Python for Data Analytics Practice Exam

Python for Data Analytics Practice Exam

About the Python for Data Analytics Exam

Python for Data Analytics refers to using the Python programming language to collect, manipulate, analyze, and visualize data. It leverages powerful libraries like Pandas, NumPy, and Matplotlib to clean, process, and explore datasets, making it an essential tool for data scientists and analysts. Python's versatility, ease of use, and extensive community support make it ideal for performing complex data analysis tasks, such as statistical analysis, data visualization, machine learning, and automation, across various industries.

Skills Required

• Basic knowledge of Python programming, including variables, data types, and control structures.
• Understanding of basic mathematics and statistics, such as mean, median, standard deviation, and probability.
• Familiarity with data types like lists, tuples, and dictionaries in Python.
• Basic understanding of data structures and algorithms.
• Knowledge of using Python libraries (optional, but helpful) like NumPy and Matplotlib.
• Basic experience with handling and working with data files (e.g., CSV, Excel).
• Familiarity with using IDEs like Jupyter Notebook or any Python environment.

Knowledge Gained 

• Data manipulation: Using Pandas for cleaning, transforming, and analyzing data in various formats (e.g., CSV, Excel).
• Data analysis: Applying statistical techniques to analyze and interpret datasets effectively.
• Data visualization: Creating visualizations using libraries like Matplotlib and Seaborn to communicate insights.
• Advanced data handling: Working with large datasets, time series data, and handling missing or inconsistent data.
• Numerical computation: Using NumPy for efficient numerical operations and matrix manipulations.
• Data-driven decision making: Applying Python for real-world tasks such as forecasting, trend analysis, and making data-driven decisions.
• Introduction to machine learning: Understanding the basics of machine learning algorithms and how to apply them to datasets using libraries like Scikit-learn.
• Automation: Writing Python scripts to automate data processing and reporting tasks.

Who should take the Exam?

• Individuals aspiring to start a career in data science or data analytics.
• Professionals working in fields like business analysis, finance, marketing, or healthcare who want to enhance their data analysis skills using Python.
• Developers or programmers looking to expand their expertise into data analytics and machine learning.
• Students pursuing degrees in computer science, engineering, data science, or related fields who want to validate their Python and data analytics knowledge.
• Analysts and researchers looking to improve their ability to work with large datasets and perform in-depth data analysis.
• Anyone interested in gaining a strong foundation in Python for handling, analyzing, and visualizing data.

Course Outline

Matplotlib and Seaborn – Libraries and Techniques

• Introduction
• What You Will Learn
• Visualization Concepts
• Introduction to Matplotlib
• Creating Simple Plots Using Matplotlib
• Creating Scatter Plots
• Creating Axis Limits
• Parameterizing Plots
• Creating Error Bars
• Plotting Histograms and Box Plots
• Plotting 2D Histograms
• Marginal Histograms and Marginal Boxplots
• Working with Subplots
• Stock Trend / Time Series Plot and Annotations
• Plotting Images and Clustering
• Creating 2D Contour plots for 3D Data
• Creating 3D Plots Including 3D Contours
• Stylesheets, rcParam, and Custom Stylesheets

Advanced Visualizations Using Business Applications

• Single and Multiple Bar Charts
• Area and Stacked-Area Charts
• Drawing Pie Charts
• Bubble Charts with Vectorization of Properties
• Plotting Regression Lines with OLS (ML)
• Categorical Variables and Histograms (with EDA)
• Seaborn Boxplot, Violin plot, Categorical Scatterplot
• Seaborn Slopeplots for Comparing Distributions
• Dumbbell Plot for Category-Wise Value Movement
• Creating Heatmaps
• Working with Pairplots
• Seasonal Trendcharts
• Yearplot and Calendarplot for Color-Scaled Trends
• Radarplot to Compare Scores of Multiple Parameters

Working with the Beautiful and Powerful Bokeh Library

• Introduction to Bokeh
• Creating Simple and Multiple Line Plots
• Customizing Your Plots
• Creating Bubble Plots – Vectorizing Your Plot
• Working with Layouts – Row / Column / Grid
• Using the ColumnDataSource Object
• Applying Filters – IndexFilter, BooleanFilter, GroupFilter
• Widgets – Dynamic Plot Controls
• Plotting on a Google Map Using Google Map API
• Closing Notes