diff --git a/go/src/student_submissions/movielens/Liliana Vera b/go/src/student_submissions/movielens/Liliana Vera
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/go/src/student_submissions/movielens/Liliana Vera	
@@ -0,0 +1 @@
+
diff --git a/go/src/student_submissions/movielens/Liliana_vera/movielesns go b/go/src/student_submissions/movielens/Liliana_vera/movielesns go
new file mode 100644
index 00000000..9bb3c5f6
--- /dev/null
+++ b/go/src/student_submissions/movielens/Liliana_vera/movielesns go	
@@ -0,0 +1,313 @@
+package main
+
+import (
+	"encoding/csv"
+	"fmt"
+	"log"
+	"os"
+	"runtime"
+	"strconv"
+	"strings"
+	"time"
+
+	"github.com/kfultz07/go-dataframe"
+)
+
+// Leer y particionar archivo
+// Function to split a csv file into small files.
+// You provided a name for the file, the number of chuncks wich it will be divided and the directory
+// where the file is located and the new ones will be saved.
+func SplitBigFile(file_name string, number_of_chunks int, directory string) []string {
+	t1 := time.Now()
+	data := ReadCsv(file_name, directory)
+
+	//Extrae el encabezado para cada CSV
+	header := data[0]
+	//Quita el encabezado antes de dividir
+	data = data[1:]
+
+	fmt.Printf("%v rows in file %s\n", len(data), file_name)
+	rowsPerFile := len(data) / number_of_chunks
+	var filesCreated []string
+
+	for i := 0; i < number_of_chunks; i++ {
+		tempName := file_name + "_" + fmt.Sprintf("%02d", i+1)
+		fmt.Printf("%s\n", tempName)
+		path := directory
+		fmt.Printf("%s\n", path)
+		tempData := append([][]string{header}, data[i*rowsPerFile:(i+1)*rowsPerFile]...)
+		WriteCsv(tempData, tempName, path)
+		filesCreated = append(filesCreated, tempName)
+	}
+	tf := time.Since(t1).Seconds()
+	println("Executed in:", tf, "seconds")
+	return filesCreated
+}
+
+// Open and read a csv file and returns the content.
+func ReadCsv(fileName string, directory string) [][]string {
+	file, err := os.Open(directory + fileName + ".csv")
+
+	if err != nil {
+		log.Fatalf("Error opening file: %s", err)
+	}
+	defer file.Close()
+
+	csvReader := csv.NewReader(file)
+
+	data, err := csvReader.ReadAll()
+
+	if err != nil {
+		log.Fatalf("Error extracting data from file %v: %s", fileName, err)
+	}
+	return data
+}
+
+// Create a csv file with the name and data provided in the path defined.
+func WriteCsv(data [][]string, name string, path string) {
+	csvFile, err := os.Create(path + name + ".csv")
+	if err != nil {
+		log.Fatalf("Error creating new csv file %v: %s", name, err)
+	}
+	defer csvFile.Close()
+
+	writer := csv.NewWriter(csvFile)
+	defer writer.Flush()
+
+	err = writer.WriteAll(data)
+	if err != nil {
+		log.Fatalf("Error writing new csv file %v: %s", name, err)
+	}
+
+	fmt.Printf("File %s has been created with %v rows\n", name, len(data))
+}
+
+func main() {
+	files := SplitBigFile("ratings", number_of_workers(), "C:\\Users\\configurar\\Documents\\2024\\CursoGo\\peliculasEQUIPO\\")
+	fmt.Println(files)
+	Mt_FindRatingsMaster()
+}
+
+// Definir el numero de hilos
+
+func number_of_workers() int {
+	// agregar metodo para leer el numero de workers
+	//return 10
+	return runtime.GOMAXPROCS(0)
+}
+
+// Definir la funcion que ejecutara el worker
+
+/*func ReadCsvToDataframe(aFileName string) dataframe.DataFrame {
+    file, _ := os.Open(aFileName)
+    defer file.Close()
+
+    // Leer el archivo CSV y crear el DataFrame
+    df := dataframe.ReadCSV(file)
+    return df
+}*/
+
+func ReadCsvToDataframe(filePath string) dataframe.DataFrame {
+	path := "C:\\Users\\configurar\\Documents\\2024\\CursoGo\\peliculasEQUIPO\\"
+	df := dataframe.CreateDataFrame(path, filePath)
+	return df
+}
+
+// w: el numero del worker
+// ci: el canal
+func Mt_FindRatingsWorker(w int, ci chan int, knowGenders []string, ca *[][]int, va *[][]float64, movies dataframe.DataFrame) {
+	aFileName := "ratings_" + fmt.Sprintf("%02d", w) + ".csv"
