English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 130 Lessons (6h 5m) | 2.60 GB
Analyze Data, Build Models, and Present Insights. Transform data into actionable insights through data analysis, predictive modeling, data visualization, and communication of results.
What you’ll learn
Master the process of data wrangling, including data storage, access, and manipulation using SQL.
Perform exploratory data analysis (EDA) using Python, focusing on data inspection, cleaning, visualization, and summarization.
Apply predictive analytics techniques to make data-driven predictions using Python.
Create compelling data visualizations with Tableau for decision-making and storytelling.
Skills you’ll gain
- Predictive Analytics
- Data Analytics
- Data Visualization
- Python (Programming Language)
- Data Science
- Tableau (Business Intelligence Software)
- Data Storytelling
- SQL
- Exploratory Data Analysis
- Data Analysis
- Database Query Tools
- Machine Learning
- Data Presentation
- Data Classification
- Logistic Regression
- Decision Tree Learning
- Random Forest Algorithm
- Matplotlib
- Seaborn
- Pandas (Python Package)
Specialization – 3 course series
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
Applied Learning Project
This specialization presents a variety of graded and practice assignments, both in the form of learning checks with multiple attempts, and in the form of programming assignments via Codio platform. Practice assignments in this course don’t count towards the Final Grade. All of the other assignmetns are automatically graded, and provide instant feedback to the Learners. Please feel free to post in Discussion Forums located in every Module if you have any questions about the assignments or the instructions. Google Chrome is the recommended browser for completing coding assignments.
Table of Contents
data-viz-using-tableau-presenting-with-storytelling
module-1-data-visualization-and-tableau
welcome
1 how-to-use-data-specialization-intro
2 data-visualization-using-tableau-presenting-with-storytelling-course-intro
3 about-the-instructor
lesson-1-data-visualization-and-tools
4 module-1-resources_instructions
5 module-1-intro-data-visualization-and-tableau
6 handbook-for-tableau_instructions
7 data-visualization-and-tools
8 coding-demo-intro-to-tableau-interface
lesson-2-concepts-and-principles-of-data-visualization
9 concepts-and-principles-of-data-visualization
lesson-3-one-dimensional-time-series-data-visualizations
10 one-dimensional-and-time-series-visualizations
11 coding-demo-one-dimensional-visualizations-bar-charts
12 coding-demo-one-dimensional-visualizations-line-charts
13 coding-demo-one-dimensional-visualizations-histograms
14 coding-demo-one-dimensional-visualizations-pie-charts
15 coding-demo-time-series-visualizations-area-charts
16 coding-demo-time-series-visualizations-gantt-charts
weekly-assignment
17 analyze-the-superstore-part-1_instructions
18 opt-in-to-penn-engineering-online-communications_instructions
module-2-multi-dimensional-visualizations-and-complex-relationships
lesson-1-multi-dimensional-visualizations-spatial-relationships
19 module-2-resources_instructions
20 module-2-intro-multi-dimensional-visualizations-and-complex-relationships
21 handbook-for-tableau_instructions
22 multi-dimensional-visualizations-spatial-relationships
23 coding-demo-multi-dimensional-visualizations-scatterplots
24 coding-demo-multi-dimensional-visualizations-heatmaps
25 coding-demo-visualizing-spatial-relationships-maps
lesson-2-multi-table-analysis
26 multi-table-analysis
27 multi-table-analysis_instructions
28 coding-demo-tableau-relationships
weekly-assignment
29 analyze-the-superstore-part-2_instructions
additional-resources
30 additional-resources-on-data-storytelling_instructions
