Course
Case Study: Competitor Sales Analysis in Power BI
- IntermediateSkill Level
- 4.7+
- 173 reviews
This Power BI case study follows a real-world business use case where you will apply the concepts of ETL and visualization.
Data Visualization
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
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Course
This Power BI case study follows a real-world business use case where you will apply the concepts of ETL and visualization.
Data Visualization
Course
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Machine Learning
Course
Learn the core techniques necessary to extract meaningful insights from time series data.
Probability & Statistics
Course
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Data Visualization
Course
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Machine Learning
Course
Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.
Data Manipulation
Course
Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.
Applied Finance
Course
In this course youll learn about basic experimental design, a crucial part of any data analysis.
Probability & Statistics
Course
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
Data Visualization
Course
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Probability & Statistics
Course
Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.
Reporting
Course
Learn to design and run your own Monte Carlo simulations using Python!
Probability & Statistics
Course
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Machine Learning
Course
This course is for R users who want to get up to speed with Python!
Software Development
Course
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Probability & Statistics
Course
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Probability & Statistics
Course
Develop the skills you need to clean raw data and transform it into accurate insights.
Data Preparation
Course
Learn to choose, build with, and secure AWS data stores including DynamoDB and S3 through hands-on console exercises and real-world scenarios.
Cloud
Course
In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Data Manipulation
Course
In this course, you’ll explore the essentials of cybersecurity, including the security lifecycle, digital transformation, and key cloud computing concepts.
Cloud
Course
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Probability & Statistics
Course
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Applied Finance
Course
This Power BI case study follows a real-world business use case on tackling inventory analysis using DAX and visualizations.
Data Visualization
Course
Learn how to load, transform, and transcribe speech from raw audio files in Python.
Data Manipulation
Course
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
Probability & Statistics
Course
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Probability & Statistics
Course
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Machine Learning
Course
Build, configure, and run your first AI agent using Googles Agent Development Kit (ADK). Set up environments, create agents in Python and YAML.
Cloud
Course
Visualize seasonality, trends and other patterns in your time series data.
Data Visualization
Course
Learn to use Amazon Bedrock to access foundation AI models and build with AI - without managing complex infrastructure.
Artificial Intelligence
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.
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.
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.
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.
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.
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.
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.
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.
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.
Make progress on the go with our mobile courses and daily 5-minute coding challenges.