English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 151 Lessons (10h 25m) | 3.60 GB
Develop a Robust Foundation in Data Analytics. Build a job-ready data analytics skillset using industry-standard tools, including generative AI, to extract insights, make decisions, and solve real-world business problems.
What you’ll learn
- Statistics for real-world decision-making. Learn to calculate and interpret descriptive and inferential statistics to solve business problems.
- Data visualization and storytelling. Create compelling visualizations that effectively communicate complex data stories to stakeholders.
- Generative AI for analytics. Leverage genAI in the data analytics lifecycle, with hands-on labs and guidance on when and how to use AI assistance.
Skills you’ll gain
- Data cleaning and preprocessing
- Data Visualization
- Python Programming for Data Analysis
- Spreadsheets
- Data Storytelling
- Inferential Statistics (Hypothesis testing and Confidence Intervals)
- Applied Statistics and Probability
- Time Series Analysis And Forecasting
- Descriptive Statistics
- SQL for Data Manipulation
- Data Analysis
- Data Segmentation
- LLM Prompting for Data Analysis
- Inferential Statistics
- Python (Programming Language)
- Linear Regression
- Customized Visualizations
- Programming with Generative AI
- Data Validation
- Web Scraping
- Data Preprocessing
- SQLite
- Data Cleaning
- Statistical Analysis
- Probability And Statistics
- Tableau
- Insight Summary
- Data Narrative Design
- Data Dashboards
Learn in-demand analytics skills that can transform your career. Data-informed decision-making is now an essential skill for everyone, from everyday consumer choices to business decisions at all levels. As reliance on data grows, so does the need for professionals who can analyze and interpret it effectively. The Data Analytics Professional Certificate, led by industry leader Sean Barnes, equips you with the skills to manage the entire data lifecycle, from defining problems to delivering insights.
The skills you’ll gain are in high demand, and with data science roles projected to grow 36% from 2023 to 2033 according to the U.S. Bureau of Labor Statistics, developing these skills puts you at the forefront of a data-centric world.
Unique to this program is its integration of new AI tools into the analytics workflow. You’ll learn to use large language models as a thought partner, accelerating tasks like simulation modeling, formula debugging, and data visualization. Each of the course examples comes from real-world use cases, building practical and immediately useful skills.
Whether you’re a software engineer working with data pipelines, a marketer or business analyst extracting insights, or building a career in data analysis, you’ll gain the foundation to excel in the data economy. This program blends core statistical methods with AI-assisted workflows, perfect for beginner data professionals or experienced practitioners looking for fresh techniques.
Applied Learning Project
Advanced statistical applications. Move beyond theory to practical implementation of correlation analysis, confidence intervals, and hypothesis testing to solve real business challenges.
Effective data-driven communication: Develop the crucial skill of translating complex analytical findings into clear, actionable insights for stakeholders at all levels.
Future-ready analytics skills. Gain experience in AI-augmented workflows, preparing you for the cutting edge of data analytics, using AI to help speed up and improve analysis.
