English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 67 Lessons (8h 13m) | 2.21 GB
Harness the Potential of Python for Data Science. Optimize, analyze, and visualize data effectively
What you’ll learn
- Leverage a Seven Step framework to create algorithms and programs.
- Use NumPy and Pandas to manipulate, filter, and analyze data with arrays and matrices.
- Utilize best practices for cleaning, manipulating, and optimizing data using Python.
- Create classification models and publication quality visualizations with your datasets.
Skills you’ll gain
- Algorithm Design
- Data Visualization
- Matplotlib
- Predictive Modeling
- Debugging
- Program Decomposition
- NumPy
- Data Analysis
- Monte Carlo Methods
- Python Programming
- Pandas (Python Package)
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you’re a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline.
Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems.
In the final two courses, you’ll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you’ll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
Applied Learning Project
Throughout this specialization, you’ll create programs to analyze real-world data and produce insights to the most important issues facing society (e.g., infant mortality, economic indices, and carbon emissions). You’ll learn a process to translate abstract problems into functional programs that will create reproducible analyses. Each course emphasizes discrete parts of a data scientist’s toolkit. All courses focus on practical applications, whether you’re debugging basic Python code in industry-standard libraries or optimizing and evaluating predictive models. By completing the programming exercises in this specialization, you’ll develop the analytical and technical skills necessary for completing a full data science pipeline– starting with a messy dataset and resulting in a publication-quality visualization.
Table of Contents
numpy-data-science
sets-and-dictionaries-storing-and-working-with-data
basics-of-object-oriented-programming
1 introduction-representing-data
2 object-oriented-programming-overview
3 classes
4 constructors
5 modules-and-import-statements
6 python-import-does-not-reload-modules_instructions
sets-and-big-o
7 sets-motivation
8 sets-in-python
9 a-bit-more-about-big-o_instructions
10 comprehensions_instructions
11 dictionaries-introduction
12 combining-dictionaries-with-classes-and-sets
13 introduction-to-the-interactive-console_instructions
14 word-counts-motivation
numpy-and-vectors
using-vectors-in-numpy
15 why-numpy_instructions
16 why-numpy_string
17 working-with-vectors_instructions
18 math-with-vectors_instructions
19 histograms_instructions
20 type-promotion-in-numpy_instructions
21 vector-recap_instructions
22 live-coding-exploring-vector-data
manipulating-vectors
23 subsetting-vectors_instructions
24 modifying-subsets-of-vectors_instructions
25 vector-subsets-recap_instructions
matrices-and-arrays
views-and-copies-in-numpy
26 vectors-matrices-and-arrays_instructions
27 views-and-copies-in-numpy_instructions
28 working-with-views-and-copies_instructions
29 views-and-copies-recap_instructions
30 objects-and-variables_instructions
working-with-matrices
31 matrices_instructions
32 reshaping-matrices_instructions
33 images-as-matrices_instructions
34 subsetting-matrices_instructions
35 modifying-subsets_instructions
36 live-coding-demo-subsetting-and-filtering-matrices
37 matrix-recaps_instructions
using-nd-arrays
38 nd-arrays_instructions
39 broadcasting_instructions
40 nd-array-review_instructions
summarizing-datasets-performance-optimization-and-data-randomization
summarizing-arrays
41 moving-past-matrices_instructions
42 summarizing-arrays_instructions
43 color-images-as-arrays_instructions
44 examples-of-summarizing-arrays_instructions
45 exercise-summarizing-arrays_instructions
vectorization-and-randomization
46 speed-and-ease-of-use_instructions
47 vectorization_instructions
48 live-coding-demonstrating-vectorization
49 exercise-vectorization_instructions
50 random-numbers_instructions
51 random-numbers_numpy.histogram
52 random-numbers_random
53 random-numbers_routines.random
54 random-numbers-exercises_instructions
55 course-wrap-up-moving-past-numpy_instructions
Resources
get-support
56 resources
pandas-data-science
intro-to-pandas-for-data-science-strings-and-i-o
lesson-1-introduction-to-the-course-and-loading-data
57 course-introduction
58 reading-and-writing-data_instructions
59 strings_instructions
60 strings_stdtypes
lesson-2-exceptions-and-file-i-o
61 file-system-concepts_instructions
62 paths-and-filesystems
63 exceptions_instructions
64 exceptions
65 understanding-exception-traces_exceptions
66 understanding-exception-traces_instructions
67 using-an-exception-traceback-to-debug
68 you-might-need-data-other-than-pandas_instructions
69 you-might-need-data-other-than-pandas_planetfact
