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Coursera Data Science Fundamentals with Python and SQL

Video Introducing this tutorial


open source tools for data science:
01 course introduction
02 languages of data science
03 introduction to python
04 introduction to r language
05 introduction to sql
06 other languages
07 practice quiz languages quiz
01 categories of data science tools
02 open source tools for data science part 1
03 open source tools for data science part 2
04 commercial tools for data science
05 cloud based tools for data science
06 practice quiz tools quiz
01 libraries for data science
02 application programming interfaces api
03 data sets powering data science
04 sharing enterprise data data asset exchange
05 machine learning models
06 the model asset exchange
07 practice quiz packages apis data sets models quiz
01 graded quiz exam
01 overview of git github
02 github getting started
03 github working with branches
04 git and github via command line optional
05 branching and merging via command line optional
06 contributing to repositories via pull request optional
07 practice quiz github quiz
01 getting started with jupyter notebook
02 getting started with jupyterlab
03 jupyter architecture
04 practice quiz jupyter notebook quiz
01 what is rstudio ide
02 installing packages and loading libraries in rstudio ide
03 plotting within rstudio ide
04 practice quiz rstudio ide quiz
01 graded quiz exam
01 what is ibm watson studio
02 watson studio introduction
03 creating an account on ibm watson studio
04 jupyter notebook in watson studio part 1
05 jupyter notebook in watson studio part 2
06 linking github to watson studio
07 practice quiz watson studio quiz
01 other ibm tools for data science
02 ibm watson knowledge catalog
03 data refinery
04 spss modeler flows in watson studio
05 ibm spss modeler
06 spss statistics
07 model deployment with watson machine learning
08 auto ai in watson studio
09 ibm watson openscale
10 practice quiz other ibm tools quiz
01 graded quiz exam
01 ibm digital badge instructions

python for applied data science ai:
01 about this course instructions
01 types
02 types exam
01 expressions and variables
02 expressions and variables exam
01 string operations
02 string operations exam
01 list and tuples
02 list and tuples exam
01 dictionaries
02 dictionaries exam
01 sets
02 sets exam
01 conditions and branching
02 conditions and branching exam
01 loops
02 loops exam
01 functions
02 functions exam
01 objects and classes
02 objects and classes exam
01 reading files with open
02 reading files with open exam
01 writing files with open
02 writing files with open exam
01 loading data with pandas
02 pandas working with and saving data
03 pandas exam
01 one dimensional numpy
02 one dimensional numpy exam
01 two dimensional numpy
02 two dimensional numpy exam
01 simple apis part 1
02 simple apis part 2
01 ibm digital badge instructions

sql data science:
01 welcome to sql for data science
02 introduction to databases
03 how to create a database instance on cloud
04 relational database concepts
05 databases exam
01 create table statement
02 select statement
03 count distinct limit
04 insert statement
05 update and delete statements
06 basic sql exam
01 using string patterns and ranges
02 sorting result sets
03 grouping result sets
04 string patterns ranges sorting and grouping exam
01 built in database functions
02 date and time built in functions
03 sub queries and nested selects
04 working with multiple tables
05 functions sub queries multiple tables exam
01 how to access databases using python
02 writing code using db api
03 connecting to a database using ibm db api
04 creating tables loading data and querying data
05 analyzing data with python
06 database access from python exam
01 about this optional section instructions
02 join overview
03 inner join
04 left outer join
05 right outer join
06 full outer join
07 practice quiz join operations quiz
01 working with real world datasets
02 getting table and column details
01 peer reviewed assignment peer assignment instructions
01 ibm digital badge instructions
02 opt in to receive your badge quiz

statistics for data science python:
01 welcome from your instructors
02 course overview instructions
03 python packages for data science
04 optional basics of jupyter notebooks instructions
01 welcome to statistics
02 types of data
03 measure of central tendency
04 measure of dispersion
01 visualization fundamentals
02 statistics by groups
03 statistical charts
04 introducing the teachers rating data
01 practice quiz data visualization quiz
02 data visualization exam
01 random numbers and probability distributions
02 state your hypothesis
03 alpha a and p value instructions
04 normal distribution
05 t distribution
06 probability of getting a high or low teaching evaluation
07 standard normal table instructions
01 z test or t test
02 dealing with tails and rejections
03 equal vs unequal variances
04 anova
05 correlation tests
01 practice quiz hypothesis testing quiz
02 hypothesis testing exam
01 regression the workhorse of statistical analysis
02 regression in place of t test
03 regression in place of anova
04 regression in place of correlation
01 practice quiz regression analysis quiz
02 regression analysis exam
01 project case scenario instructions
02 overview of project tasks instructions
03 task 1 become familiar with the dataset instructions
03 task 1 become familiar with the dataset sklearn.datasets.load boston
04 task 2 create or login into ibm cloud to use watson studio instructions
04 task 2 create or login into ibm cloud to use watson studio Setup.md
05 task 3 load in the dataset in your jupyter notebook instructions
06 task 4 generate descriptive statistics and visualizations instructions
07 task 5 use the appropriate tests to answer the questions provided instructions
08 task 6 share your jupyter notebook instructions
08 task 6 share your jupyter notebook Uploadnotebook.md
09 create and share your jupyter notebook peer assignment instructions
01 ibm digital badge instructions
02 opt in to receive your badge quiz