Table of Contents
applied-statistics-for-data-analytics
foundational-statistical-techniques
introduction
1 welcome-to-this-course
2 generative-ai-in-this-course
3 module-1-introduction
4 important-have-questions-issues-or-ideas-join-our-forum_instructions
populations-sampling
5 populations-and-sampling
6 identifying-the-population
7 probabilistic-samples
8 non-probabilistic-samples
9 types-of-bias
10 bias-in-practice_instructions
central-tendency
11 histograms
12 demo-plotting-distributions
13 central-tendency-variability-and-skewness
14 central-tendency-mean-and-mode
15 central-tendency-median
16 demo-central-tendency
17 practice-lab-djing-with-data-part-1_instructions
variability-skewness
18 variability-range-and-interquartile-range
19 variability-variance-and-standard-deviation
20 skewness
21 why-use-these-measures
22 demo-variability-and-skewness
23 box-plots
24 demo-llms-for-spreadsheet-formulas-errors
25 practice-lab-djing-with-data-part-2_instructions
26 about-the-llm-labs-in-this-course_instructions
correlation
27 correlation
28 correlation-and-causation
29 demo-correlations-scatterplots-in-spreadsheets
30 practice-lab-djing-with-data-part-3_instructions
segmentation
31 what-is-segmentation
32 demo-xlookup
33 demo-pivot-tables
graded-lab
34 graded-lab-forest-fire-prevention_instructions
probability-and-simulation
introduction
35 module-2-introduction
fundamentals-of-probability
36 randomness-and-uncertainty
37 probability-and-the-addition-rule
38 the-multiplication-and-complement-rules
39 conditional-probability
40 independence
41 random-variables
42 coin-tosses-and-dice-rolls_instructions
43 probability-vocabulary_instructions
44 practice-lab-djing-with-data-follow-up-part-1_instructions
discrete-probability-distributions
45 estimation
46 from-sample-distributions-to-population-distribution
47 the-bernoulli-distribution
48 the-binomial-distribution
49 the-cumulative-distribution-function
50 random-sampling-discrete
51 demo-spreadsheet-simulation-discrete
52 demo-llm-simulation-discrete
53 simulation-in-practice_instructions
54 discrete-probability-distributions-vocabulary_instructions
55 practice-lab-djing-with-data-follow-up-part-2_instructions
simulation
56 continuous-probability-distributions
57 the-normal-distribution
58 the-standard-normal-distribution
59 understanding-z-scores_instructions
60 random-sampling-normal
61 demo-spreadsheet-simulation-normal
62 demo-llm-simulation-normal
63 making-decisions-with-distributions
64 other-distributions_instructions
65 continuous-probability-distributions-vocabulary_instructions
66 practice-lab-djing-with-data-follow-up-part-3_instructions
graded-lab
67 graded-lab-forest-fire-prevention-follow-up_instructions
confidence-intervals
introduction
68 module-3-introduction
inferential-statistics
69 inferential-statistics
70 point-interval-estimates
71 sampling-distributions-the-central-limit-theorem
72 central-limit-theorem_instructions
confidence-intervals-for-means
73 demo-confidence-intervals-in-action
74 confidence-intervals
75 mechanisms-of-confidence-intervals
76 understanding-margin-of-error
77 demo-confidence-intervals-for-means
78 practice-lab-human-sleep-patterns-and-stress-part-1_instructions
confidence-intervals-for-proportions
79 confidence-intervals-for-proportions
80 demo-confidence-intervals-for-proportions
81 practice-lab-human-sleep-patterns-and-stress-part-2_instructions
llms-for-confidence-intervals
82 interpretation-with-llms
83 simulating-random-sampling-with-llms
84 inference-and-visualization-with-llms
graded-lab
85 graded-lab-diamond-prices_instructions
hypothesis-testing
introduction
86 module-4-introduction
hypothesis-testing-for-means
87 demo-hypothesis-testing-in-action
88 hypothesis-testing-means
89 the-hypothesis
90 identifying-the-hypothesis-and-test-type
91 calculating-the-test-statistic
92 determining-the-significance-level-and-rejection-region
93 calculating-the-p-value
94 demo-hypothesis-testing-for-means
95 hypothesis-testing-errors
96 the-t-distribution
97 practice-lab-human-sleep-patterns-and-stress-part-3_instructions
other-hypothesis-tests
98 hypothesis-testing-for-proportions
99 demo-hypothesis-testing-for-proportions
100 two-sample-tests
101 other-hypothesis-tests
102 explaining-statistical-inference_instructions
103 practice-lab-human-sleep-patterns-and-stress-part-4_instructions
llms-for-hypothesis-testing
104 interpretation-with-llms
105 inference-with-llms
graded-lab
106 graded-lab-diamond-prices_instructions
capstone
