Applied Data Analytics Specialization

applied-data-analytics-with-python-and-sql

python-essentials

programming-with-python
1 welcome-to-applied-data-analytics-with-python-and-sql_instructions
2 course-introduction
3 programming-languages-and-myths
4 python-for-ai-ml-code-simplicity
5 python-for-ai-ml-ease-of-learning
6 starting-with-python_instructions
7 starting-with-python_miniconda
8 identifiers-in-python
9 literals-in-python
10 arithmetic-comparison-logical-and-assignment-operators
11 bitwise-and-membership-operators
12 python-token-types

data-types-in-python
13 boolean-operators-in-conditional-statement
14 and-or-not-in-python
15 working-with-dictionaries
16 manipulating-dictionary
17 tuples-in-python
18 working-with-strings-in-python
19 managing-strings
20 operations-on-string
21 creating-lists
22 immutable-and-mutable-data-types
23 operations-performed-on-lists
24 sets
25 manipulating-sets
26 an-introduction-to-python-data-types_instructions

conditional-statements
27 exploring-pythons-built-in-functions
28 built-in-functions-in-python_instructions
29 conditional-statement-if-condition
30 conditional-statement-if-else-condition
31 initializing-for-loop
32 conditional-statements-and-for-loop-in-python_instructions
33 break-continue-and-pass-statement
34 control-flow-with-python-break-continue-and-pass-statements_instructions
35 validating-prime-numbers
36 mastering-the-while-loop-in-python

functions-and-file-handling
37 opening-files
38 file-operations
39 global-vs-local-variables-in-python
40 introduction-to-lambda-functions-in-python
41 built-in-modules
42 standard-modules
43 creating-user-defined-functions
44 functions-with-multiple-parameters
45 lambda-functions-in-python_instructions
46 file-handling-with-python_instructions

module-wrap-up-and-assessment
47 summary-of-python-essentials

exploring-data-with-numpy-and-pandas

numpy-with-python
48 introduction-to-numpy-in-python
49 hands-on-with-numpy
50 numpy-in-python_instructions

data-manipulation-using-pandas
51 introduction-to-pandas-in-python
52 working-with-pandas-series
53 understanding-pandas-dataframes
54 data-manipulation-with-pandas
55 combining-datasets-with-pandas
56 getting-started-with-pandas_instructions

data-visualization
57 data-visualization-fundamentals_instructions
58 matplotlib-library
59 plotting-charts
60 plotting-histogram-and-box-plot
61 plotting-multiple-charts
62 introduction-to-seaborn-scatter-plot
63 seaborn-basic-charts
64 seaborn-heatmap-manipulation
65 seaborn-flights-dataset
66 seaborn-gaining-insights-in-flight-data
67 visualizing-charts-with-plotly
68 customizing-different-charts-in-plotly
69 data-visualization-with-matplotlib_instructions
70 visualization-with-seaborn_instructions

module-wrap-up-and-assessment
71 summary-of-exploring-with-numpy-and-pandas

web-scraping-and-sql

urllib-and-scraping-data-with-python
72 what-is-web-scraping
73 web-scraping-process-flow
74 managing-data
75 beautiful-soup
76 demonstration-of-beautiful-soup
77 demonstration-of-scrapy
78 web-scraping-best-practices_instructions

normalization
79 what-is-sql
80 operation-on-keys
81 normalization-in-databases
82 types-of-normalization-1-nf
83 types-of-normalization-2-nf
84 types-of-normalization-3-nf
85 types-of-normalization-4-nf
86 types-of-normalization-5-nf
87 installation-of-ms-sql-server
88 difference-between-ms-sql-server-and-mysql_instructions

sql-commands
89 ddl-commands
90 dql-commands
91 dml-commands
92 dcl-commands
93 tcl-commands
94 setting-up-sql-server
95 managing-foreign-key
96 inserting-data
97 deleting-data
98 alter-command
99 setting-up-transaction
100 sql-commands_instructions

python-integration-with-ms-sql-server
101 connecting-with-database
102 creating-table
103 inserting-data
104 selecting-data
105 updating-records
106 deleting-records
107 python-with-microsoft-sql-server_instructions

module-wrap-up-and-assessment
108 summary-of-web-scraping-and-sql

course-wrap-up-and-assessment

untitled-lesson
109 course-summary-applied-data-analytics-with-python-and-sql
110 practice-project-data-analysis-of-police-records_instructions

