1. Introduction and Setting Up Your Integrated Analysis Environment
2. Using Python to Control and Document Your Data Science Processes
• Python Essentials
• Data types and objects
• Loading packages, namespaces
• Reading and writing data
• Simple plotting
• Control flow
• Debugging
• Code profiling
3. Accessing and Preparing Data
• Acquiring Data with Python
• Loading from CSV files
• Accessing SQL databases
Cleansing Data with Python
• Stripping out extraneous information
• Normalizing data
• Formatting data
4. Numerical Analysis, Data Exploration, and Data Visualization with NumPy Arrays, Matplotlib, and Seaborn
· NumPy Essentials
· The NumPy array
· N-dimensional array operations and manipulations
· Memory mapped files
Data Visualization
· 2D plotting with Matplotlib
· Advanced data visualization with Seaborn
5. Exploring Data with Pandas
· Searching for Gold in a Pile of Pyrite
· Data manipulation with Pandas
· Statistical analysis with Pandas
· Time series analysis with Pandas
6. Machine Learning
· Predicting the Future Can Be Good for Business
· Input: 2D, samples, and features
· Estimator, predictor, transformer interfaces
· Pre-processing data
· Regression
· Classification
· Model selection
Course Code : SICT-MSCIT
Course Code : S-SICT-TALLYXPERT
Course Code : S-SICT-ADVEXCEL
Course Code : S-SICT-GRAPHIC
Course Code : S-SICT-CORE PYTHON
Course Code : S-SICT-ADVPYTHON
Course Code : S-SICT-DATASCIPYTHON
Course Code : S-SICT-PYTHON AI / ML
Course Code : S-SICT-DATA ANALYST
Course Code : S-SICT-COREJAVA
Course Code : S-SICT-ADVJAVA
Course Code : S-SICT-SPRO
Course Code : S-SICT-SPRO6
Course Code : S-SICT-AI ML
Course Code : S-SICT-H AND N
Course Code : S-SICT-CAD
Course Code : S-SICT-C++
Course Code : S-SICT-3DS
Course Code : S-SICT-WEB
Course Code : S-SICT-BI
Course Code : S-SICT-SCRATCH
