diff --git a/data_analyst/README.md b/data_analyst/README.md
new file mode 100644
index 0000000..adf0c45
--- /dev/null
+++ b/data_analyst/README.md
@@ -0,0 +1 @@
+# Scritps to perform various data analysis tasks
diff --git a/data_analyst/lomb_scargle.py b/data_analyst/lomb_scargle.py
new file mode 100644
index 0000000..d03d315
--- /dev/null
+++ b/data_analyst/lomb_scargle.py
@@ -0,0 +1,2 @@
+# * https://docs.astropy.org/en/stable/timeseries/lombscargle.html
+# * https://medium.com/@samuelerickson977/analyzing-the-s-p-500-index-with-a-lomb-scargle-periodogram-aaaad8ac5bce
diff --git a/data_analyst/requirements.txt b/data_analyst/requirements.txt
new file mode 100644
index 0000000..a9e2534
--- /dev/null
+++ b/data_analyst/requirements.txt
@@ -0,0 +1,3 @@
+numpy
+matplotlib
+scipy
diff --git a/data_analyst/smoothing/main.py b/data_analyst/smoothing/main.py
new file mode 100644
index 0000000..0b86328
--- /dev/null
+++ b/data_analyst/smoothing/main.py
@@ -0,0 +1,125 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from math import factorial
+import csv
+import scipy
+import argparse
+
+
+# FROM: https://scipy.github.io/old-wiki/pages/Cookbook/SavitzkyGolay
+# NOTE: Not the same as https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html
+def savitzky_golay(y, window_size, order, deriv=0, rate=1):
+     r"""Smooth (and optionally differentiate) data with a Savitzky-Golay filter.
+     The Savitzky-Golay filter removes high frequency noise from data.
+     It has the advantage of preserving the original shape and
+     features of the signal better than other types of filtering
+     approaches, such as moving averages techniques.
+     Parameters
+     ----------
+     y : array_like, shape (N,)
+         the values of the time history of the signal.
+     window_size : int
+         the length of the window. Must be an odd integer number.
+     order : int
+         the order of the polynomial used in the filtering.
+         Must be less then `window_size` - 1.
+     deriv: int
+         the order of the derivative to compute (default = 0 means only smoothing)
+     Returns
+     -------
+     ys : ndarray, shape (N)
+         the smoothed signal (or it's n-th derivative).
+     Notes
+     -----
+     The Savitzky-Golay is a type of low-pass filter, particularly
+     suited for smoothing noisy data. The main idea behind this
+     approach is to make for each point a least-square fit with a
+     polynomial of high order over a odd-sized window centered at
+     the point.
+     Examples
+     --------
+     t = np.linspace(-4, 4, 500)
+     y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
+     ysg = savitzky_golay(y, window_size=31, order=4)
+     import matplotlib.pyplot as plt
+     plt.plot(t, y, label='Noisy signal')
+     plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
+     plt.plot(t, ysg, 'r', label='Filtered signal')
+     plt.legend()
+     plt.show()
+     References
+     ----------
+     .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of
+        Data by Simplified Least Squares Procedures. Analytical
+        Chemistry, 1964, 36 (8), pp 1627-1639.
+     .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing
+        W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery
+        Cambridge University Press ISBN-13: 9780521880688
+     """
+     
+     try:
+         window_size = np.abs(np.int64(window_size))
+         order = np.abs(np.int64(order))
+     except ValueError:
+         raise ValueError("window_size and order have to be of type int")
+     if window_size % 2 != 1 or window_size < 1:
+         raise TypeError("window_size size must be a positive odd number")
+     if window_size < order + 2:
+         raise TypeError("window_size is too small for the polynomials order")
+     order_range = range(order+1)
+     half_window = (window_size -1) // 2
+     # precompute coefficients
+     b = np.asmatrix([[k**i for i in order_range] for k in range(-half_window, half_window+1)])
+     m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)
+     # pad the signal at the extremes with
+     # values taken from the signal itself
+     firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )
+     lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])
+     y = np.concatenate((firstvals, y, lastvals))
+     return np.convolve( m[::-1], y, mode='valid')
+
+
+def data_1():
+    x = []
+    y = []
+    with open('smooth.csv') as csvfile:
+        spamreader = csv.reader(csvfile, delimiter=',')
+        data = list([x for x in spamreader][1:])
+        for row in data:
+            x.append(float(row[0]))
+            y.append(float(row[1].replace(",","")))
+    x = np.array(x)
+    y = np.array(y)
+    # yhat = scipy.signal.savgol_filter(y, 31, 2)
+    yhat = savitzky_golay(y, 31 , 2)
+    plt.plot(x,y)
+    plt.plot(x,yhat, color='red')
+    plt.show()
+
+
+# TODO(gitbuda): Implement skip x values (e.g. if x are dates -> x axis is a huge mess).
+def smooth(csv_file_path, x_axis_column_name, y_axis_column_name):
+    x = []
+    y = []
+    with open(csv_file_path) as csvfile:
+        reader = csv.DictReader(csvfile)
+        for row in reader:
+            print(row)
+            x.append(row[x_axis_column_name])
+            y.append(float(row[y_axis_column_name]))
+    x = np.array(x)
+    y = np.array(y)
+    # yhat = scipy.signal.savgol_filter(y, 31, 2)
+    yhat = savitzky_golay(y, 31 , 2)
+    plt.plot(x,y)
+    plt.plot(x,yhat, color='red')
+    plt.show()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--path")
+    parser.add_argument("--x")
+    parser.add_argument("--y")
+    args = parser.parse_args()
+    smooth(args.path, args.x, args.y)