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Course content
Sections:
39
•
Activities:
0
•
Resources:
119
- 001 Course Introduction
- 002 The Importance of Data Analyst
- 003 Why Data is the new Oil
- 004 Making Sense of Buzz Words Data Science Big Data Machine Deep Learning.mp4
- 005 The Roles in the Data World - Analyst Engineer Scientist Statistician DevOps
- 006 Download Code and Slides
- 007 Slides-Data-Analytics-Bootcamp
- 007 Download Course Code Slides and Setup Google Colab for your iPython Notebooks
- Download
- 008 Why use Python for Data Anakytics and Data Science
- 009 Python - Basic Variables
- 010 Python - ArrayLists and Dictionaries
- 011 Python - Conditional Statements
- 012 Python - Loops
- 013 Python - Functions
- 014 Python - Classes
- 015 Introduction to Pandas
- 016 Pandas 1 - Data Series
- 017 Pandas 2A - DataFrames - Index Slice Stats Finding Empty cells Filtering
- 018 Pandas 2B - DataFrames - Index Slice Stats Finding Empty cells Filtering
- 019 Pandas 3B - Data Cleaning - Alter ColomnsRows Missing Data String Operations
- 020 Pandas 3A Data Cleaning Alter Colomns RowsMissingDataStringOperations
- 021 Pandas 4 Data Aggregation Group By Map Pivot Aggreate Functions
- 022 Pandas 5 - Feature Engineer Lambda and Apply
- 023 Pandas 6 - Concatenating Merging and Joinining.mp4
- 024 Pandas 7 - Time Series Data
- 025 Pandas 7 - ADVANCED Operations - Iterows Vectorization and Numpy
- 026 Pandas 8 - ADVANCED Operations - More Map Zip and Apply
- 027 Pandas 9 - Advanced Operations - Parallel Processing
- 028 Map Visualizations with Plotly - Cloropeths from Scratch - USA and World
- 029 Map Visualizations with Plotly - Heatmaps Scatter Plots and Lines
- 030 Introduction to Statistics
- 031 Descriptive Statistics - Why Statistical Knowledge is so Important
- 032 Descriptive Statistics 1 - Exploratory Data Analysis (EDA) Visualizations
- 033 Descriptive Statistics 2 - Exploratory Data Analysis (EDA) Visualizations
- 034 Sampling Averages Variance And How to lie and Mislead with Statistics
- 035 Variance Standard Deviation and Bessels Correction
- 036 Types of Variables - Quantitive and Qualitative.
- 037 Frequency Distributions
- 038 Frequency Distributions Shapes
- 039 Analyzing Frequency Distributions - What is the Best Type of Wine Red or White
- 040 Covariance Correlation - Do Amazon Google know you better than anyone else
- 041 Sampling - Sample Sizes Confidence Intervals - What Can You Trust
- 042 Mean Mode and Median - Not as Simple As Youd Think
- 043 The Normal Distribution the Central Limit Theorem
- 044 Lying with Correlations Divorce Rates in Maine caused by Margarine Consumption
- 045 Z-Scores
- 11 Probability Theory/046 Probability - An Introduction
- 047 Estimating Probability
- 048 Addition Rule
- 049 Permutations Combinations
- 050 Bayes Theorem
- 051 Hypothesis Testing Introduction
- 052 Statistical Significance
- 053 Hypothesis Testing P Value
- 054 Hypothesis Testing Pearson Correlation
- 055 All about Google Data Studio
- 056 Opening Google Data Studio and Uploading Data
- 057 Your First Dashboard Part 1
- 058 Your First Dashboard Part 2
- 059 Creating New Fields
- 060 Pivot Tables
- 061 Dynamic Filtered Tables
- 062 Scorecards
- 063 Scorecards with Time Comparison
- 064 Bar Charts
- 065 Line Charts
- 066 Time Series and Comparitive Time Series Plots
- 067 Pie Charts Donut Charts and Tree Maps
- 068 Scatter Plots
- 069 Google Data Studio - Geographic Map Plots
- 070 Google Data Studio - Scatter Plots
- 071 Google Data Studio - Sharing your Interactive Dashboards
- 072 Homework Project - Create Executive Sales Dashboard
- 073 How Machine Learning enables Computers to Learn
- 074 What is a Machine Learning Model.
- 075 Types of Machine Learning
- 076 Linear Regression Introduction to Cost Functions and Gradient Descent
- 077 Linear Regressions in Python from Scratch and using Sklearn
- 078 Polynomial and Multivariate Linear Regression
- 079 Logistic Regression
- 080 Support Vector Machines (SVMs)
- 081 Decision Trees and Random Forests the Gini Index
- 082 K-Nearest Neighbors (KNN)
- 083 Assessing Performance Confusion Matrix Precision and Recall
- 084 Understanding the ROC and AUC Curve
- 085 What Makes a Good Model Regularization Overfitting Generalization Outliers
- 086 Introduction to Neural Networks
- 087 Types of Deep Learning Algoritms CNNs RNNs LSTMs
- 088 Introduction to Unsupervised Learning
- 089 K-Means Clustering
- 090 Choosing K Elbow Method Silhouette Analysis
- 091 K-Means in Python - Choosing K using the Elbow Method Silhoutte Analysis
- 092 Principal Component Analysis
- 093 t-Distributed Stochastic Neighbor Embedding (t-SNE)
- 094 PCA t-SNE in Python with Visualization Comparisons
- 096 Understanding the Problem Exploratory Data Analysis and Visualizations
- 097 Data Cleaning and Preparation
- 098 Sales and Revenue Analysis
- 099 Analysis per Country Repeat Customers and Items
- 100 Understanding the Problem Exploratory Data Analysis and Visualizations
- 101 Data Preparation and Machine Learning Modeling
- 102 Data Exploration Description
- 103 Simple Exploratory Data Analysis and Visualizations
- 104 Feature Engineering
- 105 K-Means Clustering of Customer Data
- 106 Cluster Analysis
- 107 Case Study 5 - Text Analytics - Airline Tweets (Word Clusters)
- 108 Understanding the Problem Exploratory Data Analysis and Visualizations
- 109 Customer Lifetime Value Modeling
- 110 Healthcare Analytics - Predict Diabetes
- 112 Case Study 9 - Election Polls - Making Better Predictions
- 123 Understanding the Problem Exploratory Data Analysis and Visualizations
- 124 Data Preparation and Machine Learning Modeling
- 125 Understanding the Problem Exploratory Data Analysis and Visualizations
- 126 AB Test Result Analysis
- 127 AB Testing a Worked Real Life Example - Designing an AB Test
- 128 Statistical Power and Significance
- 129 Analysis of AB Test Resutls