Art of Exploring and Analysing the Data
Explore Data Like a Pro with Python
Master the essential skills to explore, understand, and analyse data with Python. This course will guide you through the process of data exploration, where you’ll learn how to summarize and visualize data to uncover hidden patterns and trends. Using popular Python libraries, you’ll be able to clean, process, and visualize datasets, turning raw data into meaningful insights that can inform decisions.
Basic Python
Pandas and NumPy Basics
Basic Statistics
Basic Visualization
This ensures you’re ready to explore data effectively without getting lost in the fundamentals.
Art of Exploring and Analysing The Data (Syllabus)
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Introduction to Pandas
- Pandas Basics
Series and DataFrame basics
Reading CSV and Excel files
Indexing and Slicing in Pandas
- Data Exploration
head(), tail(), info(), shape
unique(), value_counts()
dtypes, describe() - Indexing & Slicing
using loc and iloc and slicing - Statistical Functions
mean, median, correlation
Summarization & Plotting using Pandas
- Summarisation & Reporting
apply-lambda function
group-by, cross tab and pivot
Plotting using DataFrames (Histogram, Bar plot, Pie Plot, Box plot, Scatter plot)
Pandas Case study
- Case Study – Univariate Analysis
Importing and exploration
Statistical functions (Mean, Median, Mode, Standard deviation)
Visualization (Histogram, bar plot, Pie plot, box plot)
Generating Insights
Pandas Case study
- Case Study – Bi-Variate Analysis
Correlation
Summarisation & Aggregation (groupby)
Visualization (Heatmap, Scatter plot, Box plot)
Generating Insights
Key Takeaways
Data Cleaning and Preparation: Handle missing data, standardize formats, and make datasets analysis-ready.
Visual Analysis: Create visualizations using Matplotlib and Seaborn to interpret data patterns and trends.
Statistical Summaries: Calculate basic statistics to quickly understand your data.
Insights and Hypotheses: Turn data into actionable insights for business decisions and predictive modeling.