Resources

module-2-resources
111 resources

module-3-resources
112 resources

module-4-resources
113 resources

predictive-modeling-with-python

data-and-information

data-types-in-statistics
114 welcome-to-predictive-modeling-with-python_instructions
115 course-introduction
116 data-types
117 categorical-data
118 nominal-data
119 demonstration-of-data-types-dataset-description
120 demonstration-of-types

statistical-analysis
121 what-is-statistics
122 measures-of-central-tendency
123 demonstration-of-central-tendency
124 applications-of-central-tendency-in-statistics_instructions

statistical-dispersion
125 measures-of-dispersion
126 demonstration-measures-of-dispersion
127 libraries-for-statistical-analysis_instructions

module-wrap-up-and-assessment
128 summary-of-data-and-information

probability-distribution-function

general-probability-distribution
129 probability-density-and-mass-function
130 cumulative-distribution-function
131 discrete-probability
132 example-of-pdf-and-pmf_instructions

negative-bernoulli-and-geometric-distribution
133 negative-bernoulli-distribution
134 demonstration-of-negative-bernoulli-distribution
135 geometric-distribution
136 demonstration-of-geometric-distribution
137 importance-of-negative-bernoulli-and-geometric-distributions_instructions

poisson-and-uniform-distribution
138 poisson-distribution
139 example-of-poisson-distribution
140 demonstration-of-poisson-distribution
141 continuous-probability-distribution
142 uniform-distribution
143 continuous-probability-distribution-and-uniform-distribution-mathematical_instructions

exponential-and-normal-distribution
144 exponential-distribution
145 demonstration-of-exponential-distribution
146 normal-distribution
147 demonstration-of-normal-distribution

module-wrap-up-and-assessment
148 summary-of-probability-distribution-functions

inferential-statistics

introduction-to-central-limit-theorem
149 central-limit-theorem
150 demonstration-of-central-limit-theorem
151 demontration-conclusion-of-central-limit-theorem
152 central-limit-theorem-clt-mathematical-example_instructions

statistical-inference-methods
153 population-and-sample-space
154 parameter-and-statistics
155 forms-of-inferential-statistics
156 point-and-interval-estimation
157 maximum-likelihood
158 demonstration-exploring-data
159 demonstration-drawing-sample-data

statistical-hypothesis-and-significance-testing
160 hypothesis-testing
161 hypothesis-testing-example
162 statistical-test-implementation
163 one-tailed-and-two-tailed-test
164 z-test-and-t-test
165 power-analysis
166 demonstration-of-confidence-interval-and-margin-of-error
167 demonstration-of-hypothesis-testing
168 demonstrating-power-analysis
169 statistical-inference-real-world-applications_instructions

parametric-and-non-parametric-tests
170 chi-square-test
171 pearson-and-spearman-correlation
172 chi-square-test-demonstration
173 pearson-correlation-demonstration
174 spearman-correlation-demonstration
175 anova
176 example-for-one-way-anova-part-1
177 example-for-two-way-anova-part-2
178 demonstration-for-one-way-anova
179 demonstration-for-two-way-anova
180 shapiro-wilk-test_instructions

module-wrap-up-and-assessment
181 summary-for-inferential-statistics

introduction-to-exploratory-data-analysis-eda

understanding-eda
182 what-is-eda
183 univariate-analysis-data-and-outliers
184 univariate-analysis-kurtosis-and-chart-types
185 multivariate-analysis
186 multivariate-analysis-covariance-correlation-and-association
187 multivariate-analysis-correlation-matrix
188 multivariate-analysis-scatter-plots-and-heatmaps
189 understanding-exploratory-data-analysis-eda_instructions

data-cleaning-and-pre-processing
190 identifying-and-handling-missing-data
191 sampling-methods
192 mean-median-mode-imputation
193 data-normalization-and-standardization
194 methods-to-transform-data
195 univariate-bivariate-and-multivariate-imputation
196 demonstration-i-understanding-the-data
197 demonstration-ii-visualizing-and-handling-missing-data
198 demonstration-iii-scaling-and-imputation-of-data
199 demonstration-iv-train-test-split
200 demonstration-v-stratified-k-fold-cross-validation
201 demonstration-vi-sampling-and-evaluation
202 best-practices-in-data-pre-processing_instructions

feature-engineering-and-data-transformation
203 introduction-to-feature-engineering
204 feature-transformation
205 encoding-one-hot-encoding
206 encoding-label-encoding
207 autofeat-library
208 demonstration-i-setting-up-the-scenario
209 demonstration-ii-data-transformation
210 demonstration-iii-encoding
211 demonstration-iv-autofeat
212 overview-of-autofeat-library_instructions

module-wrap-up-and-assessment
213 summary-for-introduction-to-eda

predictive-modeling-and-analysis

regression
214 introduction-to-linear-regression
215 assumptions-in-linear-regression
216 working-of-linear-regression
217 cost-function-in-linear-regression
218 gradient-descent-in-linear-regression
219 demonstration-of-linear-regression-building-model
220 demonstration-of-linear-regression-testing-the-model
221 logistic-regression
222 cost-function-in-logistic-regression
223 gradient-descent-in-logistic-regression
224 importance-of-sigmoid-function
225 demonstration-logistic-regression-data-processing
226 demonstration-logistic-regression-model-execution
227 regularization-in-regression_instructions

