**Data Scientist Nanodegree**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 43h 07m | 7.80 GB

Build effective machine learning models, run data pipelines, build recommendation systems, and deploy solutions to the cloud with industry-aligned projects.

Data science skills are in high demand, and you’ll finish this program with practical skills needed to land a data science job. You’ll build projects designed with our industry partners using real-world data, and by the end of the program you will be able to build machine learning models includng supervised and unsupervised methods; create and run data pipelines; design experiments; build recommendation systems; deploy solutions to the cloud; and more. This program is an ideal way to move into a data science career, and by the end of the program you’ll be ready to apply for data science jobs.

Term One is “Machine Learning for Data Scientists.” Across three sections, students focus on Supervised Learning, Deep Learning, and Unsupervised Learning. Featured projects include using Kaggle to build an algorithm for identifying charity donors, and creating an image classifier. Term Two is “Applied Data Science,” and the focus is on solving problems with data science, as well as software and data engineering. As a capstone project, students build their own data science portfolio project.

The Data Scientist Nanodegree program is for students who possess strong programming and data analysis skills, and is positioned as the next step for graduates of the Data Analyst Nanodegree program. Those interested in advanced analytics without programming are encouraged to consider the Business Analyst Nanodegree program, and beginners are invited to explore our Data Foundations Nanodegree program.

**Table of Contents**

1 Welcome to the Data Scientist Nanodegree program

2 Get Help from Peers and Mentors

3 Get Help with Your Account

4 Setting Up Your Computer

5 What Is Ahead

6 Machine Learning Bird’s Eye View

7 Linear Regression

8 Perceptron Algorithm

9 Decision Trees

10 Naive Bayes

11 Support Vector Machines

12 Ensemble Methods

13 Model Evaluation Metrics

14 Training and Tuning

15 Finding Donors Project

16 Introduction to Neural Networks

17 Implementing Gradient Descent

18 Training Neural Networks

19 Keras

20 Deep Learning with PyTorch

21 Image Classifier Project

22 Clustering

23 Hierarchical and Density Based Clustering

24 Gaussian Mixture Models and Cluster Validation

25 PCA

26 Random Projection and ICA

27 Project Identify Customer Segments

28 Congratulations!

29 Why Python Programming

30 Data Types and Operators

31 Control Flow

32 Functions

33 Scripting

34 NumPy

35 Pandas

36 Basic SQL

37 SQL Joins

38 SQL Aggregations

39 SQL Subqueries Temporary Tables

40 SQL Data Cleaning

41 [Advanced] SQL Window Functions

42 [Advanced] SQL Advanced JOINs Performance Tuning

43 Data Visualization in Data Analysis

44 Design of Visualizations

45 Univariate Exploration of Data

46 Bivariate Exploration of Data

47 Multivariate Exploration of Data

48 Explanatory Visualizations

49 Visualization Case Study

50 Shell Workshop

51 What is Version Control

52 Create A Git Repo

53 Review a Repo’s History

54 Add Commits To A Repo

55 Tagging, Branching, and Merging

56 Undoing Changes

57 Working With Remotes

58 Working On Another Developer’s Repository

59 Staying In Sync With A Remote Repository

60 Introduction

61 Vectors

62 Linear Combination

63 Linear Transformation and Matrices

64 Descriptive Statistics – Part I

65 Descriptive Statistics – Part II

66 Admissions Case Study

67 Probability

68 Binomial Distribution

69 Conditional Probability

70 Bayes Rule

71 Python Probability Practice

72 Normal Distribution Theory

73 Sampling distributions and the Central Limit Theorem

74 Confidence Intervals

75 Hypothesis Testing

76 Case Study AB tests

77 Regression

78 Multiple Linear Regression

79 Logistic Regression

80 Welcome to the Data Scientist Nanodegree Program

81 Get Help from Peers and Mentors

82 Get Help with Your Account

83 The Skills That Set You Apart

84 The Data Science Process

85 Communicating to Stakeholders

86 Project Write A Data Science Blog Post

87 Optimize Your GitHub Profile

88 Introduction to Software Engineering

89 Software Engineering Practices Pt I

90 Software Engineering Practices Pt II

91 Introduction to Object-Oriented Programming

92 Portfolio Exercise Upload a Package to PyPi

93 Web Development

94 Portfolio Exercise Deploy a Data Dashboard

95 Introduction to Data Engineering

96 ETL Pipelines

97 NLP Pipelines

98 Machine Learning Pipelines

99 Project Disaster Response Pipeline

100 Strengthen Your Online Presence Using LinkedIn

101 Intro to Experiment Design and Recommendation Engines

102 Concepts in Experiment Design

103 Statistical Considerations in Testing

104 AB Testing Case Study

105 Portfolio Exercise Starbucks

106 Introduction to Recommendation Engines

107 Matrix Factorization for Recommendations

108 Recommendation Engines

109 Data Scientist Capstone

110 Congratulations!

111 Neural Networks

112 Deep Neural Networks

113 Convolutional Neural Networks

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