+	println("Worker  ", fmt.Sprintf("%02d", w), "  is processing file ", aFileName, "\n")
+
+	ratings := ReadCsvToDataframe(aFileName) // modificar para devolver un dataframe
+	ng := len(knowGenders)
+	start := time.Now()
+
+	// import all records from the movies DF into the ratings DF, keeping genres column from movies
+	//df.Merge is the equivalent of an inner-join in the DF lib I am using here
+	ratings.Merge(&movies, "movieId", "genres")
+
+	// We only need "genres" and "ratings" to find Count(Ratings | Genres), so keep only those columns
+	grcs := [2]string{"genres", "rating"} // grcs => Genres Ratings Columns
+	grDF := ratings.KeepColumns(grcs[:])  // grDF => Genres Ratings DF
+	for ig := 0; ig < ng; ig++ {
+		for _, row := range grDF.FrameRecords {
+			if strings.Contains(row.Data[0], knowGenders[ig]) {
+				(*ca)[ig][w-1] += 1
+				v, _ := strconv.ParseFloat((row.Data[1]), 32) // do not check for error
+				(*va)[ig][w-1] += v
+			}
+		}
+	}
+	duration := time.Since(start)
+	fmt.Println("Duration = ", duration)
+	fmt.Println("Worker ", w, " completed")
+
+	// notify master that this worker has completed its job
+	ci <- 1
+}
+
+// 1.0 Definir la funcion master que ejecutará el programa (1.1 Unir el resultado de todos los workers)
+
+func Mt_FindRatingsMaster() {
+	fmt.Println("In MtFindRatingsMaster")
+	start := time.Now()
+	nf := number_of_workers()
+
+	//SplitBigFile("ratings", nf, "C:\\Users\\configurar\\Documents\\2024\\CursoGo\\peliculasEQUIPO\\")
+
+	// kg is a 1D array that contains the Known Genres
+	kg := []string{"Action", "Adventure", "Animation", "Children", "Comedy", "Crime", "Documentary",
+		"Drama", "Fantasy", "Film-Noir", "Horror", "IMAX", "Musical", "Mystery", "Romance",
+		"Sci-Fi", "Thriller", "War", "Western", "(no genres listed)"}
+
+	ng := len(kg) // number of known genres
+	// ra is a 2D array where the ratings values for each genre are maintained.
+	// The columns signal/maintain the core number where a worker is running.
+	// The rows in that column maintain the rating values for that core and that genre
+	ra := make([][]float64, ng)
+	// ca is a 2D array where the count of Ratings for each genre is maintained
+	// The columns signal the core number where the worker is running
+	// The rows in that column maintain the counts for that that genre
+	ca := make([][]int, ng)
+	// populate the ng rows of ra and ca with nf columns
+	for i := 0; i < ng; i++ {
+		ra[i] = make([]float64, nf)
+		ca[i] = make([]int, nf)
+	}
+	var ci = make(chan int) // create the channel to sync all workers
+	movies := ReadCsvToDataframe("movies.csv")
+	println("Lectura completa del movies.csv\n")
+	// run FindRatings in 10 workers
+	for i := 0; i < nf; i++ {
+		go Mt_FindRatingsWorker(i+1, ci, kg, &ca, &ra, movies)
+	}
+	// wait for the workers
+	iMsg := 0
+	go func() {
+		for {
+			i := <-ci
+			iMsg += i
+		}
+	}()
+	for {
+		if iMsg == nf {
+			break
+		}
+	}
+	// all workers completed their work. Collect results and produce report
+	locCount := make([]int, ng)
+	locVals := make([]float64, ng)
+	locPromedio := make([]float64, ng)
+	for i := 0; i < ng; i++ {
+		for j := 0; j < nf; j++ {
+			locCount[i] += ca[i][j]
+			locVals[i] += ra[i][j]
+		}
+		locPromedio[i] = locVals[i] / float64(locCount[i])
+	}
+	for i := 0; i < ng; i++ {
+		fmt.Println(fmt.Sprintf("%2d", i), "  ", fmt.Sprintf("%20s", kg[i]), "  ", fmt.Sprintf("%8d", locCount[i]), " ", fmt.Sprintf("%.2f", locPromedio[i]))
+	}
+	duration := time.Since(start)
+	fmt.Println("Duration = ", duration)
+	println("Mt_FindRatingsMaster is Done")
+}#import Pkg; Pkg.add("CSV")
+#import Pkg; Pkg.add("DataFrames")
+
+using DataFrames
+using CSV
+using Parquet
+using Printf
+
+# Función para dividir el archivo ratings en 10 partes y guardarlas en formato Parquet
+function generate_small_files(ratings_file::String, output_prefix::String, output_dir::String)
+    println("Dividiendo archivo de ratings en partes...")
