English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 33h 13m | 9.76 GB
Successful Data Analysts have a unique set of skills, and represent important value to organisations eager to make data-powered business decisions. In this program, you’ll learn to use Python, SQL, and statistics to uncover insights, communicate critical findings, and create data-driven solutions. Demand for qualified Data Analysts continues to rise, and as a graduate of this program, you will be prepared to take on these roles.
Table of Contents
1 Welcome to the Nanodegree Program!
2 The Life of a Data Analyst
3 Project Prep SQL and Moving Averages
4 Explore Weather Trends
5 Numbers and Strings
6 Functions, Installation and Conditionals
7 Data Structures and Loops
8 Files and Modules
9 Wikipedia Web Crawl Case Study
10 Python Project
11 Anaconda
12 Jupyter Notebooks
13 The Data Analysis Process
14 Data Analysis Process – Case Study 1
15 Data Analysis Process – Case Study 2
16 Programming Workflow for Data Analysis
17 Basic SQL
18 SQL Joins
19 SQL Aggregations
20 SQL Subqueries & Temporary Tables
21 SQL Data Cleaning
22 [Advanced] SQL Window Functions
23 [Advanced] SQL Advanced JOINs & Performance Tuning
24 Investigate a Dataset
25 Descriptive Statistics – Part I
26 Descriptive Statistics – Part II
27 Admissions Case Study
28 Probability
29 Binomial Distribution
30 Conditional Probability
31 Bayes Rule
32 Python Probability Practice
33 Normal Distribution Theory
34 Sampling distributions and the Central Limit Theorem
35 Confidence Intervals
36 Hypothesis Testing
37 Case Study AB tests
38 Regression
39 Multiple Linear Regression
40 Logistic Regression
41 Analyze AB Test Results
42 Congratulations & Next Steps
43 Welcome to Term 2!
44 Statistics and Programming Exercises
45 Test a Perceptual Phenomenon
46 What is EDA
47 R Basics
48 Explore One Variable
49 Problem Set Explore One Variable
50 Explore Two Variables
51 Problem Set Explore Two Variables
52 Explore Many Variables
53 Problem Set Explore Many Variables
54 Diamonds & Price Predictions
55 Explore and Summarize Data
56 Introduction to Data Wrangling
57 Gathering Data
58 Assessing Data
59 Cleaning Data
60 Wrangle and Analyze Data
61 Introduction to Data Visualization
62 Design
63 Data Visualizations in Tableau
64 Making Dashboards & Stories in Tableau
65 Create a Tableau Story
66 Congratulations & Next Steps
67 What is Version Control
68 Create A Git Repo
69 Review a Repo’s History
70 Add Commits To A Repo
71 Tagging, Branching, and Merging
72 Undoing Changes
73 GitHub Review
74 Develop Your Personal Brand
75 LinkedIn Review
76 Udacity Professional Profile
77 Conduct a Job Search
78 Refine Your Entry-Level Resume
79 Refine Your Career Change Resume
80 Refine Your Prior Industry Experience Resume
81 Craft Your Cover Letter
82 Ace Your Interview
83 Practice Behavioral Questions
84 Interview Fails
85 Land a Job Offer
86 Interview Practice
87 Introduction and Efficiency
88 List-Based Collections
89 Searching and Sorting
90 Maps and Hashing
91 Trees
92 Graphs
93 Case Studies in Algorithms
94 Technical Interview – Python
95 Welcome to Machine Learning
96 Naive Bayes
97 SVM
98 Decision Trees
99 Choose Your Own Algorithm
100 Datasets and Questions
101 Regressions
102 Outliers
103 Clustering
104 Feature Scaling
105 Text Learning
106 Feature Selection
107 PCA
108 Validation
109 Evaluation Metrics
110 Tying It All Together
111 Matrix Math and NumPy Refresher
112 Why Python Programming
113 Data Types and Operators
114 Control Flow
115 Functions
116 Scripting
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