70 file-io
71 csv-files_csv
72 csv-files_instructions
module-2-tabular-data-with-pandas
lesson-1-tabular-data-with-series
73 introduction-to-the-module-tabular-data_instructions
74 introduction-to-the-module-tabular-data_why-xarray
75 pandas-series_instructions
76 manipulating-series-subsetting-and-indexing-series_instructions
77 optional-indexing-with-brackets_instructions
78 the-object-data-type_instructions
lesson-2-tabular-data-with-dataframes
79 tabular-data-through-dataframes_instructions
80 tabular-data-through-dataframes_io
81 subsetting-dataframes-tips-and-pitfalls_instructions
82 live-coding-indexing-and-subsetting
83 the-categorical-data-type_instructions
84 pyarrow-an-alternative-to-numpy-as-pandas-backend_instructions
85 pyarrow-an-alternative-to-numpy-as-pandas-backend_pyarrow
module-3-loading-and-cleaning-data
lesson-1-viewing-storing-and-reading-data
86 intro-to-module-3_instructions
87 storing-and-reading-data-plain-text_instructions
88 storing-and-reading-data-plain-text_io
89 storing-and-reading-data-plain-text_pandas.read_json
90 storing-and-reading-data-binary-files_instructions
91 storing-and-reading-data-binary-files_io
92 storing-and-reading-data-binary-files_pickle
93 pandas-indices_instructions
94 views-and-copies-numpy-review_basics.copies
95 views-and-copies-numpy-review_instructions
96 views-and-copies-pandas_instructions
97 views-and-copies-copy-on-write_instructions
98 indexing-lab-discussion_instructions
lesson-2-cleaning-data
99 cleaning-data-identifying_instructions
100 cleaning-data-editing-globally_instructions
101 cleaning-data-editing-globally_re
102 cleaning-data-editing-globally_text
103 cleaning-data-editing-specific-data_instructions
104 cleaning-data-datatypes_instructions
105 cleaning-data-missing-data_instructions
106 live-coding-cleaning-data
module-4-data-manipulation
lesson-1-combining-data
107 intro-to-querying-data_instructions
108 combining-datasets-concatenating_instructions
109 combining-datasets-concatenating_pandas.DataFrame.reset_index
110 combining-datasets-joins_instructions
111 combining-datasets-merging_instructions
112 combining-datasets-validating-a-merge_instructions
lesson-2-grouping-and-querying
113 grouping-datasets_instructions
114 grouping-data-with-pivot-tables_instructions
115 reshaping-data_instructions
116 checking-for-duplicates_instructions
117 live-coding-combining-and-grouping
118 queries_instructions
module-4-wrap-up
119 module-4-recap_instructions
python-data-modeling
plotting
introduction-to-the-course
120 why-do-data-scientists-code
121 is-this-course-for-me-given-my-knowledge-of-machine-learning-and-or-statistics_instructions
basic-plotting-with-matplotlib
122 plotting-introduction_instructions
123 effective-plotting-practices_colormaps
124 effective-plotting-practices_instructions
125 basic-plotting-with-matplotlib_index
126 basic-plotting-with-matplotlib_instructions
matplotlib-specifics
127 a-figure-in-ten-pieces-matplotlib-customization_instructions
128 plotting-text-and-a-side-note-on-axis-scaling_annotations
129 plotting-text-and-a-side-note-on-axis-scaling_instructions
130 plotting-text-and-a-side-note-on-axis-scaling_matplotlib.pyplot.text
131 deep-dive-bar-plots_instructions
132 stack-plots-optional_instructions
133 stack-plots-optional_stackplot_demo
134 pie-charts_instructions
135 subplots_instructions
136 subplots_matplotlib.pyplot.figure
137 deep-dive-subplots_instructions
138 deep-dive-scatter-plots_instructions
139 deep-dive-scatter-plots_markers_api
140 error-bars_instructions
141 heat-maps_instructions
142 histograms_instructions
143 two-dimensional-histograms-optional_colormaps
144 two-dimensional-histograms-optional_instructions
145 tying-together-histograms-optional_instructions
146 legends_instructions
147 legends_matplotlib.axes.Axes.legend
148 saving-to-file_instructions
149 explicit-and-implicit-syntax_instructions
150 plotting-zoo-multiple-ways-of-visualization_instructions
151 plotting-with-pandas_instructions
152 plotting-with-pandas_visualization
153 customizing-plot-styles_instructions
154 customizing-plot-styles_matplotlib_configuration_api
155 customizing-plot-styles_style_sheets_reference
156 plotting-zoo-restyled_instructions
advanced-plotting-optional
157 the-matplotlib-model_instructions
158 the-matplotlib-model_matplotlib.lines.Line2D
159 making-plots-pretty-laying-the-foundation_instructions
160 making-plots-pretty-the-process_instructions
161 plotting-with-seaborn_api
162 plotting-with-seaborn_instructions
163 plotting-with-seaborn_matplotlib_configuration_api
164 plotting-with-seaborn_objects_interface
165 plotting-with-seaborn_style_sheets_reference
166 seaborn-object-recipes_instructions
prediction
k-nearest-neighbors-classifiers
167 module-2-introduction_instructions
168 introduction-to-prediction_instructions
169 k-nearest-neighbors-classification_instructions
