The School Of

Data Science

Build expertise in data streaming, business analytics, data visualization, data analysis, data science programming, data engineering, data architecture, predictive analytics, and more. With the skills you learn in our data science courses, you can launch or advance a successful career in the field.

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Learning Paths by Job Title

Successful data analysts have a unique set of skills and represent important value to organizations eager to make data-powered business decisions. Combine skills in programming and statistical analysis to uncover insights, communicate critical findings, and create data-driven solutions.

Steps To Become A Data Analyst

Programming for Data Science with Python
(808)
2 months
, Beginner
Step 1
Programming for Data Science with Python
(808)
2 months
, Beginner
Skills Covered
Git, NumPy, Pandas, Control flow, Python scripting, Built-in Python functions, Python methods, Python function definition, Unix shell, Python package management, Python data types, Iterators, Python exception handling, Python best practices, Python operators, Python data structures, Docstrings, Anaconda, Python ides, Variable scope, List comprehension, Python syntax, User input handling, Data storytelling, Tableau data pane, Tableau map-based visualizations, Tableau interactive dashboards, Tableau field organization and customization, Data visualization design, Tableau visualizations, Chart selection, Tableau storypoint, Tableau calculated fields
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Data Analyst
(1185)
2 months
, Intermediate
Step 2
Data Analyst
(1185)
2 months
, Intermediate
Skills Covered
Latent variables, Data visualization design, Data fluency, Exploratory data analysis, Professional presentations, Data limitations and biases, Data storytelling, Jupyter notebooks, Quantitative data visualization, Data cleaning, Data storage, Data Tidiness Assessment, Data gathering, Pandas, Data quality assessment, Data wrangling, File i/o, Basic data visualizations, Data analysis process, NumPy, Data manipulation
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Popular Data Science Programs

Data Engineering with AWS
(1133)
2 months
, Intermediate
Data Analyst
(1185)
2 months
, Intermediate
Data Scientist
(782)
3 months
, Advanced
Programming for Data Science with Python
(808)
2 months
, Beginner
SQL
(270)
2 months
, Beginner

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Data Science Across Industries

Technology
- Technology companies use data science to analyze user behavior, preferences, and feedback to inform product development, personalize user experiences, and optimize system performance.
- They do this while leveraging predictive analytics, fraud detection, and cybersecurity measures to enhance overall operations and customer satisfaction.
Marketing
- Data science is crucial for marketing analytics, customer segmentation, campaign optimization, and measuring the effectiveness of advertising strategies.
- It enables organizations to analyze consumer behavior, target specific audiences, and optimize marketing campaigns for better outcomes.
Insurance
- Data science is used in risk assessment, underwriting, claims processing, fraud detection, and customer segmentation in the insurance industry.
- It enables insurers to leverage data to make accurate risk assessments, streamline processes, and provide personalized offerings.
Environmental Science
- Data science is applied in environmental science for climate modeling, weather forecasting, ecological analysis, and natural disaster prediction.
- It helps scientists analyze large datasets, identify patterns, and make accurate predictions to better understand and mitigate environmental challenges.