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DATA SCIENCE (Data (Tools (Teradata, Cloudera, MapR, Datameer), NoSQL…
DATA SCIENCE
Data
CSV
SQL
NoSQL
MongoDB
APIs
Tools
Teradata
Cloudera
MapR
Datameer
Storage
Statistics
Correlation
Pearsons R
Regression
Linear
Multiple
Z-test
T-test
ANOVA (F-test)
Code
Python
Pandas
R
Data Analysis
Tools
Terradata
Process
Requirements
Collection
Processing
Cleaning
Exploratory analysis
Modeling and algorithms
Data product
Communication
#
Visualisation
Tools
Tableau
Google Data Studio
Power BI
Quantitative messages
Time-series
Ranking
Part-to-whole
Deviation
Frequency distribution
Correlation
Nominal comparison
Geographic
Problem Solving
Techniques for analyzing quantitative data
Check raw data for anomalies prior to performing your analysis
Re-perform important calculations, such as verifying columns of data that are formula driven
Confirm main totals are the sum of subtotals
Check relationships between numbers that should be related in a predictable way, such as ratios over time
Normalize numbers to make comparisons easier, such as analyzing amounts per person or relative to GDP or as an index value relative to a base year
Break problems into component parts by analyzing factors that led to the results, such as DuPont analysis of return on equity.
Business
ROI Optimisation
Mathematics
Machine Learning