Data Science Fundamentals with Python and SQL Specialization

About Course

Data Science Fundamentals with Python and SQL Specialization, Data Science Fundamentals with Python and SQL, Build the Foundation for your Data Science career. Develop hands-on experience with Jupyter, Python, SQL. Perform Statistical Analysis on real data sets. Data Science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field.

The specialization consists of 5 self-paced online courses that will provide you with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases. You’ll learn these data science pre-requisites through hands-on practice using real data science tools and real-world data sets. Upon successfully completing these courses, you will have the practical knowledge and experience to delve deeper in Data Science and work on more advanced Data Science projects. All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets. Build your data science portfolio from the artifacts you produce throughout this program.

Working knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, Watson Studio
Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy
Statistical Analysis techniques including Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression
Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables

Course Content

01. Tools for Data Science

010 02_languages-of-data-science.mp4

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015 03_introduction-to-python.mp4

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020 04_introduction-to-r-language.mp4

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025 05_introduction-to-sql.mp4

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030 06_other-languages.mp4

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031 07_practice-quiz-languages_quiz.html

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036 01_categories-of-data-science-tools.mp4

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041 02_open-source-tools-for-data-science-part-1.mp4

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046 03_open-source-tools-for-data-science-part-2.mp4

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049 04_commercial-tools-for-data-science.mp4

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054 05_cloud-based-tools-for-data-science.mp4

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055 06_practice-quiz-tools_quiz.html

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058 01_libraries-for-data-science.mp4

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063 02_application-programming-interfaces-api.mp4

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068 03_data-sets-powering-data-science.mp4

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073 04_sharing-enterprise-data-data-asset-exchange.mp4

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078 05_machine-learning-models.mp4

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083 06_the-model-asset-exchange.mp4

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084 07_practice-quiz-packages-apis-data-sets-models_quiz.html

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087 01_introduction-to-jupyter-notebook.mp4

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092 02_getting-started-with-jupyter.mp4

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097 03_jupyter-kernels.mp4

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102 04_jupyter-architecture.mp4

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103 05_practice-quiz-jupyter-notebook_quiz.html

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108 01_introduction-to-r-and-rstudio.mp4

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111 02_plotting-within-rstudio.mp4

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112 03_practice-quiz-rstudio-ide_quiz.html

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117 01_overview-of-git-github.mp4

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122 02_github-getting-started.mp4

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127 03_github-working-with-branches.mp4

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132 04_git-and-github-via-command-line-optional.mp4

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137 05_branching-and-merging-via-command-line-optional.mp4

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142 06_contributing-to-repositories-via-pull-request-optional.mp4

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143 07_practice-quiz-github_quiz.html

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148 01_what-is-ibm-watson-studio.mp4

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153 02_watson-studio-introduction.mp4

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158 03_creating-an-account-on-ibm-watson-studio.mp4

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163 04_jupyter-notebook-in-watson-studio-part-1.mp4

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168 05_jupyter-notebook-in-watson-studio-part-2.mp4

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173 06_linking-github-to-watson-studio.mp4

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174 07_practice-quiz-watson-studio_quiz.html

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179 01_other-ibm-tools-for-data-science.mp4

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184 02_ibm-watson-knowledge-catalog.mp4

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189 03_data-refinery.mp4

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194 04_spss-modeler-flows-in-watson-studio.mp4

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199 05_ibm-spss-modeler.mp4

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204 06_spss-statistics.mp4

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209 07_model-deployment-with-watson-machine-learning.mp4

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214 08_auto-ai-in-watson-studio.mp4

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219 09_ibm-watson-openscale.mp4

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220 10_practice-quiz-other-ibm-tools_quiz.html

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221 01_create-and-share-your-jupyter-notebook_peer_assignment_instructions.html

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03. Python Project for Data Science

005 02_html-for-webscraping.mp4

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008 03_webscraping.mp4

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009 01_project-overview_instructions.html

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010 02_stock-shares_instructions.html

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011 03_optional-gamestop-stock-vs-tesla_instructions.html

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013 04_optional-jupyter-notebook-to-complete-your-final-project_instructions.html

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014 05_optional-hands-on-lab-analyzing-historical-stock-revenue-data-and-building-a_instructions.html

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015 06_analyzing-historical-stock-revenue-data-and-building-a-dashboard_peer_assignment_instructions.html

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016 01_next-steps_instructions.html