module-3-presentation-of-insights-data-storytelling
lesson-1-analyzing-and-interpreting-results
31 module-3-resources_instructions
32 module-3-intro-presentation-of-insights-data-storytelling
33 intro-intro-to-analyzing-and-interpreting-results
34 analyzing-and-interpreting-results
35 creating-effective-data-analytics-presentations
36 communicating-data-insights-effectively-to-stakeholders
37 best-practices-for-storytelling
38 case-study-best-practices-for-presenting-storytelling_instructions
39 opt-in-to-penn-engineering-online-communications_instructions
lesson-2-hearst-presentation
40 introduction-to-the-hearst-student-presentation_instructions
41 presentation-introduction
42 background-approach
43 categorizing-non-converters-into-converter-lookalikes-likely-converters-and
44 model-results
lesson-3-summary-of-the-course
45 how-to-use-data-closing-comments
intro-to-data-analytics-sql-and-eda-using-python
module-1-introduction-to-data-data-analytics-and-defining-the-problem
welcome
46 how-to-use-data-specialization-intro
47 intro-to-data-analytics-sql-and-eda-using-python-course-intro
48 about-the-instructor
lesson-1-intro-to-data
49 module-1-resources_instructions
50 module-1-intro-data-data-analytics-defining-the-problem
51 intro-to-data-data-analytics
52 programming-languages-and-tools-used-in-this-course_instructions
lesson-2-intro-to-data-science-and-data-analysis
53 intro-to-data-science-and-data-analysis
lesson-3-intro-to-data-analytics
54 intro-to-data-analytics
55 hearst-case-study-data-analytics-process
lesson-4-defining-the-analytics-problem
56 defining-the-analytics-problem
57 felix-case-study-defining-the-problem
weekly-assignment
58 codio-demo-sql
59 opt-in-to-penn-engineering-online-communications_instructions
module-2-introduction-to-data-wrangling-and-sql
lesson-1-intro-to-data-storage-and-access
60 module-2-resources_instructions
61 module-2-intro-intro-to-data-wrangling-using-sql
62 ways-to-store-data_instructions
lesson-2-intro-to-sql
63 intro-to-sql
lesson-3-overview-of-relational-databases
64 overview-of-relational-databases
65 getting-started-with-dbeaver_instructions
lesson-4-selecting-and-querying-data
66 coding-demo-connecting-to-sqlite-database-in-dbeaver
67 intro-to-selecting-and-querying-data
68 executing-sql-with-keyboard-shortcuts_instructions
69 coding-demo-basic-select-statements
70 common-comparison-operators
71 coding-demo-filtering-records-and-sorting-data
72 tables-in-example2-database-video-for-completing-the-assignments
lesson-5-joining-data
73 joining-data
74 right-join_instructions
75 primary-keys-and-foreign-keys-for-tables-in-example2-database
76 coding-demo-inner-join-and-left-join
lesson-6-single-row-functions-and-group-functions
77 single-row-functions
78 single-row-function-syntax_instructions
79 coding-demo-single-row-functions
80 group-functions
81 additional-group-function-syntax_instructions
82 coding-demo-group-functions
lesson-7-creating-updating-and-deleting-data
83 creating-data
84 updating-and-deleting-data_instructions
85 coding-demo-create-table-and-insert-into
lesson-8-importing-exporting-and-formatting-data
86 importing-and-exporting-data
87 coding-demo-loading-data-in-sqlite
88 formatting-data
89 coding-demo-formatting-data
module-3-exploratory-data-analysis-eda-using-python
lesson-1-loading-inspecting-querying-data
90 module-3-resources_instructions
91 module-3-intro-overview-of-exploratory-data-analysis-eda
92 loading-inspecting-and-querying-data
93 reading-more-about-the-pandas-module_instructions
94 coding-demo-loading-and-inspecting-data
95 coding-demo-querying-data
96 opt-in-to-penn-engineering-online-communications_instructions
97 codio-demo-jupyter-notebook
lesson-2-casting-and-cleaning-data
98 coding-demo-casting-data
99 different-ways-of-casting-data_instructions
100 coding-demo-cleaning-data
