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Where can you start learning Data Science?
- How to think critically about data and draw robust conclusions based on incomplete information.
- Computational thinking and skills, including the Python 3 programming language for visualizing and analyzing data.
- How to make predictions based on machine learning.
- How to interpret and communicate data and results using a vast array of real-world examples.
- How to load and clean real-world data
- How to make reliable statistical inferences from noisy data
- How to use machine learning to learn models for data
- How to visualize complex data
- How to use Apache Spark to analyze data that does not fit within the memory of a single computer
- Fundamental R programming skills
- Statistical concepts such as probability, inference, and modeling and how to apply them in practice
- Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
- Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
- Implement machine learning algorithms
- In-depth knowledge of fundamental data science concepts through motivating real-world case studies
- Use Microsoft Excel to explore data
- Use Transact-SQL to query a relational database
- Create data models and visualize data using Excel or Power BI
- Apply statistical methods to data
- Use R or Python to explore and transform data
- Follow a data science methodology
- Create and validate machine learning models with Azure Machine Learning
- Write R or Python code to build machine learning models
- Apply data science techniques to common scenarios
- Implement a machine learning solution for a given data problem
- Understand Python language basics and apply to data science
- Practice iterative data science using Jupyter notebooks on IBM Cloud
- Analyze data using Python libraries like pandas and Numpy
- Create stunning data visualizations with Matplotlib, folium and seaborn
- Build machine learning models using scipy and sci-kit learn
- Demonstrate proficiency in solving real-life data science problems