Python Data Analysis Training
D ata analysis course teaches some of the most important techniques and tools necessary to manipulate and analyze large datasets and summarize conclusions. This includes:
- exploratory and predictive statistics
- more advanced computer program design
- an introduction to algorithms
- python for statistical analysis
- data visualization best practices
- Understanding Data Analysis
- How python is used for Data Analysis
- IPython Basics
- IPython HTML Notebook
- Advanced IPython Features
- The NumPy ndarray
- Data types for ndarrays
- Arrays and Scalars
- Indexing and Slicing
- Fast element wise array functions
- Data Processing
- Linear Algebra
- Random number generation
Working with pandas
- Pandas data structures
- DataFrame and Index Objects
- Sorting and Ranking
- Handling missing data
- Hierarchical Indexing
Data Loading, Storage, File Formats
- Reading and Writing data in text format
- Binary Data Format
- Interacting with the databases
- Combining and merging data sets.
- Transforming the data
- String manipulation
Plotting and Visualization
- Plotting functions in pandas
- Plotting maps
Data Aggregation and Group Operations
- Data Aggregation
- Group wise operations
- Pivot Tables
- Date and Time types
- Time basics
- Date basics
- Period Arithmetic
- Time Series Plotting
Financial and Economic Data Applications
- Data Munging
- Group Transforms and Analysis
- Rolling Correlation and Linear Regression
- Advanced Array Manipulation
- Structured and Record Arrays
- NumPy matrix class
- This course is intended for people with little to no background in data analysis and computer programming.
- An introductory statistics class and an introductory programming class will both come in handy, but are not necessary.
- A basic familiarity with calculus and general computer competency is assumed.
- To develop relevant programming abilities.
- To demonstrate proficiency with statistical analysis of data.
- To develop the ability to build and assess data-based models.
- To execute statistical analyses with professional statistical software.
- To demonstrate skill in data management.
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