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links.txt

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001 01_course-introduction_instructions.html

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04. Statistics for Data Science with Python

006 02_course-overview_instructions.html

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011 03_python-packages-for-data-science.mp4

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012 04_optional-basics-of-jupyter-notebooks_instructions.html

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015 01_welcome-to-statistics.mp4

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018 02_types-of-data.mp4

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021 03_measure-of-central-tendency.mp4

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024 04_measure-of-dispersion.mp4

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025 01_practice-quiz-introduction-to-descriptive-statistics_quiz.html

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026 02_introduction-and-descriptive-statistics_exam.html

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029 01_visualization-fundamentals.mp4

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032 02_statistics-by-groups.mp4

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035 03_statistical-charts.mp4

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038 04_introducing-the-teachers-rating-data.mp4

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039 01_practice-quiz-data-visualization_quiz.html

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040 02_data-visualization_exam.html

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043 01_random-numbers-and-probability-distributions.mp4

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046 02_state-your-hypothesis.mp4

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047 03_alpha-a-and-p-value_instructions.html

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050 04_normal-distribution.mp4

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053 05_t-distribution.mp4

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056 06_probability-of-getting-a-high-or-low-teaching-evaluation.mp4

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057 07_standard-normal-table_instructions.html

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058 01_practice-quiz-introduction-to-probability-distribution_quiz.html

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059 02_introduction-to-probability-distribution_exam.html

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062 01_z-test-or-t-test.mp4

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065 02_dealing-with-tails-and-rejections.mp4

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068 03_equal-vs-unequal-variances.mp4

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071 04_anova.mp4

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074 05_correlation-tests.mp4

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075 01_practice-quiz-hypothesis-testing_quiz.html

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076 02_hypothesis-testing_exam.html

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079 01_regression-the-workhorse-of-statistical-analysis.mp4

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082 02_regression-in-place-of-t-test.mp4

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085 03_regression-in-place-of-anova.mp4

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088 04_regression-in-place-of-correlation.mp4

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089 01_practice-quiz-regression-analysis_quiz.html

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090 02_regression-analysis_exam.html

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091 01_project-case-scenario_instructions.html

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092 02_overview-of-project-tasks_instructions.html

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093 03_task-1-become-familiar-with-the-dataset_instructions.html

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094 04_optional-task-2-create-or-login-into-ibm-cloud-to-use-watson-studio_CreateIBMCloudAccount.md.html

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097 05_task-3-load-in-the-dataset-in-your-jupyter-notebook_instructions.html

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098 06_task-4-generate-descriptive-statistics-and-visualizations_instructions.html

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099 07_task-5-use-the-appropriate-tests-to-answer-the-questions-provided_instructions.html

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100 08_optional-task-6-share-your-jupyter-notebook_instructions.html

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102 09_create-and-share-your-jupyter-notebook_peer_assignment_instructions.html

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103 01_final-exam_exam.html

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105 02_opt-in-to-receive-your-badge_quiz.html

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links.txt

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05. Databases and SQL for Data Science with Python

005 01_welcome-to-sql-for-data-science.mp4

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010 02_introduction-to-databases.mp4

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015 03_how-to-create-a-database-instance-on-cloud.mp4

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020 04_relational-database-concepts.mp4

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023 01_create-table-statement.mp4

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024 02_select-statement.mp4

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025 03_count-distinct-limit.mp4

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028 04_insert-statement.mp4

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033 05_update-and-delete-statements.mp4

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036 01_using-string-patterns-and-ranges.mp4

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037 02_sorting-result-sets.mp4

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038 03_grouping-result-sets.mp4

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039 01_built-in-database-functions.mp4

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042 02_date-and-time-built-in-functions.mp4

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045 03_sub-queries-and-nested-selects.mp4

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049 04_working-with-multiple-tables.mp4

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050 01_how-to-access-databases-using-python.mp4

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053 02_writing-code-using-db-api.mp4

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056 03_connecting-to-a-database-using-ibm-db-api.mp4

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059 04_creating-tables-loading-data-and-querying-data.mp4

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062 05_analyzing-data-with-python.mp4

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063 01_about-this-optional-section_instructions.html

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066 02_join-overview.mp4

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069 03_inner-join.mp4

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072 04_left-outer-join.mp4

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075 05_right-outer-join.mp4

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078 06_full-outer-join.mp4

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079 07_practice-quiz-join-operations_quiz.html

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082 01_working-with-real-world-datasets.mp4

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085 02_getting-table-and-column-details.mp4

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086 01_peer-reviewed-assignment_peer_assignment_instructions.html

00:00
088 02_opt-in-to-receive-your-badge_quiz.html

00:00

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About Course

What Will You Learn?

