Course
Data Preparation in Excel
- BasicSkill Level
- 4.8+
- 7.7K
Understand how to prepare Excel data through logical functions, nested formulas, lookup functions, and PivotTables.
Data Preparation
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Course
Data Preparation in Power BI
- BasicSkill Level
- 4.9+
- 7.1K
In this interactive Power BI course, you’ll learn how to use Power Query Editor to transform and shape your data to be ready for analysis.
Data Preparation
Course
Introduction to Importing Data in Python
- BasicSkill Level
- 4.8+
- 5.1K
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Data Preparation
Course
Introduction to Power Query in Excel
- BasicSkill Level
- 4.8+
- 4.6K
Explore Excel Power Query for advanced data transformation and cleansing to boost your decision-making and analysis.
Data Preparation
Course
Cleaning Data in Python
- IntermediateSkill Level
- 4.8+
- 4.5K
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Data Preparation
Course
Introduction to Alteryx
- BasicSkill Level
- 4.9+
- 4.3K
Enter the world of Alteryx Designer and learn how to navigate the tool to load, prepare, and aggregate data.
Data Preparation
Course
Intermediate Importing Data in Python
- BasicSkill Level
- 4.8+
- 4.2K
Improve your Python data importing skills and learn to work with web and API data.
Data Preparation
Course
Introduction to Google Sheets
- BasicSkill Level
- 4.8+
- 3.1K
Bring your Google Sheets to life by mastering fundamental skills such as formulas, operations, and cell references.
Data Preparation
Course
Introduction to Importing Data in R
- BasicSkill Level
- 4.8+
- 2.5K
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Data Preparation
Course
Web Scraping in Python
- IntermediateSkill Level
- 4.8+
- 1.8K
Learn to retrieve and parse information from the internet using the Python library scrapy.
Data Preparation
Course
Data Preparation in Alteryx
- BasicSkill Level
- 4.9+
- 1.6K
Master data preparation, cleaning, and analysis in Alteryx Designer, whether you are a new or seasoned analyst.
Data Preparation
Course
Intermediate Power Query in Excel
- IntermediateSkill Level
- 4.8+
- 1.5K
Building on your foundational Power Query in Excel knowledge, this intermediate course takes you to the next level of data transformation mastery
Data Preparation
Course
Streamlined Data Ingestion with pandas
- IntermediateSkill Level
- 4.8+
- 1.4K
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
Data Preparation
Course
Cleaning Data in R
- IntermediateSkill Level
- 4.8+
- 1.2K
Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.
Data Preparation
Course
Creating PostgreSQL Databases
- BasicSkill Level
- 4.8+
- 1.1K
Learn how to create a PostgreSQL database and explore the structure, data types, and how to normalize databases.
Data Preparation
Course
Introduction to KNIME
- BasicSkill Level
- 4.8+
- 1K
Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.
Data Preparation
Course
Cleaning Data with PySpark
- AdvancedSkill Level
- 4.8+
- 873
Learn how to clean data with Apache Spark in Python.
Data Preparation
Course
Connecting Data in Tableau
- BasicSkill Level
- 4.9+
- 864
Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.
Data Preparation
Course
Intermediate Google Sheets
- BasicSkill Level
- 4.8+
- 673
Expand your Google Sheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
Data Preparation
Course
Cleaning Data in PostgreSQL Databases
- IntermediateSkill Level
- 4.8+
- 531
Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.
Data Preparation
Course
Dealing With Missing Data in R
- BasicSkill Level
- 4.8+
- 478
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Data Preparation
Course
Web Scraping in R
- IntermediateSkill Level
- 4.7+
- 477
Learn how to efficiently collect and download data from any website using R.
Data Preparation
Course
Intermediate Importing Data in R
- IntermediateSkill Level
- 4.8+
- 439
Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
Data Preparation
Course
Case Study: Analyzing Sales Data in Alteryx
- BasicSkill Level
- 4.8+
- 374
Explore Alteryx Designer in a retail data case study to boost sales analysis and strategic decision-making.
Data Preparation
Course
Cleaning Data in SQL Server Databases
- IntermediateSkill Level
- 4.8+
- 287
Develop the skills you need to clean raw data and transform it into accurate insights.
Data Preparation
Course
Data Transformation in KNIME
- BasicSkill Level
- 4.8+
- 257
Enhance your KNIME skills with our course on data transformation, column operations, and workflow optimization.
Data Preparation
Course
Marketing Analytics in Tableau
- IntermediateSkill Level
- 4.8+
- 247
Master marketing analytics using Tableau. Analyze performance, benchmark metrics, and optimize strategies across channels.
Data Preparation
Course
Case Study: Analyzing Fitness Data in Alteryx
- IntermediateSkill Level
- 4.9+
- 76
Advance your Alteryx skills with real fitness data to develop targeted marketing strategies and innovative products!
Data Preparation
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.
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