Javascript: predefined variables

Hi all,
I am having trouble starting this problem. I would love to hear any suggestions. Thank you!
Using the data set as a pre-defined variable in your program, write code that uses the dataset to print the FIRST NAMES ONLY of people who have BOTH above average English grades AND below average age from the dataset.Requirements:
Cannot use any array type built-in functions except filter(), map(), and reduce().
var dataSet = [
{
“name”: “Maura Glass”,
“age”: 60,
“math”: 97,
“english”: 63,
“yearsOfEducation”: 4
},
{
“name”: “James Gates”,
“age”: 55,
“math”: 72,
“english”: 96,
“yearsOfEducation”: 10
},
{
“name”: “Mills Morris”,
“age”: 26,
“math”: 83,
“english”: 77,
“yearsOfEducation”: 10
},
{
“name”: “Deena Morton”,
“age”: 57,
“math”: 63,
“english”: 63,
“yearsOfEducation”: 10
},
{
“name”: “Edith Roth”,
“age”: 38,
“math”: 79,
“english”: 94,
“yearsOfEducation”: 10
},
{
“name”: “Marva Morse”,
“age”: 31,
“math”: 93,
“english”: 78,
“yearsOfEducation”: 9
},
{
“name”: “Etta Potts”,
“age”: 48,
“math”: 57,
“english”: 93,
“yearsOfEducation”: 7
},
{
“name”: “Tate Moss”,
“age”: 22,
“math”: 83,
“english”: 64,
“yearsOfEducation”: 8
},
{
“name”: “Sanders Burris”,
“age”: 27,
“math”: 65,
“english”: 66,
“yearsOfEducation”: 5
},
{
“name”: “Latoya Malone”,
“age”: 35,
“math”: 100,
“english”: 100,
“yearsOfEducation”: 5
},
{
“name”: “Wade Foreman”,
“age”: 25,
“math”: 76,
“english”: 87,
“yearsOfEducation”: 10
},
{
“name”: “Miller Valentine”,
“age”: 31,
“math”: 56,
“english”: 89,
“yearsOfEducation”: 6
},
{
“name”: “Rita Olson”,
“age”: 53,
“math”: 100,
“english”: 52,
“yearsOfEducation”: 6
},
{
“name”: “Potter Newton”,
“age”: 29,
“math”: 91,
“english”: 75,
“yearsOfEducation”: 5
},
{
“name”: “Madeline Bartlett”,
“age”: 23,
“math”: 60,
“english”: 74,
“yearsOfEducation”: 10
},
{
“name”: “Tamara Tran”,
“age”: 46,
“math”: 73,
“english”: 78,
“yearsOfEducation”: 4
},
{
“name”: “Elena Evans”,
“age”: 43,
“math”: 60,
“english”: 82,
“yearsOfEducation”: 10
},
{
“name”: “Cote Merrill”,
“age”: 55,
“math”: 86,
“english”: 63,
“yearsOfEducation”: 7
},
{
“name”: “Madeleine Brennan”,
“age”: 52,
“math”: 82,
“english”: 88,
“yearsOfEducation”: 4
},
{
“name”: “Alford Weber”,
“age”: 38,
“math”: 71,
“english”: 85,
“yearsOfEducation”: 4
},
{
“name”: “Kirsten Daniel”,
“age”: 35,
“math”: 86,
“english”: 61,
“yearsOfEducation”: 8
},
{
“name”: “Melton Chan”,
“age”: 26,
“math”: 55,
“english”: 96,
“yearsOfEducation”: 4
},
{
“name”: “Mcmahon Woodward”,
“age”: 54,
“math”: 56,
“english”: 63,
“yearsOfEducation”: 9
},
{
“name”: “Helga Monroe”,
“age”: 29,
“math”: 79,
“english”: 92,
“yearsOfEducation”: 5
},
{
“name”: “Patricia Herrera”,
“age”: 46,
“math”: 94,
“english”: 99,
“yearsOfEducation”: 10
},
{
“name”: “Mccullough Lambert”,
“age”: 56,
“math”: 65,
“english”: 96,
“yearsOfEducation”: 8
},
{
“name”: “Haynes Davidson”,
“age”: 60,
“math”: 86,
“english”: 50,
“yearsOfEducation”: 5
}
]

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