Programming for Data Science with Python
(837)
53 hours
Beginner
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.
Your Path to Career Success
Chart your path to a $200k+ career in tech
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
Step 1
Programming for Data Science with Python(837)
53 hours
Beginner
Skills Covered
NumPy, Pandas, SQL aggregations, SQL joins, SQL queries, Control flow, Built-in Python functions, Python methods, Python function definition, SQL subqueries, SQL window functions, 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, Git, Data cleaning, SQL query performance tuning, Python scripting
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Data Analyst(1277)
43 hours
Intermediate
Step 2
Data Analyst(1277)
43 hours
Intermediate
Skills Covered
Latent variables, Data fluency, Exploratory data analysis, Professional presentations, Data limitations and biases, Data storytelling, Jupyter notebooks, Quantitative data visualization, Data visualization design, Data cleaning, Data storage, Data Tidiness Assessment, Data gathering, Data wrangling, Pandas, Data quality assessment, File i/o, Basic data visualizations, Data analysis process, NumPy, Data manipulation
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There is a shortage of qualified data scientists in the workforce, and individuals with these skills are in high demand. Learn data science to build expertise in programming, data wrangling, machine learning, experiment design, and data visualization—then launch your career in data science.
Steps To Become A Data Scientist
- Programming for Data Science with Python
(837)
53 hours
Beginner
Step 1
Programming for Data Science with Python(837)
53 hours
Beginner
Skills Covered
NumPy, Pandas, SQL aggregations, SQL joins, SQL queries, Control flow, Built-in Python functions, Python methods, Python function definition, SQL subqueries, SQL window functions, 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, Git, Data cleaning, SQL query performance tuning, Python scripting
Learn More - Data Analyst
(1277)
43 hours
Intermediate
Step 2
Data Analyst(1277)
43 hours
Intermediate
Skills Covered
Latent variables, Data fluency, Exploratory data analysis, Professional presentations, Data limitations and biases, Data storytelling, Jupyter notebooks, Quantitative data visualization, Data visualization design, Data cleaning, Data storage, Data Tidiness Assessment, Data gathering, Data wrangling, Pandas, Data quality assessment, File i/o, Basic data visualizations, Data analysis process, NumPy, Data manipulation
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Data Scientist(812)
61 hours
Advanced
Step 3
Data Scientist(812)
61 hours
Advanced
Skills Covered
Blog posts, Magic methods, Unit testing, Data storytelling, CRISP-DM, AI algorithms in Python, Github portfolios, Similarity Score, Principal component analysis, Dimensionality reduction, Types of recommenders, Cluster validation, K-means clustering, Recommender system limitations, Basic unsupervised learning, Feature visualization, Recommendation engines, Matrix factorization, Hierarchical clustering models, Latent variables, Actionable insight generation, Evaluating unsupervised learning models, Business metrics, PyTorch, Tokenization, Convolutional neural networks, NLP pipelines, Stemming, Machine learning pipeline creation, NLP models, Part of speech tagging, Model cross-validation, Support vector machines, Data pre-processing for ML, Lemmatization, Feature extraction, Image pre-processing, Opencv, Computer vision fluency, Hyperparameter tuning, Python virtual environments, Model deployment, Inheritance, Github actions, Object-oriented design patterns, Python package management, Web forms, Object-oriented Python, Python packaging, Python decorator functions, Linting, Python classes, Feature importance calculation, Model bias analysis, Gradient descent, Random forest models, Tree-based models, scikit-learn, Model evaluation, Neural network basics, Basic supervised machine learning
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Data engineering is the foundation for the new world of Big Data. A well-trained data engineer is able to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets.
Steps To Become A Data Engineer
- Programming for Data Science with Python
(837)
53 hours
Beginner
Step 1
Programming for Data Science with Python(837)
53 hours
Beginner
Skills Covered
NumPy, Pandas, SQL aggregations, SQL joins, SQL queries, Control flow, Built-in Python functions, Python methods, Python function definition, SQL subqueries, SQL window functions, 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, Git, Data cleaning, SQL query performance tuning, Python scripting
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Data Engineering with AWS(1267)
39 hours
Intermediate
Step 2
Data Engineering with AWS(1267)
39 hours
Intermediate
Skills Covered
Cassandradb, PostgreSQL, Database normalization, Denormalized data schemas, Data modeling basics, Data pipeline dags, Data pipeline partitioning, Amazon s3, Data pipeline maintenance, Redshift, Data pipeline creation, Apache Airflow, Data lineage, AWS data lakes, ELT, Big data fluency, Data wrangling, Amazon Athena, Data Lakehouse Architecture, Data transformation, Apache Spark, Data format fundamentals, AWS glue, Data lakes, Data extraction, Infrastructure as code, Olap cubes, Table design, Cloud computing fluency, ETL, Database fundamentals, Data warehouse architecture, AWS storage services, Table partitioning, Online transaction processing, AWS data warehouse, Star schemas
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Data Streaming(148)
53 hours
Advanced
Step 3
Data Streaming(148)
53 hours
Advanced
Skills Covered
Faust, Confluent Kafka Python client, Kafka rest proxy, KSQL, Kafka connect, Streaming data stores, Kafka basics, Apache Avro, SQL window functions, SQL aggregations, Data streaming schemas, Stream processing use cases, SQL queries, JSON, Kafka performance monitoring, Data serialization, Data deserialization, Data privacy principles, Base64, Batch processing, Kafka schema registry, Timeframing in stream processing, Command line interface basics, Windowing in stream processing, Apache Spark, Stream processing
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Gain analytics skills without learning to program. Learn business online and build skills in Excel and SQL, analyze real datasets, and visualize your findings to address business intelligence needs in many industries.
Steps To Become A Business Analyst
Business Analytics with Tableau(1252)
40 hours
Beginner
Step 1
Business Analytics with Tableau(1252)
40 hours
Beginner
Skills Covered
Univariate and Bivariate Chart Selection, Tableau Aggregations, Tableau visualizations, Tableau proficiency, Tableau Marks and Filters, Data Visualization Design Integrity, Interactive and Responsive Tableau Dashboard Design, Tableau hierarchies, Tableau Dashboard Creation, Connecting to Data and Setting Up Tableau, Tableau interactive dashboards, Tableau Chart Types, Tableau storypoint, Color and Visual Encodings, Tableau groups, Building and Saving Worksheets in Tableau, Tableau calculated fields, SQL joins, SQL Clauses, SQL subqueries, SQL Operators, SQL Data Cleaning, SQL aggregations, Basic SQL, Scatter Plot Awareness, Growth metrics, Spreadsheet Navigation, Data visualization in spreadsheets, KPI Awareness, Lookups and Pivoting, Finance metrics, Line and Area Charts in Spreadsheets, Basic spreadsheet use, Tabular Data Awareness, Spreadsheet Operations, Efficiency Metrics, Data Relationships, Pie Charts in Spreadsheets, Spreadsheet Cell Referencing, Scatter Plots in Spreadsheets, Pie Chart Awareness, Business metrics, Customer Satisfaction Metrics, Bar and Column Charts in Spreadsheets, Function and Formula Syntax, Histograms in Spreadsheets, Bar and Column Chart Awareness, Spreadsheet functions, Data types, Histogram Awareness, Profitability Metrics, Date and Data Cleanup, Basic descriptive statistics, Line and Area Chart Awareness, Chart types, Chart Selection and Formatting in Spreadsheets, Innovation Metrics, Pivot tables, Data Summarization, Logic and Aggregation Formulas
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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.
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