Course Details

In this Python for Data Analytics course, you will learn how to harness the power of Python to analyze, visualize, and derive meaningful insights from data. From data manipulation to creating impactful visualizations, you will gain hands-on experience with essential libraries like Pandas, NumPy, Matplotlib, and Seaborn.

PYTHON FOR DATA ANALYTICS

In this Python for Data Analytics course, you will learn how to harness the power of Python to analyze, visualize, and derive meaningful insights from data. From data manipulation to creating impactful visualizations, you will gain hands-on experience with essential libraries like Pandas, NumPy, Matplotlib, and Seaborn.

Course Fee

₦ 30,000

Duration

4weeks

APPLY
  • Overview of Python’s role in data analytics.
  • Installing and setting up Python environments (Jupyter, Anaconda).
  • Basic Python syntax and operations
  • Working with lists, dictionaries, and tuples.
  • Introduction to NumPy arrays for numerical data.
  • Understanding and using Pandas DataFrames.
  • Importing data from CSV, Excel, and databases.
  • Cleaning data (handling missing values, duplicates).
  • Filtering, sorting, and grouping data.
  • Merging and joining DataFrames.
  • Descriptive statistics (mean, median, mode, standard deviation).
  • Visualizing data distributions with Pandas.
  • Detecting outliers and anomalies in data.
  • Uncovering patterns using correlation analysis.
  • Creating basic plots (line, bar, scatter, histograms).
  • Customizing charts (titles, labels, legends).
  • Advanced visualizations (heatmaps, pairplots).
  • Best practices for visual storytelling.
  • Handling date and time data in Python.
  • Resampling and rolling window calculations.
  • Plotting time series data for trend analysis.
  • Performing time series forecasting with Python.
  • Using groupby to summarize data.
  • Aggregating data for deeper insights.
  • Pivot tables in Pandas for multi-dimensional analysis.
  • Practical use cases of data aggregation.
  • Overview of machine learning in data analytics.
  • Implementing simple linear regression models.
  • Train-test split and model evaluation.
  • Using Scikit-learn for predictive analytics.
  • Writing functions to automate repetitive tasks.
  • Looping through datasets and applying transformations.
  • Working with Python scripts to process data automatically.
  • Analyzing customer churn with Python.
  • Building sales performance dashboards.
  • Conducting market basket analysis for retail data.
  • Applying Python to financial data analysis.