107 capstone-heart-disease-prevention_instructions
course-wrap-up
108 your-next-steps
acknowledgments
109 acknowledgments_instructions
data-analytics-foundations
data-and-the-data-analyst-role
introduction
110 welcome-to-data-analytics
111 generative-ai-in-this-course
112 welcome-to-the-course_instructions
113 module-1-introduction
114 life-as-a-data-analyst
115 important-have-questions-issues-or-ideas-join-our-forum_instructions
what-is-data-analytics
116 what-is-data-analytics
117 evidence-based-decision-making
118 a-history-of-data-analytics
119 modern-industry-use-cases
120 data-in-ancient-civilizations_instructions
what-is-data
121 defining-data
122 unstructured-data
123 structured-data
124 big-data
the-data-analytics-role-in-context
125 data-ecosystems
126 collaborators-outside-your-data-team
127 collaborators-on-your-data-team
large-language-models-for-data-analytics
128 introduction-to-large-language-models
129 choosing-an-llm
130 prompting-llms
131 llm-limitations
132 demo-interacting-with-llms
133 about-the-llm-labs-in-this-course_instructions
graded-lab
134 graded-lab-pizza-delivery_instructions
resources
135 module-1-resources_instructions
using-spreadsheets-for-data-analytics
introduction
136 module-2-introduction
how-spreadsheets-organize-data
137 solving-problems-with-data
138 spreadsheets-for-business-analytics
139 accessing-google-sheets-and-its-alternatives_instructions
140 working-with-spreadsheets-in-this-course_instructions
141 navigating-google-sheets
142 importing-data
143 sorting-filtering-and-formatting
144 named-ranges_instructions
spreadsheet-fundamentals
145 getting-to-know-your-data
146 summary-statistics-max-min-average
147 conditional-formatting
148 summary-statistics-countif
149 summary-statistics-sumif-averageif
150 summary-statistics-countifs-sumifs
151 data-processing-if-ifs-right-left
152 where-does-data-come-from
153 cells-and-cell-ranges_instructions
154 practice-lab-exploring-the-hotel-reservations-dataset_instructions
data-exploration-with-llms
155 data-exploration-with-llms
156 llms-and-privacy_instructions
157 choosing-the-right-llm-approach_instructions
time-series-data
158 introduction-to-time-series
159 real-world-time-series
160 moving-averages
161 percent-change
162 practice-lab-exploring-baby-names_instructions
graded-lab
163 graded-lab-video-game-market-research_instructions
module-2-resources
164 module-2-resources_instructions
data-visualization-in-spreadsheets
introduction
165 module-3-introduction
data-storytelling
166 what-is-data-storytelling
167 the-language-of-data-visualizations
168 analyzing-visualizations
creating-charts
169 the-right-chart-for-the-right-insight
170 chart-types-insights_instructions
171 demo-bar-column-charts
172 demo-customizing-charts
173 demo-scatter-plots
174 demo-grouped-bar-column-charts
175 demo-stacked-bar-column-charts
176 demo-line-charts
177 additional-chart-types-and-insights_instructions
178 practice-lab-exploring-hotel-reservations_instructions
best-practices-in-data-visualization
179 strategies-for-effective-data-visualization
180 data-encoding
181 chart-elements
182 data-visualization-examples-the-good-and-the-better
data-visualization-with-llms
183 demo-interpreting-data-visualizations-with-llms
184 demo-creating-data-visualizations-with-llms
graded-lab
185 graded-lab-bike-sharing-market-research_instructions
module-3-resources
186 book-references_instructions
187 module-3-resources_instructions
the-data-analytics-lifecycle
introduction
188 module-4-introduction
the-data-analytics-lifecycle
189 the-data-analytics-lifecycle
190 defining-the-problem
191 collecting-preprocessing-data
192 analyzing-data
193 identifying-insights
194 sharing-results
195 evaluating-outcomes
196 alternate-versions-of-the-data-analytics-lifecycle_instructions
working-with-stakeholders
197 gathering-stakeholder-requirements
198 synthesizing-stakeholder-input
199 understanding-the-rumsfeld-matrix_instructions
200 checking-in-with-stakeholders
201 domain-knowledge
202 domain-knowledge-examples_instructions
203 practice-lab-spotify-case-study_instructions
llms-for-stakeholder-analysis
204 demo-llms-for-stakeholder-analysis
graded-lab
205 graded-lab-bakery-case-study_instructions
module-4-resources
206 module-4-resources_instructions
capstone-project
207 capstone-customer-cancellation-analysis_instructions
course-wrap-up
208 your-next-steps-in-data-analytics
acknowledgments
209 acknowledgments_instructions
Resolve the captcha to access the links!