classification-decision-tree-and-random-forest
228 classification-in-machine-learning
229 decision-tree-part-1-what-is-decision-tree
230 decision-tree-part-2-what-is-random-forest
231 basic-terminologies-of-decision-tree
232 working-of-decision-tree
233 building-a-decision-tree
234 advantages-and-disadvantages-of-decision-tree
235 demonstration-part-1-explaining-the-scenario
236 demonstration-part-2-exploring-the-data
237 demonstration-part-3-profiling-report
238 demonstration-part-4-attrition-and-univariate-graph
239 demonstration-part-5-data-pre-processing
240 demonstration-part-6-building-decision-tree
241 demonstration-part-7-tree-classifier
242 demonstration-part-8-pros-and-cons
243 random-forest-example-part-1-ensemble-learning-and-bagging
244 random-forest-example-part-2-working-of-random-forest

model-evaluation-and-optimization
245 performance-metrics-for-regression-mae-and-mape
246 performance-metrics-for-regression-mse-rmse-rmsle-and-r-square
247 confusion-matrix
248 roc-and-auc
249 hyperparameter-tuning-and-optimization
250 model-selection
251 model-evaluation
252 bias-variance-trade-off
253 cross-validation
254 demonstration-i-grid-search-analyze-the-data
255 demonstration-ii-grid-search-building-model
256 optuna-a-powerful-tool-for-hyperparameter-optimization_instructions

module-wrap-up-and-assessment
257 summary-of-predictive-models

course-wrap-up-and-assessment

untitled-lesson
258 course-summary-of-predictive-modeling-with-python
259 black-friday-sales-analysis_instructions

Resources

module-6-resources
260 resources

module-5-resources
261 resources

module-4-resources
262 resources

module-3-resources
263 resources

module-1-resources
264 resources

python-for-data-visualization-and-analysis

visualization-with-matplotlib

data-visualization-with-matplotlib
265 welcome-to-python-for-data-visualization-and-analysis_instructions
266 course-introduction
267 environment-set-up
268 importance-of-data-visualization
269 from-numbers-to-narratives_instructions

types-of-plots-and-charts
270 line-plot
271 bar-chart
272 horizontal-bar-chart
273 stacked-bar-chart
274 histogram
275 demonstration-plotting-line-and-bar-graph
276 demonstration-plotting-histogram
277 choosing-the-right-chart-bar-charts-line-charts-and-histogram_instructions
278 choosing-the-right-chart-type_instructions

plotting-different-charts
279 scatter-plot
280 pie-chart
281 box-plot
282 customizing-charts
283 demonstration-pie-chart
284 demonstration-scatter-plot-and-box-plot
285 choosing-the-right-chart-scatter-pie-and-box_instructions

module-wrap-up-and-assessment
286 summary-of-visualization-with-matplotlib

data-visualization-with-seaborn

seaborn-library
287 what-is-seaborn
288 installing-and-setting-up-seaborn
289 comparing-seaborn-with-matplotlib
290 seaborn-with-matplotlib_instructions

plot-types
291 relational-plot-rel-plot
292 distribution-plot-dist-plot
293 categorical-plot-cat-plot
294 demonstration-visualizing-charts-with-seaborn
295 demonstration-visualizing-heatmap
296 demonstration-category-relational-and-distribution-plots
297 demonstration-personalizing-charts-and-visuals
298 demonstration-tailoring-graphs-and-visuals
299 a-guide-to-seaborn_instructions

module-wrap-up-and-assessment
300 summary-for-data-visualization-with-seaborn

interactive-data-visualization

plotly-library
301 plotly
302 customizing-basic-plot-background-and-markers
303 customizing-basic-plot-lines-titles-and-labels
304 customizing-basic-plot-interactive
305 interactive-plots
306 demonstration-plots-with-hover-feature
307 demonstration-customizing-hover-features-and-tooltips
308 turning-static-plots-interactive_instructions

plotly-dashboard
309 plotly-dash
310 demonstration-defining-layout-and-structure
311 demonstration-building-web-apps
312 demonstration-chaining-callbacks
313 demonstration-multiple-inputs-and-outputs-with-interactions
314 demonstration-importing-airbnb-data
315 demonstration-web-app-for-airbnb-data
316 plotly-dash-best-practices_instructions

working-of-ipywidgets
317 ipywidgets
318 displaying-widgets-layouts-and-container-widgets
319 interactive-controls-combining-multiple-widgets-for-interactivity
320 custom-widgets-creating-and-registering-custom-widgets
321 extending-widget-functionality

streamlit
322 what-is-streamlit
323 demonstration-code-details
324 demonstration-executing-the-app
325 demonstration-data-visualization-on-streamlit
326 building-with-streamlit_instructions

module-wrap-up-and-assessment
327 summary-for-interactive-data-visualization

course-wrap-up-and-assessment

untitled-lesson
328 course-summary-of-python-for-data-visualization-and-analysis
329 project-sales-data-analysis-and-visualization-dashboard_instructions

Resources

module-1-resources
330 resources

module-2-resources
331 resources

module-3-resources
332 resources

module-4-resources
333 resources