to better understand the
range, average, and variance within a dataset
‣ Present the most salient statistics in order to provide
context to your audience
‣ Explain the importance of segmentation
14 PREDICTIVE ANALYSIS
‣ Describe the value of inferential statistics and predictive
analysis
‣ Review linear regression and Ordinary Least Squares (OLS)
‣ Use sample data to make predictions about a larger
population
15 DASHBOARD DESIGN
‣ Use scatter plots and bar graphs to visualize data
‣ Apply the best practices to build a dashboard
‣ Demonstrate good visual design without overloading their
dashboard with complexity
16 TRACK METRICS WITH DASHBOARDS
‣ Use bubble graphs to visualize data
‣ Apply the best practices to build a dashboard
‣ Contextualize data analysis by creating Tableau dashboards
[includes charts + conditional formatting] with supporting
information specific to the dataset
17 EFFECTIVE PRESENTATIONS WITH DATA
‣ Display geocoded information in Tableau
‣ Provide real-world context for basis of analysis
‣ Provide localized context for implications of findings
‣ Deliver short, effective presentations
DATA ANALYSIS 2 (CONTINUED)
DATA COMMUNICATION 3
10
Data Analytics
Units Continued

18 FLEXIBLE SESSION
‣ Focus on a topic selected by the instructor/class in order to
provide deeper insight into a specific area of data analysis
19 FLEXIBLE SESSION
‣ Focus on a topic selected by the instructor/class in order to
provide deeper insight into a specific area of data analysis
20 FINAL PROJECT PRESENTATION
‣ Present final project presentation to class

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