Moving from Data Analyst to Data Scientist
Offered By: LinkedIn Learning
Course Description
Overview
Are you a data analyst wanting to pivot to a new career focus as a data scientist? This learning path provides valuable help in transitioning your skills. You'll take existing skills in coding languages like Python, SQL, DAX, and R, and then learn how to use them in an expanded context. You'll also develop power skills in core tools like Tableau, Spark, and KNIME, and get a strong grounding in statistics skills.
- Learn SQL, R, Python, Tableau, and Power BI in data science.
- Master data mining, wrangling, and ETL with Python and SQL.
- Use machine learning to extract insights and make decisions.
- Build statistical knowledge and solve data science problems.
Syllabus
Courses under this program:
Course 1: Intermediate SQL for Data Scientists
-Dive into one of the most important data science tools: SQL. Learn how to use joins and subqueries, statistical functions, window functions, and much more.
Course 2: Data Wrangling in R
-Learn about the principles of tidy data and discover how to import, transform, clean, and wrangle data using the R programming language.
Course 3: Python Functions for Data Science
-Save time, and make your code more readable and reusable, by learning the most powerful Python functions for data science.
Course 4: Data Science Foundations: Data Mining in Python
-Learn the key concepts and skills behind one of the most important elements of data science: data mining.
Course 5: Basics of Data Visualization Analysis
-Learn the fundamentals of data visualization, including how to use common charts and graphs across a variety of different types of datasets.
Course 6: Statistics Foundations 1: The Basics
-Learn to understand your data using basics of statistics, such as defining the middle, mean, and median of your data set; measuring the standard deviation; and finding outliers.
Course 7: Statistics Foundations 2: Probability
-Learn to understand your data in beginner-friendly lessons, using probability. Topics include permutations, percentiles, how to use probability trees, and much more.
Course 8: Statistics Foundations 3: Using Data Sets
-Go beyond the basics of statistics with practical, example-based lessons to learn how data sets and statistics are used in the real world.
Course 9: Statistics Foundations 4: Advanced Topics
-Complete your mastery of statistics with this advanced concepts course on t-distribution, degrees of freedom, regression testing, and ANOVA.
Course 10: Tableau for Data Scientists
-Take your Tableau skills to the next level. Learn how to format and filter messy data, use Tableau for data analysis, and visualize data with maps and dashboards.
Course 11: Power BI Data Modeling with DAX
-Take your data modeling skills to the next level. Learn how to leverage Data Analysis Expressions (DAX) to create formulas for Power BI that extend your data model.
Course 12: Actionable Insights and Business Data in Practice
-Learn how to start interpreting data-based findings and making high-value, data-driven suggestions to solve problems for stakeholders.
Course 13: ETL in Python and SQL
-Gain the knowledge you need to build data pipelines in a data-driven world.
Course 14: Introduction to Machine Learning with KNIME
-Learn KNIME, a popular open-source platform for predictive analytics and machine learning. Discover how to use KNIME for merging and aggregation, modeling, data scoring, and more.
Course 15: Hands-On Data Science using SQL, Tableau, Python, and Spark
-Get practical, hands-on data science experience using SQL, Tableau, Python, and Spark.
Course 1: Intermediate SQL for Data Scientists
-Dive into one of the most important data science tools: SQL. Learn how to use joins and subqueries, statistical functions, window functions, and much more.
Course 2: Data Wrangling in R
-Learn about the principles of tidy data and discover how to import, transform, clean, and wrangle data using the R programming language.
Course 3: Python Functions for Data Science
-Save time, and make your code more readable and reusable, by learning the most powerful Python functions for data science.
Course 4: Data Science Foundations: Data Mining in Python
-Learn the key concepts and skills behind one of the most important elements of data science: data mining.
Course 5: Basics of Data Visualization Analysis
-Learn the fundamentals of data visualization, including how to use common charts and graphs across a variety of different types of datasets.
Course 6: Statistics Foundations 1: The Basics
-Learn to understand your data using basics of statistics, such as defining the middle, mean, and median of your data set; measuring the standard deviation; and finding outliers.
Course 7: Statistics Foundations 2: Probability
-Learn to understand your data in beginner-friendly lessons, using probability. Topics include permutations, percentiles, how to use probability trees, and much more.
Course 8: Statistics Foundations 3: Using Data Sets
-Go beyond the basics of statistics with practical, example-based lessons to learn how data sets and statistics are used in the real world.
Course 9: Statistics Foundations 4: Advanced Topics
-Complete your mastery of statistics with this advanced concepts course on t-distribution, degrees of freedom, regression testing, and ANOVA.
Course 10: Tableau for Data Scientists
-Take your Tableau skills to the next level. Learn how to format and filter messy data, use Tableau for data analysis, and visualize data with maps and dashboards.
Course 11: Power BI Data Modeling with DAX
-Take your data modeling skills to the next level. Learn how to leverage Data Analysis Expressions (DAX) to create formulas for Power BI that extend your data model.
Course 12: Actionable Insights and Business Data in Practice
-Learn how to start interpreting data-based findings and making high-value, data-driven suggestions to solve problems for stakeholders.
Course 13: ETL in Python and SQL
-Gain the knowledge you need to build data pipelines in a data-driven world.
Course 14: Introduction to Machine Learning with KNIME
-Learn KNIME, a popular open-source platform for predictive analytics and machine learning. Discover how to use KNIME for merging and aggregation, modeling, data scoring, and more.
Course 15: Hands-On Data Science using SQL, Tableau, Python, and Spark
-Get practical, hands-on data science experience using SQL, Tableau, Python, and Spark.
Courses
-
Learn KNIME, a popular open-source platform for predictive analytics and machine learning. Discover how to use KNIME for merging and aggregation, modeling, data scoring, and more.
-
Go beyond the basics of statistics with practical, example-based lessons to learn how data sets and statistics are used in the real world.
-
Learn to understand your data using basics of statistics, such as defining the middle, mean, and median of your data set; measuring the standard deviation; and finding outliers.
-
Learn the key concepts and skills behind one of the most important elements of data science: data mining.
-
Learn about the principles of tidy data and discover how to import, transform, clean, and wrangle data using the R programming language.
-
Take your data modeling skills to the next level. Learn how to leverage Data Analysis Expressions (DAX) to create formulas for Power BI that extend your data model.
-
Save time, and make your code more readable and reusable, by learning the most powerful Python functions for data science.
-
Complete your mastery of statistics with this advanced concepts course on t-distribution, degrees of freedom, regression testing, and ANOVA.
-
Get practical, hands-on data science experience using SQL, Tableau, Python, and Spark.
-
Learn to understand your data in beginner-friendly lessons, using probability. Topics include permutations, percentiles, how to use probability trees, and much more.
-
Take your Tableau skills to the next level. Learn how to format and filter messy data, use Tableau for data analysis, and visualize data with maps and dashboards.
-
Dive into one of the most important data science tools: SQL. Learn how to use joins and subqueries, statistical functions, window functions, and much more.
-
Learn the fundamentals of data visualization, including how to use common charts and graphs across a variety of different types of datasets.
-
Gain the knowledge you need to build data pipelines in a data-driven world.
-
Learn how to start interpreting data-based findings and making high-value, data-driven suggestions to solve problems for stakeholders.
Taught by
Dan Sullivan, Mike Chapple, Lavanya Vijayan, Barton Poulson, PhD, Madecraft , Eddie Davila, Matt Francis, Gini von Courter, Jennifer Ebe, Keith McCormick and Ben Sullins
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