diff --git a/CH15_Big data and MapReduce/Mapper.py b/CH15_Big data and MapReduce/Mapper.py
new file mode 100644
index 0000000..0dd47cd
--- /dev/null
+++ b/CH15_Big data and MapReduce/Mapper.py	
@@ -0,0 +1,18 @@
+#!/usr/bin/env python
+#coding=utf-8
+
+import sys
+from numpy import mat, mean, power
+
+def read_input(file):
+    for line in file:
+        yield line.rstrip()
+input = read_input(sys.stdin)
+input = [float(line) for line in input]
+#print(input,'=====')
+numInputs = len(input)
+input = mat(input)
+sqInput = power(input, 2)
+
+print ("%d\t%f\t%f" % (numInputs, mean(input), mean(sqInput)) ) # calc mean of columns
+#print (  sys.stderr, "report: still alive" )
\ No newline at end of file
diff --git a/CH15_Big data and MapReduce/Reducer.py b/CH15_Big data and MapReduce/Reducer.py
new file mode 100644
index 0000000..7fdeb12
--- /dev/null
+++ b/CH15_Big data and MapReduce/Reducer.py	
@@ -0,0 +1,22 @@
+import sys
+from numpy import mat, mean, power
+
+def read_input(file):
+    for line in file:
+        yield line.rstrip()
+input = read_input(sys.stdin)
+mapperOut = [line.split('\t') for line in input]
+print(mapperOut)
+cumVal = 0.0
+cumSumSq = 0.0
+cumN = 0.0
+for instance in mapperOut:
+    nj = float(instance[0])
+    cumN += nj
+    cumVal += nj * float(instance[1])
+    cumSumSq += nj * float(instance[2])
+mean = cumVal / cumN
+var = (cumSumSq/cumN-mean*mean)
+
+print ("%d\t%f\t%f" % (cumN, mean, var) )
+print ( sys.stderr, "report: still alive" )
\ No newline at end of file
diff --git a/CH15_Big data and MapReduce/inputFile.txt b/CH15_Big data and MapReduce/inputFile.txt
new file mode 100644
index 0000000..73a10c1
--- /dev/null
+++ b/CH15_Big data and MapReduce/inputFile.txt	
@@ -0,0 +1,100 @@
+0.970413
+0.901817
+0.828698
+0.197744
+0.466887
+0.962147
+0.187294
+0.388509
+0.243889
+0.115732
+0.616292
+0.713436
+0.761446
+0.944123
+0.200903
+0.547166
+0.800028
+0.848790
+0.001641
+0.058010
+0.859900
+0.009178
+0.736598
+0.683586
+0.142515
+0.212120
+0.752769
+0.546184
+0.652227
+0.583803
+0.812863
+0.036862
+0.075076
+0.257536
+0.431278
+0.600214
+0.985564
+0.055846
+0.905295
+0.336262
+0.198738
+0.845815
+0.527989
+0.448650
+0.235313
+0.599749
+0.443923
+0.968723
+0.911076
+0.279338
+0.569492
+0.635985
+0.267532
+0.975018
+0.463698
+0.842340
+0.065590
+0.233049
+0.810390
+0.448260
+0.431967
+0.549648
+0.703612
+0.187974
+0.231709
+0.784160
+0.072283
+0.921053
+0.735468
+0.715923
+0.150431
+0.661089
+0.734955
+0.633709
+0.216102
+0.498474
+0.195620
+0.339548
+0.245314
+0.819848
+0.521242
+0.549276
+0.200906
+0.202525
+0.922876
+0.025404
+0.604032
+0.752204
+0.158860
+0.651622
+0.592898
+0.500392
+0.410614
+0.968388
+0.265918
+0.565707
+0.413670
+0.080507
+0.929978
+0.609755
diff --git a/CH15_Big data and MapReduce/mrMean.py b/CH15_Big data and MapReduce/mrMean.py
new file mode 100644
index 0000000..5536187
--- /dev/null
+++ b/CH15_Big data and MapReduce/mrMean.py	
@@ -0,0 +1,47 @@
+'''
+Created on Feb 28, 2011
+
+@author: Peter
+'''
+from mrjob.step import MRStep
+from mrjob.job import MRJob
+
+class MRmean(MRJob):
+    def __init__(self, *args, **kwargs):
+        super(MRmean, self).__init__(*args, **kwargs)
+        self.inCount = 0
+        self.inSum = 0
+        self.inSqSum = 0
+    
+    def map(self, key, val): #needs exactly 2 arguments
+        if False: yield
+        inVal = float(val)
+        self.inCount += 1
+        self.inSum += inVal
+        self.inSqSum += inVal*inVal
+        
+    def map_final(self):
+        mn = self.inSum/self.inCount
+        mnSq = self.inSqSum/self.inCount
+        yield (1, [self.inCount, mn, mnSq])
+
+    def reduce(self, key, packedValues):
+        cumVal=0.0; cumSumSq=0.0; cumN=0.0
+        for valArr in packedValues: #get values from streamed inputs
+            nj = float(valArr[0])
+            cumN += nj
+            cumVal += nj*float(valArr[1])
+            cumSumSq += nj*float(valArr[2])
+        mean = cumVal/cumN
+        var = (cumSumSq - 2*mean*cumVal + cumN*mean*mean)/cumN
+        yield (mean, var) #emit mean and var
+        
+    def steps(self):
+        return ([MRStep(mapper=self.map, mapper_final=self.map_final,\
+                          reducer=self.reduce,),
+                 MRStep(mapper=self.map, mapper_final=self.map_final,\
+                        reducer=self.reduce,)
+                 ])
+
+if __name__ == '__main__':
+    MRmean.run()