170 first-steps-in-coding-a-knn-classifier_instructions
171 live-coding-creating-and-evaluating-a-knn-classifier
172 knn-for-regression-foundation_instructions
173 knn-for-regression-evaluation_instructions
module-2-wrap-up
174 module-2-wrap-up_instructions
regression
linear-regression-and-inference
175 linear-regression-in-python_instructions
176 linear-regression-in-python_welcome
177 linear-regression-a-brief-introduction_instructions
178 a-brief-intro-to-categorical-variables_instructions
179 inference-vs-prediction-in-data-science_instructions
180 linear-regression-in-python_instructions
181 linear-regression-in-python_statsmodels.regression.linear_model.RegressionResults
182 linear-regression-in-python_user-guide
183 from-pandas-to-numpy-with-patsy_R-comparison
184 from-pandas-to-numpy-with-patsy_api
185 from-pandas-to-numpy-with-patsy_instructions
186 from-pandas-to-numpy-with-patsy_overview
187 optional-reading-linear-regression-extensions_gam
188 optional-reading-linear-regression-extensions_glm
189 optional-reading-linear-regression-extensions_instructions
190 live-coding-exploring-data-with-linear-regression
final-project
gapminder-project
191 gapminder-project-introduction_instructions
192 project-data-gathering_instructions
193 gapminder-project-plotting_instructions
194 gapminder-project-full-pipeline_instructions
module-4-wrap-up
195 whats-next-after-this-course_instructions
python-design-for-data-science
introduction-to-larger-programs
top-down-design
196 course-introduction-moving-on-to-larger-programs
197 software-engineering-vs-data-analysis
198 putting-it-all-together-top-down-design_instructions
random-story
199 random-story-overview_instructions
200 random-story-planning
201 random-story-from-parsing-to-blank-types
202 random-story-from-blank-types-to-categories
203 random-story-from-categories-to-backreferences
monte-carlo-methods-and-introduction-to-the-poker-project
untitled-lesson
204 poker-project-introduction
205 more-on-monte-carlo_instructions
206 poker-assignment-breakdown_instructions
writing-test-cases-and-identifying-sources-of-error
rules-of-poker-and-hand-evaluation
207 rules-of-poker_instructions
integrating-larger-programs
putting-the-poker-project-together
208 poker-unknown-cards-future-cards
209 poker-project-wrap-up_instructions
python-programming-fundamentals
algorithm-design
seven-steps-for-algorithm-design
210 introduction-to-python-programming-fundamentals
211 plan-first-then-code_instructions
212 overview-of-the-7-steps_instructions
213 algorithms_instructions
214 stepping-through-an-algorithm
working-an-example-of-the-seven-steps-process
215 step-1-work-an-example-yourself_instructions
216 step-2-write-down-what-you-just-did_instructions
217 step-3-generalize-your-steps_instructions
218 step-4-test-your-algorithm_instructions
219 testing-an-algorithm-for-a-numerical-sequence
creating-an-algorithm
220 intro-to-a-pattern-of-squares_instructions
221 a-pattern-of-squares
222 testing-a-pattern-of-squares
223 drawing-a-rectangle
224 closest-point
225 generalizing-closest-point
data-types
226 everything-is-a-number
227 non-numbers_instructions
translating-ideas-into-code
important-python-syntax
228 semantics-what-does-code-mean
229 variables-and-expressions
230 functions
231 printing
232 conditional-statements
233 loops
234 first-four-steps-revisited_instructions
235 revisiting-intersection-of-two-rectangles
translating-an-algorithm-into-python
236 translating-algorithms-to-code_instructions
237 ending-blocks-with-pass_instructions
238 top-down-design-and-composability_instructions
239 stars-example_instructions
240 planning-isprime
241 generalizing-isprime
242 translating-isprime-to-code
243 tuples
using-vs-code
244 why-vs-code
245 introduction-to-vs-code_instructions
246 intro-lab
247 how-to-reset-lab-files_instructions
248 retirement-calculations_instructions
validating-your-code
approaches-to-testing
249 testing-means-finding-bugs
250 black-box-testing_instructions
251 test-driven-approaches
252 test-driven-development
253 white-box-testing_instructions
254 creating-test-cases_instructions
255 asserts_instructions
256 code-review_instructions
257 code-review
testing-and-debugging
258 debugging-the-scientific-method
259 debugging-hypotheses
260 introduction-to-debugging-tools_instructions
261 principles-and-tools-for-debugging_instructions
262 debugging-python-in-vs-code_instructions
diving-deeper-with-lists
looking-more-into-lists
263 lists-references-to-mutable-objects
264 lists-iteration
265 lists-indexing-and-slicing
266 default-arguments-revisited_instructions
heart-rate-an-example
267 heart-rate-example-introduction_instructions
268 heart-rate-introduction
269 heart-rate-peaks
270 heart-rate-code
271 heart-rate-ispeakat
272 moving-averages_instructions
Resources
get-support
273 resources
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