+    
+    # Leer el archivo completo de ratings
+    data = CSV.read(ratings_file, DataFrame)
+    total_rows = nrow(data)
+    num_chunks = 10  # Dividir en 10 partes
+    chunk_size = ceil(Int, total_rows / num_chunks)
+
+    # Crear y guardar cada chunk en archivos separados
+    for i in 1:num_chunks
+        start_row = (i - 1) * chunk_size + 1
+        end_row = min(i * chunk_size, total_rows)
+        chunk = data[start_row:end_row, :]
+
+        output_path = joinpath(output_dir, "$(output_prefix)_ratings$(lpad(i, 2, '0')).parquet")
+        Parquet.write_parquet(output_path, chunk)
+        println("Archivo guardado: $output_path con $(nrow(chunk)) filas")
+    end
+end
+
+# Función principal para procesar archivos de ratings y cruzarlos con movies
+function find_ratings_master(input_dir::String, output_dir::String)
+    nF = 10  # Número de archivos ratings
+    prqDir = output_dir
+
+    # Lista de géneros de películas
+    genres = ["Action", "Adventure", "Animation", "Children", "Comedy", "Crime", "Documentary",
+              "Drama", "Fantasy", "Film-Noir", "Horror", "IMAX", "Musical", "Mystery", "Romance",
+              "Sci-Fi", "Thriller", "War", "Western", "(no genres listed)"]
+    ng = length(genres)
+
+    # Arrays para acumular los resultados de calificaciones por género
+    rating_sum = zeros(ng, nF)
+    count_sum = zeros(Int, ng, nF)
+
+    # Leer el archivo movies.csv y mantener solo las columnas necesarias
+    movies_path = joinpath(input_dir, "movies.csv")
+    df_movies = CSV.read(movies_path, DataFrame)
+    df_movies = df_movies[:, [:movieId, :genres]]
+
+    # Procesar cada archivo de ratings
+    for i in 1:nF
+        rating_file = joinpath(prqDir, "ratings_ratings$(lpad(i, 2, '0')).parquet")
+        println("Procesando archivo: $rating_file")
+
+        if isfile(rating_file)
+            df_ratings = DataFrame(read_parquet(rating_file))
+            rating_sum[:, i], count_sum[:, i] = process_ratings(ng, genres, df_movies, df_ratings)
+        else
+            println("Archivo no encontrado: $rating_file")
+        end
+    end
+
+    # Sumar resultados finales por género y mostrarlos
+    for i in 1:ng
+        total_rating = sum(rating_sum[i, :])
+        total_count = sum(count_sum[i, :])
+        promedio = total_rating/total_count 
+        @printf("Género: %s   Total calificaciones: %.2f   Total conteo: %d   Promedio: %.2f\n", genres[i], total_rating, total_count,promedio) 
+    end
+end
+
+# Función para procesar cada archivo ratings y acumular resultados por género
+function process_ratings(ng::Int, genres::Vector{String}, df_movies::DataFrame, df_ratings::DataFrame)
+    rating_accum = zeros(ng)
+    count_accum = zeros(Int, ng)
+
+    # Hacer un inner join entre movies y ratings
+    joined_df = innerjoin(df_movies, df_ratings, on=:movieId)
+
+    # Calcular sumas y conteos por cada género
+    for i in 1:ng
+        genre_rows = joined_df[occursin.(genres[i], joined_df.genres), :]
+        count_accum[i] = nrow(genre_rows)
+        rating_accum[i] = sum(genre_rows.rating)
+    end
+
+    return rating_accum, count_accum
+end
+
+# Rutas de entrada y salida
+input_dir = "C:\\Users\\Alexis\\Documents\\Practica_Julia"
+output_dir = "C:\\Users\\Alexis\\Documents\\Practica_Julia"
+
+# Dividir el archivo ratings y guardar en formato Parquet
+generate_small_files(joinpath(input_dir, "ratings.csv"), "ratings", output_dir)
+
+# Procesar los archivos de ratings y cruzarlos con movies
+find_ratings_master(input_dir, output_dir)
diff --git a/julia/src/student_submissions/movielens/Liliana_Vera/movielens.jl b/julia/src/student_submissions/movielens/Liliana_Vera/movielens.jl
new file mode 100644
index 00000000..3c353709
--- /dev/null
+++ b/julia/src/student_submissions/movielens/Liliana_Vera/movielens.jl
@@ -0,0 +1,99 @@
+#import Pkg; Pkg.add("CSV")
+#import Pkg; Pkg.add("DataFrames")
+
+using DataFrames
+using CSV
+using Parquet
+using Printf
+
+# Función para dividir el archivo ratings en 10 partes y guardarlas en formato Parquet
+function generate_small_files(ratings_file::String, output_prefix::String, output_dir::String)
+    println("Dividiendo archivo de ratings en partes...")