Course Content

01. Tools for Data Science

010 02_languages-of-data-science.mp4

015 03_introduction-to-python.mp4

020 04_introduction-to-r-language.mp4

025 05_introduction-to-sql.mp4

030 06_other-languages.mp4

031 07_practice-quiz-languages_quiz.html

036 01_categories-of-data-science-tools.mp4

041 02_open-source-tools-for-data-science-part-1.mp4

046 03_open-source-tools-for-data-science-part-2.mp4

049 04_commercial-tools-for-data-science.mp4

054 05_cloud-based-tools-for-data-science.mp4

055 06_practice-quiz-tools_quiz.html

058 01_libraries-for-data-science.mp4

063 02_application-programming-interfaces-api.mp4

068 03_data-sets-powering-data-science.mp4

073 04_sharing-enterprise-data-data-asset-exchange.mp4

078 05_machine-learning-models.mp4

083 06_the-model-asset-exchange.mp4

084 07_practice-quiz-packages-apis-data-sets-models_quiz.html

087 01_introduction-to-jupyter-notebook.mp4

092 02_getting-started-with-jupyter.mp4

097 03_jupyter-kernels.mp4

102 04_jupyter-architecture.mp4

103 05_practice-quiz-jupyter-notebook_quiz.html

108 01_introduction-to-r-and-rstudio.mp4

111 02_plotting-within-rstudio.mp4

112 03_practice-quiz-rstudio-ide_quiz.html

117 01_overview-of-git-github.mp4

122 02_github-getting-started.mp4

127 03_github-working-with-branches.mp4

132 04_git-and-github-via-command-line-optional.mp4

137 05_branching-and-merging-via-command-line-optional.mp4

142 06_contributing-to-repositories-via-pull-request-optional.mp4

143 07_practice-quiz-github_quiz.html

148 01_what-is-ibm-watson-studio.mp4

153 02_watson-studio-introduction.mp4

158 03_creating-an-account-on-ibm-watson-studio.mp4

163 04_jupyter-notebook-in-watson-studio-part-1.mp4

168 05_jupyter-notebook-in-watson-studio-part-2.mp4

173 06_linking-github-to-watson-studio.mp4

174 07_practice-quiz-watson-studio_quiz.html

179 01_other-ibm-tools-for-data-science.mp4

184 02_ibm-watson-knowledge-catalog.mp4

189 03_data-refinery.mp4

194 04_spss-modeler-flows-in-watson-studio.mp4

199 05_ibm-spss-modeler.mp4

204 06_spss-statistics.mp4

209 07_model-deployment-with-watson-machine-learning.mp4

214 08_auto-ai-in-watson-studio.mp4

219 09_ibm-watson-openscale.mp4

220 10_practice-quiz-other-ibm-tools_quiz.html

221 01_create-and-share-your-jupyter-notebook_peer_assignment_instructions.html

03. Python Project for Data Science

005 02_html-for-webscraping.mp4

008 03_webscraping.mp4

009 01_project-overview_instructions.html

010 02_stock-shares_instructions.html

011 03_optional-gamestop-stock-vs-tesla_instructions.html

013 04_optional-jupyter-notebook-to-complete-your-final-project_instructions.html

014 05_optional-hands-on-lab-analyzing-historical-stock-revenue-data-and-building-a_instructions.html

015 06_analyzing-historical-stock-revenue-data-and-building-a-dashboard_peer_assignment_instructions.html

016 01_next-steps_instructions.html

links.txt

001 01_course-introduction_instructions.html

04. Statistics for Data Science with Python

006 02_course-overview_instructions.html

011 03_python-packages-for-data-science.mp4

012 04_optional-basics-of-jupyter-notebooks_instructions.html

015 01_welcome-to-statistics.mp4

018 02_types-of-data.mp4

021 03_measure-of-central-tendency.mp4

024 04_measure-of-dispersion.mp4

025 01_practice-quiz-introduction-to-descriptive-statistics_quiz.html

026 02_introduction-and-descriptive-statistics_exam.html

029 01_visualization-fundamentals.mp4

032 02_statistics-by-groups.mp4