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Intermediate Network Analysis in Python
- AdvancedSkill Level
- 4.3+
- 105
Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.
Probability & Statistics
Data, AI, and Cloud Courses
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Case Study: Inventory Analysis in Tableau
- IntermediateSkill Level
- 4.6+
- 90
Enhance your Tableau skills with this case study on inventory analysis. Analyze a dataset, create calculated fields, and create visualizations.
Data Visualization
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Case Studies: Network Analysis in R
- BasicSkill Level
- 4.9+
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Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Probability & Statistics
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Introduction to Excel
- BasicSkill Level
- 4.6+
- 23.8K
Master the Excel basics and learn to use this spreadsheet tool to conduct impactful analysis.
Data Manipulation
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Introduction to Power Query in Excel
- BasicSkill Level
- 4.5+
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Explore Excel Power Query for advanced data transformation and cleansing to boost your decision-making and analysis.
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Introduction to Alteryx
- BasicSkill Level
- 4.7+
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Enter the world of Alteryx Designer and learn how to navigate the tool to load, prepare, and aggregate data.
Data Preparation
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Intermediate DAX in Power BI
- IntermediateSkill Level
- 4.6+
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Discover a wide range of DAX calculations and learn how to use them in Microsoft Power BI.
Data Manipulation
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Case Study: HR Analytics in Power BI
- BasicSkill Level
- 4.5+
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Apply your skills to import, analyze and visualize Human Resources (HR) data using Power BI.
Data Manipulation
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Introduction to Google Sheets
- BasicSkill Level
- 4.5+
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Bring your Google Sheets to life by mastering fundamental skills such as formulas, operations, and cell references.
Data Preparation
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Advanced Excel Functions
- IntermediateSkill Level
- 4.6+
- 2.6K
Boost your Excel skills with advanced referencing, lookup, and database functions using practical exercises.
Data Manipulation
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- IntermediateSkill Level
- 4.4+
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Learn Databricks SQL for data engineering, analytics, and real-time data workflows in the lakehouse architecture.
Data Engineering
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Introduction to Sigma
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Data Manipulation
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Calculations in Tableau
- AdvancedSkill Level
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In this interactive course, you’ll learn how to use functions for your Tableau calculations and when you should use them!
Data Visualization
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Case Study: Supply Chain Analytics in Power BI
- IntermediateSkill Level
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Learn how to use Power BI for supply chain analytics in this case study. Create a make vs. buy analysis tool, calculate costs, and analyze production volumes.
Data Visualization
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Introduction to Spark SQL in Python
- AdvancedSkill Level
- 4.5+
- 463
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
Data Manipulation
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Web Scraping in R
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Learn how to efficiently collect and download data from any website using R.
Data Preparation
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Decoding Decision Modeling
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Data Literacy
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Customer Segmentation in Python
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- 328
Learn how to segment customers in Python.
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Calculations in Sigma
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Build dynamic Sigma calculations to explore data, automate logic, and uncover trends with practical business examples.
Data Manipulation
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Marketing Analytics in Tableau
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Master marketing analytics using Tableau. Analyze performance, benchmark metrics, and optimize strategies across channels.
Data Preparation
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Introduction to the Tidyverse
- BasicSkill Level
- 4.7+
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Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.
Software Development
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Introduction to Regression in R
- IntermediateSkill Level
- 4.5+
- 3.5K
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Probability & Statistics
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Introduction to SQL Server
- BasicSkill Level
- 4.7+
- 2.9K
Learn to use SQL Server to perform common data manipulation tasks and master common data manipulation tasks using this database system.
Software Development
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Natural Language Processing (NLP) in Python
- IntermediateSkill Level
- 4.7+
- 2.2K
Master text analysis with essential NLP techniques from preprocessing to advanced transformer models.
Artificial Intelligence
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Financial Modeling in Excel
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- 4.7+
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Learn about Excel financial modeling, including cash flow, scenario analysis, time value, and capital budgeting.
Applied Finance
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Sampling in Python
- IntermediateSkill Level
- 4.6+
- 2.1K
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Probability & Statistics
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AI for Finance
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Apply AI in finance to analyze data, prompt effectively, and automate workflows for better decisions.
Artificial Intelligence
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Experimental Design in Python
- IntermediateSkill Level
- 4.6+
- 1.2K
Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!
Probability & Statistics
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Introduction to Writing Functions in R
- BasicSkill Level
- 4.3+
- 1.1K
Take your R skills up a notch by learning to write efficient, reusable functions.
Software Development
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Sampling in R
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Master sampling to get more accurate statistics with less data.
Probability & Statistics
FAQs
What is data science?
Data science is an area of expertise focused on gaining information from data. Using programming skills, scientific methods, algorithms, and more, data scientists analyze data to form actionable insights.
How can I learn data science?
You’ll need to learn a programming language such as Python or R and master the principles of math and statistics. Knowledge of data analysis methods and data science tools is also essential. There are many ways to learn data science. As well as formal means of education, such as a degree or university study, there are plenty of other resources to help you learn at your own pace. As well as online courses and tutorials, there are books, videos, and more.
What skills are required for data science?
As well as knowledge of mathematics and statistics, data scientists need programming skills in languages such as Python, R, and SQL. Additionally, data science requires the ability to work with large data sets, knowledge of data visualization, data wrangling, and database management. Skills in machine learning and deep learning can also be useful.
What can I use data science for?
In a professional capacity, almost every industry can use data science to some degree. Healthcare organizations use data science to detect and cure diseases, while finance companies use it to detect and prevent fraud. All kinds of industries use data science for marketing, such as building recommendation systems and analyzing customer churn.
Is data science a good career?
Yes, data science is among the fastest-growing sectors in the US and worldwide. It’s also one of the best-paid careers out there. According to data from Payscale, experience data scientists earn an average of $97,609 and have a satisfaction rating of four stars out of five in the US.
Is it difficult to become a data scientist?
There are a few things to consider here. First, data science degrees can be competitive to get onto, often requiring consistently high grades. Similarly, many of the skills required for data science require a lot of study and patience. It can take several months to master all of the necessary basics, as well as a lot of practical experience to secure an entry-level position.
Does data science require coding?
Yes, you’ll need some coding experience in languages such as Python, R, SQL, Java, and C/C++. However, due to its relatively simple syntax, Python programming language is often the preferred choice among newcomers.
How long does it take to become a data scientist?
For a person with no prior coding experience and/or mathematical background, it can typically take 7 to 12 months of intensive studies to be at the level of an entry-level data scientist. However, it is important to remember that learning only the theoretical basis of data science may not make you a real data scientist.
What topics can I study within data science?
Once you’ve mastered the foundations of data science, you can then specialize in a variety of areas, including machine learning, artificial intelligence, big data analysis, business analytics and intelligence, data mining, and more.