Python for Data Analysis

50 Hours

Intermediate

8 Modules

LE 6,499.00
LE 9,000.00
LE 6,499.00
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Preprocess and clean data: Handling missing values, removing duplicates, and formatting data to make it analysis-ready. Visualize data trends: Creating graphs and plots to understand underlying patterns in data. Model data: Using machine learning and statistical analysis to make predictions and derive insights. This skill set is highly applicable in industries like finance, healthcare, marketing, and more, making Python a valuable asset in a data professional's toolkit.
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Vendor: Cloud Learning Academy
Categories: Python,
Product Type: Python Courses
    • Course Outline
    • What you will learn
    • Audience profile
    Overview of Python for data tasks Setting up Python for data analysis (Anaconda, Jupyter Notebooks) Key libraries for data analysis: NumPy, Pandas, Matplotlib, Seaborn
    Understanding data types and structures Data frames and series in Pandas Loading and saving data (CSV, Excel, databases) Data filtering, sorting, and aggregation
    Handling missing values Data transformation techniques (normalization, scaling) Working with datetime data String manipulation and regex for data cleaning
    Descriptive statistics and summary analysis Data visualization techniques with Matplotlib and Seaborn Identifying trends and patterns Outlier detection and analysis
    Applying groupby and pivot tables Merging, joining, and concatenating data sets Cross-tabulations and frequency tables Calculating rolling statistics and time series analysis basics
    Advanced visualization with Matplotlib and Seaborn Customizing charts (labels, legends, colors) Creating histograms, scatter plots, box plots, and heatmaps Storytelling with data visualization
    Basics of machine learning with Python Supervised vs. unsupervised learning overview Building simple predictive models Model evaluation techniques (train-test split, cross-validation)
    Applying learned skills to a real-world data analysis problem Collecting, cleaning, analyzing, and visualizing a dataset Presenting insights and recommendations
    You'll learn foundational skills in Python programming specifically for data analysis. This includes understanding data types, manipulating data with Pandas, visualizing data with Matplotlib, and applying techniques like data cleaning, data transformation, and exploratory data analysis (EDA).
    You'll learn to efficiently handle, clean, and prepare large datasets, using libraries like Pandas and Numpy to perform filtering, sorting, aggregation, and transformation. These skills are crucial for real-world data management.
    The course covers using Matplotlib and Seaborn for creating informative charts, such as histograms, scatter plots, box plots, and heatmaps, to help you interpret and present data effectively.
    Yes, there is an introductory section on data modeling and basic machine learning concepts to give you an overview of building and evaluating simple predictive models.
    Absolutely! Each module includes practical exercises, and you’ll complete a capstone project at the end, applying your skills to analyze a real-world dataset.
    Yes, while prior programming knowledge is helpful, the course is structured to guide beginners through the essentials of Python for data analysis.
    Individuals looking to start a career in data analytics or data science and need foundational programming skills in Python.
    Professionals who want to automate data processing, enhance data insights, and perform in-depth analysis using Python's data libraries.
    Researchers who work with large datasets and wish to leverage Python for data cleaning, transformation, and visualization.

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