Key roles for a successful analytical project | steps requires to analyze the datasets | Data Analytical project roles
Key roles for a successful analytics project:
Business user:This person can consult and advice the project team on the context of the project, the value of the result. Usually business analyst, line manager, or deep subject matter expert.
Project Sponsor: Provide impetus and requirements for the project and defined business problem. Generally provides the funding and gauges the degree of value from the final outputs of the working team.
Project Manager: Ensures that key milestones and objectives are met on time and at the expected quality.
Business Intelligence Analyst: Creates dashboards and reports, and have knowledge of the data feeds and sources. Deep understanding of the data, KPI's (Key performance indicators), key metrics and BI reporting perspective.
Database administrator (DBA): Provisions and configures database environment to support the analytics needs of the working team.
Data Engineer: Leverages deep technical skills to assists with tuning SQL queries for data management and data extraction. They work closely with the data scientists.
Data Scientist: Provide subject matter expertise for analytical techniques, modeling techniques, and applying valid analytical techniques to given business problem.
Intermediate R programming from Datacamp In this chapter, we will learn about conditional statements, loops, and functions to power the R scripts. Conditional and control flow # Equality: # Comparison of logicals TRUE == FALSE # Comparison of numerics -6 * 14 != 17 - 101 # Comparison of character strings "useR" == "user" # Compare a logical with a numeric TRUE == 1  TRUE # Greater and less than: # Comparison of numerics -6 * 5 + 2 >= -10 + 1 # Comparison of character strings "raining" <= "raining dogs" # Comparison of logicals TRUE > FALSE  TRUE # Compare vectors: # The linkedin and facebook vectors have already been created for you linkedin <- c ( 16 , 9 , 13 , 5 , 2 , 17 , 14 ) facebook <- c ( 17 , 7 , 5 , 16 , 8 , 13 , 14 ) # Popular days linkedin > 15 # Quiet days linkedin <= 5 # LinkedIn more popular than Facebook linkedin > facebook  FALSE TRUE TRUE FALSE FAL
An introduction to R from Datacamp In this chapter, we will learn about the basics and widely used data structures in R like vectors, factors, lists, and data frames. Arithmetic operations: # An addition 5 + 5  10 # A subtraction 5 - 5  0 # A multiplication 3 * 5  15 # A division ( 5 + 5 ) / 2  5 # Exponentiation 2 ^ 5  32 # Modulo 28 %% 6  4 Variable Assignment: # Assign a value to the variable my_apples my_apples <- 5 # Fix the assignment of my_oranges my_oranges <- 6 # Create the variable my_fruit and print it out my_fruit <- my_apples + my_oranges my_fruit  11 Basic data types in R: # Declare variables of different types my_numeric <- 42 my_character <- "universe" my_logical <- FALSE # Check class of my_numeric class ( my_numeric )  "numeric" # Check class of my_character class ( my_character )  "character" # Check class of my_logical class ( my_logical )  "l