+    
+    # Leer el archivo completo de ratings
+    data = CSV.read(ratings_file, DataFrame)
+    total_rows = nrow(data)
+    num_chunks = 10  # Dividir en 10 partes
+    chunk_size = ceil(Int, total_rows / num_chunks)
+
+    # Crear y guardar cada chunk en archivos separados
+    for i in 1:num_chunks
+        start_row = (i - 1) * chunk_size + 1
+        end_row = min(i * chunk_size, total_rows)
+        chunk = data[start_row:end_row, :]
+
+        output_path = joinpath(output_dir, "$(output_prefix)_ratings$(lpad(i, 2, '0')).parquet")
+        Parquet.write_parquet(output_path, chunk)
+        println("Archivo guardado: $output_path con $(nrow(chunk)) filas")
+    end
+end
+
+# Función principal para procesar archivos de ratings y cruzarlos con movies
+function find_ratings_master(input_dir::String, output_dir::String)
+    nF = 10  # Número de archivos ratings
+    prqDir = output_dir
+
+    # Lista de géneros de películas
+    genres = ["Action", "Adventure", "Animation", "Children", "Comedy", "Crime", "Documentary",
+              "Drama", "Fantasy", "Film-Noir", "Horror", "IMAX", "Musical", "Mystery", "Romance",
+              "Sci-Fi", "Thriller", "War", "Western", "(no genres listed)"]
+    ng = length(genres)
+
+    # Arrays para acumular los resultados de calificaciones por género
+    rating_sum = zeros(ng, nF)
+    count_sum = zeros(Int, ng, nF)
+
+    # Leer el archivo movies.csv y mantener solo las columnas necesarias
+    movies_path = joinpath(input_dir, "movies.csv")
+    df_movies = CSV.read(movies_path, DataFrame)
+    df_movies = df_movies[:, [:movieId, :genres]]
+
+    # Procesar cada archivo de ratings
+    for i in 1:nF
+        rating_file = joinpath(prqDir, "ratings_ratings$(lpad(i, 2, '0')).parquet")
+        println("Procesando archivo: $rating_file")
+
+        if isfile(rating_file)
+            df_ratings = DataFrame(read_parquet(rating_file))
+            rating_sum[:, i], count_sum[:, i] = process_ratings(ng, genres, df_movies, df_ratings)
+        else
+            println("Archivo no encontrado: $rating_file")
+        end
+    end
+
+    # Sumar resultados finales por género y mostrarlos
+    for i in 1:ng
+        total_rating = sum(rating_sum[i, :])
+        total_count = sum(count_sum[i, :])
+        promedio = total_rating/total_count 
+        @printf("Género: %s   Total calificaciones: %.2f   Total conteo: %d   Promedio: %.2f\n", genres[i], total_rating, total_count,promedio) 
+    end
+end
+
+# Función para procesar cada archivo ratings y acumular resultados por género
+function process_ratings(ng::Int, genres::Vector{String}, df_movies::DataFrame, df_ratings::DataFrame)
+    rating_accum = zeros(ng)
+    count_accum = zeros(Int, ng)
+
+    # Hacer un inner join entre movies y ratings
+    joined_df = innerjoin(df_movies, df_ratings, on=:movieId)
+
+    # Calcular sumas y conteos por cada género
+    for i in 1:ng
+        genre_rows = joined_df[occursin.(genres[i], joined_df.genres), :]
+        count_accum[i] = nrow(genre_rows)
+        rating_accum[i] = sum(genre_rows.rating)
+    end
+
+    return rating_accum, count_accum
+end
+
+# Rutas de entrada y salida
+input_dir = "C:\\Users\\Alexis\\Documents\\Practica_Julia"
+output_dir = "C:\\Users\\Alexis\\Documents\\Practica_Julia"
+
+# Dividir el archivo ratings y guardar en formato Parquet
+generate_small_files(joinpath(input_dir, "ratings.csv"), "ratings", output_dir)
+
+# Procesar los archivos de ratings y cruzarlos con movies
+find_ratings_master(input_dir, output_dir)
\ No newline at end of file