Statistics in Data Science with Python

Statistics in Data Science with Python

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  Statistics in DataScience with Python
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  Mahe Karim Front EndDeveloper ID - 162-15-7770 Area of Interest:  Full Stack Developer  Data Analyst  Animation  Why Not Jump Into Passive Income ? ;) Who I Am ?
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  Implement of ourcourse Step 1 Step 2 Step 3 •Statistics •Data Science •Python
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  Basic RoadTo DataScience Statistics Machine Learning Deep Learning Programming Language ( Python / R ) Data Science
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  Smartest way tobe a Data Scientist / Analyst • Core Statistics • Statistical Machine Learning • Probabilistic Modeling Step 1 Statistics • Database • Data Mining • Data Design Step 2 Computing • Deep Learning • NLP • DataAnalysis Step 3 ML
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  3 steps tolearning the statistics and probability required for data science: • Descriptive statistics, distributions, hypothesis testing, and regression. Core Statistics Concepts • Conditional probability, priors, posteriors, and maximum likelihood. BayesianThinking • Learn basic machine concepts and how statistics fits in. Intro to Statistical Machine Learning
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  Verified course includeSTATISTICS
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  Most ImportantTopics InStatistics • Part 1 - Simple Linear Regression Part 2 - Multivariate Linear Regression Part 3 - Logistic Regression Part 4 - Multivariate Logistic Regression Part 5 - Neural Networks Part 6 - SupportVector Machines Part 7 - K-Means Clustering & PCA Part 8 - Anomaly Detection & Recommendation
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  import os import numpyas np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline path = os.getcwd() + 'dataex1data1.txt' data = pd.read_csv(path, header=None, names=['Population', 'Profit']) data.head()
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  Data Set:
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  data.plot(kind='scatter', x='Population', y='Profit', figsize=(12,8))
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  Implementing Simple LinearRegression def computeCost(X, y, theta): inner = np.power(((X * theta.T) - y), 2) return np.sum(inner) / (2 * len(X) # append a ones column to the front of the data set data.insert(0, 'Ones', 1) # set X (training data) and y (target variable) cols = data.shape[1] X = data.iloc[:,0:cols-1] y = data.iloc[:,cols-1:cols] # convert from data frames to numpy matrices X = np.matrix(X.values) y = np.matrix(y.values) theta = np.matrix(np.array([0,0]))
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  x = np.linspace(data.Population.min(),data.Population.max(), 100) f = g[0, 0] + (g[0, 1] * x) fig, ax = plt.subplots(figsize=(12,8)) ax.plot(x, f, 'r', label='Prediction') ax.scatter(data.Population, data.Profit, label='Traning Data') ax.legend(loc=2) ax.set_xlabel('Population') ax.set_ylabel('Profit') ax.set_title('Predicted Profit vs. Population Size')
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  Prediction ;) :D:p <3
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  Resources:  https://elitedatascience.com/learn-statistics-for-data-science  https://github.com/datasciencemasters/go An Introduction to Statistical Learning with Applications in R Gareth James, DanielaWitten,Trevor Hastie and RobertTibshirani  http://www.johnwittenauer.net/machine-learning-exercises-in- python-part-1/  Think Stats