Difficulty Level: INTERMEDIATE
Age group: 15-39
From Deployment to Feedback - Python Libraries for Data Science - Series Data Structure - DataFrame Data Structure - Working with Pandas Data Frame - Plotting with PyPlot - Importing/Exporting Data between CSV Files - Feature Selection Python Libraries for Data Science - Import Library into Python Program - Using Panda - Pandas Data Structures - Series - DataFrame Series Data Structure - Creating Series Objects - Series Object Attributes - Accessing a Series Object and its Elements - Extracting Slices from Series Object - Operations on Series Object - Modifying Elements of Series Object - Renaming Indexes - The Head() and tail() functions - Vector Operations on Series Objects - Arithmetic on Series Object - Sorting Series Values - Series Objects vs. Lists - Series Objects vs. Dictionaries - Difference between Numpy Arrays and Series Objects DataFrame Data Structure - Creating and Displaying a Data Frame - Data Frame Attributes - DataFrame vs.Series and 2D Numpy Array - Selecting/Accessing Multiple Columns - Selecting Rows and Columns from a DataFrame - Select/Accessing Individual Value - Selecting DataFrame Rows/Columns based on Boolean Conditions - Adding/Modifying a Row/Column Working with Pandas Data Frame - Iterating Over a Data Frame - Binary Operations in a Data Frame - Descriptive Statistics with Pandas - Essential Functions and Functionality - Advanced Operations on Data Frame - Handling Missing Data - Combining Data Frame - Function group by() Function group by() - Applying Various settings in plot() Function Plotting with PyPlot - What is Data Visualization? - Using Pyplot of Matplot Library - Working with Pyplot Methods - Creating Line Charts and Scatter Charts - Specifying Plot Size and Grid - Creating Bar Charts and Pie Charts - Customizing the Plot - Creating Histograms with Pyplot - Creating Frequency Polygons - Creating Box Plots - Plotting Data from a Data Frame Importing/Exporting Data between CSV Files - Transferring Data between .csv Files and Data Frames - Storing DataFrame's Data to CSV File - Loading Data From CSV to Data Frames Feature Selection - Feature Selection - Regression - Factor Analysis - Factor Analysis Process - Principal Component Analysis (PCA) - First Principal Component - Eigenvalues and PCA - Feature Reduction - Linear Discriminant Analysis - Maximum Separable Line - Find Maximum Separable Line
Knowledge of Programming Language
Enthuziasm to learn
Data Workflow Automation: Learn to automate data workflows and perform repetitive tasks using Python scripts and Jupyter Notebooks, enhancing productivity and reproducibility.
Machine Learning: Gain proficiency in building, evaluating, and tuning machine learning models using Scikit-learn for predictive analytics.
Statistical Analysis: Apply intermediate statistical methods and probability theories to analyze data distributions and test hypotheses.
Data Visualization: Develop skills in creating complex visualizations using Matplotlib and Seaborn to effectively communicate data insights.
Data Manipulation & Cleaning: Master intermediate techniques for data manipulation and cleaning using libraries like Pandas and NumPy, ensuring data integrity for analysis.
Understanding of Python data structure libraries
Knowledge of series and pandas data structure functions
Skill in plotting with PyPlot
Ability to use CSV files
Mastery in series, Pandas, and Pyplot libraries
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