101 cleaning-data-dealing-with-missing-values_instructions
lesson-3-joining-and-filtering-data
102 joining-filtering-data
103 coding-demo-joining-data
104 coding-demo-filtering-data
105 intro-to-computations
106 coding-demo-data-computations
lesson-4-updating-and-creating-data
107 updating-creating-data
108 coding-demo-updating-and-creating-data
lesson-5-summarizing-data
109 summarizing-data
110 coding-demo-summarizing-data
111 pivot-tables-aggfunc_instructions
lesson-6-visualizing-data
112 visualizing-data
113 about-jupyter-notebook-magic-functions_instructions
114 coding-demo-visualizing-data-histograms
115 coding-demo-visualizing-data-scatterplots
116 coding-demo-visualizing-data-heatmaps
117 coding-demo-visualizing-data-barplots
intro-to-predictive-analytics-using-python
module-1-introduction-to-predictive-analytics-and-regressions
welcome
118 how-to-use-data-specialization-intro
119 intro-to-predictive-analytics-using-python-course-intro
120 about-the-instructor
lesson-1-overview-of-predictive-analytics
121 week-1-resources_instructions
122 week-1-intro-overview-of-predictive-analytics
lesson-2-supervised-predictive-models
123 supervised-predictive-models
lesson-3-linear-regression
124 linear-regression
125 reading-types-of-linear-regression_instructions
126 coding-demo-loading-the-data-and-exploring-the-data
127 coding-demo-creating-a-correlation-matrix
128 coding-demo-the-train-test-protocol
129 coding-demo-building-a-linear-regression-model
130 coding-demo-model-evaluation
131 coding-demo-interpreting-a-linear-regression-model
132 codio-demo-jupyter-notebook
lesson-4-logistic-regression
133 logistic-regression
134 reading-multi-class-logistic-regression_instructions
135 coding-demo-creating-categorical-attributes
136 coding-demo-incorporating-new-data
137 coding-demo-building-a-logistic-regression-model
138 coding-demo-interpreting-a-logistic-regression-model
139 coding-demo-visualizing-decision-boundaries
140 coding-demo-creating-a-confusion-matrix
141 opt-in-to-penn-engineering-online-communications_instructions
module-2-decision-trees-and-introduction-to-advanced-predictive-analytics-and
lesson-1-decision-trees
142 week-2-resources_instructions
143 week-2-intro-decision-trees-and-introduction-to-advanced-predictive-analytics
144 decision-trees
145 reading-entropy-and-information-gain_instructions
146 coding-demo-loading-the-data-and-creating-decision-trees
147 coding-demo-feature-scaling
148 coding-demo-building-a-decision-tree-model
149 coding-demo-decision-tree-vs-linear-regression-model
150 coding-demo-decision-tree-vs-logistic-regression-model
151 coding-demo-interpreting-a-decision-tree
152 coding-demo-interpreting-a-decision-tree-continued
lesson-2-more-supervised-learning-models
153 intro-to-advanced-predictive-analytics
154 more-supervised-learning-models
lesson-3-random-forests
155 random-forests
156 coding-demo-random-forests-loading-the-data-and-preprocessing
157 coding-demo-tree-pre-pruning-and-baseline-decision-trees
158 reading-cross-validation_instructions
159 coding-demo-building-a-random-forest-classifier
160 coding-demo-interpreting-a-random-forest
module-3-introduction-to-unsupervised-learning-and-clustering
lesson-1-unsupervised-learning
161 week-3-resources_instructions
162 week-3-intro-introduction-to-unsupervised-learning-and-clustering
163 unsupervised-learning
164 clustering
165 coding-demo-k-means-clustering-loading-the-data-and-preprocessing
166 coding-demo-identifying-the-ideal-number-of-clusters
167 coding-demo-final-k-means-clustering-model
168 coding-demo-interpreting-a-k-means-clustering-model
169 reading-distance-measures_instructions
170 opt-in-to-penn-engineering-online-communications_instructions
lesson-2-models-comparison
171 model-comparison
Resolve the captcha to access the links!